10 ways to build applications faster with Amazon CodeWhisperer

Amazon CodeWhisperer is a powerful generative AI tool that gives me coding superpowers. Ever since I have incorporated CodeWhisperer into my workflow, I have become faster, smarter, and even more delighted when building applications. However, learning to use any generative AI tool effectively requires a beginner’s mindset and a willingness to embrace new ways of working.

Best practices for tapping into CodeWhisperer’s power are still emerging. But, as an early explorer, I’ve discovered several techniques that have allowed me to get the most out of this amazing tool. In this article, I’m excited to share these techniques with you, using practical examples to illustrate just how CodeWhisperer can enhance your programming workflow. I’ll explore:

Typing less
Generating functions using code
Generating functions using comments
Generating classes
Implementing algorithms
Writing unit tests
Creating sample data
Simplifying regular expressions
Learning third-party code libraries faster
Documenting code

Before we begin

If you would like to try these techniques for yourself, you will need to use a code editor with the AWS Toolkit extension installed. VS Code, AWS Cloud9, and most editors from JetBrains will work. Refer to the CodeWhisperer “Getting Started” resources for setup instructions.

CodeWhisperer will present suggestions automatically as you type. If you aren’t presented with a suggestion, you can always manually trigger a suggestion using the Option + C (Mac) or Alt + C (Windows) shortcut. CodeWhisperer will also sometimes present you with multiple suggestions to choose from. You can press the → and ← keys to cycle through all available suggestions.

The suggestions CodeWhisperer offers are non-deterministic, which means you may receive slightly different suggestions than the ones shown in this article. If you receive a suggestion that doesn’t suit your needs, try cycling through all available suggestions using the arrow keys or try rephrasing any code comment prompts slightly. Even punctuation changes can produce fresh suggestions.

The examples in this article are written in JavaScript and TypeScript. However, the techniques I’ll demonstrate are equally suited to many of the programming languages supported by CodeWhisperer. Now, onto the examples!

Typing less

At the most basic level, CodeWhisperer can be used as a supercharged auto-complete, significantly reducing your typing when writing individual lines or blocks of code. To use CodeWhisperer in this way, just write code as you normally would. CodeWhisperer will make automatic suggestions as you type. Accept suggestions when they’re helpful, and ignore suggestions when they’re not. In my experience, using CodeWhisperer in this way alone reduces my typing by 50% or more.

Generating functions

Automatically generating function bodies from signatures

Skilled programmers use clear, descriptive names for their functions and parameters. If you follow this practice in your own programming, CodeWhisperer will have all the context it needs to implement many functions for you.

After I enter the function signature below, CodeWhisperer generates the function body for me.

User input (JavaScript):

function standardDeviation(numbers)


function standardDeviation(numbers) {
let mean = numbers.reduce((a, b) => a + b) / numbers.length;
let variance =
numbers.reduce((a, b) => a + Math.pow(b – mean, 2)) / numbers.length;
return Math.sqrt(variance);

Generating functions from comments

Sometimes a good function signature alone isn’t enough to convey the desired behavior of a function. In those cases, you can use a detailed code comment as a prompt to CodeWhisperer. I recommend using your language’s doc comment delimiter when using this technique.

User Input (JavaScript):

* Given a list that contains some numbers and strings, format them into a string
* in which the numbers are prepended with “#” and the strings are wrapped in
* double quotes.


* Given a list that contains some numbers and strings, format them into a string
* in which the numbers are prepended with “#” and the strings are wrapped in
* double quotes.
function formatList(list) {
return list
.map((item) => {
if (typeof item === “number”) {
return `#${item}`;
} else {
return `”${item}”`;
.join(“, “);

Generating classes

You can generate full or partial class implementations from a single code comment. The comment should describe the key properties and behaviors of the class. Simple descriptions are often all that is required.

User input (TypeScript):

* A folder has a name, children, and an optional parent. A child can’t be added
* twice.


After I write the above block comment, CodeWhisperer provides multiple suggestions, including the following implementation, complete with documentation comments for each method!

* A folder has a name, children, and an optional parent. A child can’t be added
* twice.
export class Folder {
name: string;
children: Folder[];
parent?: Folder;

constructor(name: string, parent?: Folder) {
this.name = name;
this.children = [];
this.parent = parent;

* Add a child to the folder.
* @param child The child to add.
* @returns True if the child was added, false if it was already present.
addChild(child: Folder): boolean {
if (this.children.includes(child)) {
return false;
child.parent = this;
return true;

* Remove a child from the folder.
* @param child The child to remove.
* @returns True if the child was removed, false if it wasn’t present.
removeChild(child: Folder): boolean {
const index = this.children.indexOf(child);
if (index === -1) {
return false;
this.children.splice(index, 1);
child.parent = undefined;
return true;

Implementing algorithms

CodeWhisperer enables you to implement well-known algorithms with ease. Simply tell CodeWhisperer which algorithm you want to apply to a particular coding problem, and it will oblige.

User input (JavaScript):

const actors = [ … ];

// Sort the actors by age using the merge sort algorithm.


const actors = [ … ];

// Sort the actors by age using the merge sort algorithm.
function mergeSort(arr) {
if (arr.length < 2) {
return arr;

const middle = Math.floor(arr.length / 2);
const left = arr.slice(0, middle);
const right = arr.slice(middle);

return merge(mergeSort(left), mergeSort(right));

function merge(left, right) {
const result = [];

while (left.length && right.length) {
if (left[0].age < right[0].age) {
} else {

while (left.length) {

while (right.length) {

return result;


Writing unit tests

CodeWhisperer accelerates the implementation of unit tests. Not only is it able to implement the logic for most of the explicit unit tests I ask for, but it often suggests unit tests for edge cases that hadn’t even occurred to me.

When I want CodeWhisperer to implement a unit test, I start with a declaration of the condition I’m testing and the expected result as in the following example.

User input (JavaScript):

describe(“Folder.addChild()”, () => {
it(“when adding an existing folder should not add duplicate”


describe(“Folder.addChild()”, () => {
it(“when adding an existing folder should not add duplicate”, () => {
const folder = new Folder(“root”);
const child = new Folder(“child”);

After you’ve written a test or two, CodeWhisperer is likely to suggest more tests, even without explicit prompting.

Tip: You’ll notice that my test description includes 1) the function I’m testing, 2) the condition I’m testing, and 3) what I expect the result to be. Following this three-part pattern is a great way to ensure CodeWhisperer has the context it needs to implement high quality test logic.

Creating sample data

One of the delightful discoveries I’ve made is that CodeWhisperer can generate dummy data—or really any code that has a repeating pattern. As an illustration, for the following list I entered only the first “user” object as an example, and CodeWhisperer helpfully continued the pattern.

User input (JavaScript):

const fakeUsers = [
{ name: “User 1”, id: “user1” },


const fakeUsers = [
{ name: “User 1”, id: “user1” },
{ name: “User 2”, id: “user2” },
{ name: “User 3”, id: “user3” },
{ name: “User 4”, id: “user4” },
{ name: “User 5”, id: “user5” },
{ name: “User 6”, id: “user6” }

It even works for generating random but plausible values like the names seen below.

User input (JavaScript):

const fakeUsers = [
{ name: “Sally”, id: “user1” },


const fakeUsers = [
{ name: “Sally”, id: “user1” },
{ name: “Bob”, id: “user2” },
{ name: “Joe”, id: “user3” },
{ name: “Jane”, id: “user4” },
{ name: “Jill”, id: “user5” },
{ name: “Jack”, id: “user6” }

Simplifying regular expressions

While CodeWhisperer can’t translate natural language into custom regular expressions (yet), it does know a lot of commonly used regular expressions. This saves you time by putting these regular expressions at your fingertips whenever you need them.

Let’s look at one notoriously complex but common regular expression.

User input (JavaScript):

* Function that validates whether a string is a valid email address.


* Function that validates whether a string is a valid email address.
function validateEmail(email: string): boolean {
const re =
return re.test(String(email).toLowerCase());

Learning third-party code libraries faster

CodeWhisperer helps accelerate my learning when I’m using an unfamiliar library, module, or framework. In the example below, I needed to write a utility function that would take a gzip compressed value coming from an API and translate it into a value that the rest of my code could understand. I knew the “pako” library would help me, but I had no idea how to use it. I just prompted CodeWhisperer with an import statement and a comment describing my goal.

import pako from “pako”;

* Accepts a base64-encoded, gzip-compressed JSON string and returns
* a parsed JSON object.


import pako from “pako”;

* Accepts a base64-encoded, gzip-compressed JSON string and returns
* a parsed JSON object.
export function decodeGzipJson(base64String) {
const compressed = atob(base64String);
const uncompressed = pako.inflate(compressed, { to: “string” });
return JSON.parse(uncompressed);

Documenting code

CodeWhisperer is capable of generating docstrings and comments for the code it generates, as well as for your existing code. For example, let’s say I want CodeWhisperer to document the matches() method of this FavoritesFilter TypeScript class I’ve implemented (I’ve omitted some implementation details for brevity).

class FavoritesFilter implements IAssetFilter {

matches(asset: Asset): boolean {


I can just type a doc comment delimiter (/** */) immediately above the method name and CodeWhisperer will generate the body of the doc comment for me.

Note: When using CodeWhisperer in this way you may have to manually trigger a suggestion using Option + C (Mac) or Alt + C (Windows).

class FavoritesFilter implements IAssetFilter {

* Determines whether the asset matches the filter.
matches(asset: Asset): boolean {



I hope the techniques above inspire ideas for how CodeWhisperer can make you a more productive coder. Install CodeWhisperer today to start using these time-saving techniques in your own projects. These examples only scratch the surface. As additional creative minds start applying CodeWhisperer to their daily workflows, I’m sure new techniques and best practices will continue to emerge. If you discover a novel approach that you find useful, post a comment to share what you’ve discovered. Perhaps your technique will make it into a future article and help others in the CodeWhisperer community enhance their superpowers.

Kris Schultz (he/him)

Kris Schultz has spent over 25 years bringing engaging user experiences to life by combining emerging technologies with world class design. In his role as 3D Specialist Solutions Architect, Kris helps customers leverage AWS services to power 3D applications of all sorts.

Building a CRM System: Does CRM Require Coding?

Are you curious to learn if CRM requires coding? Researching for answers to questions like do I need to know how to code to use a CRM system? What coding language is used for CRM systems? How difficult is it to learn CRM coding? Technology is just a tool. In terms of getting the kids working together and motivating them, the teacher is the most important. 

Understanding the complexities of Customer Relationship Management (CRM) requires an in-depth knowledge of how businesses interact with customers. This includes how data is collected, stored, and used to improve customer experience. With a good background in coding, businesses can create custom CRM solutions that are tailored to their customer base.

By reading this article you will learn what is CRM system, does it requires coding and the key benefits. Also, we provided our top 10+ CRM systems in 2023 that don’t require coding. Let’s dive deeper into it!

What is CRM System and Does It Require Coding?

The answer to the question “Does customer relationship management require coding?” is “No, it does not” when it comes to maintaining client relationships.

Customer relationship management (CRM) is an effective technology used by businesses to better manage their interactions with prospects and customers. While some CRM systems need coding to be utilized, most do not. The majority of CRM systems are made with an intuitive, user-friendly interface that enables users to manage their customer interactions quickly and effectively without any coding knowledge. This enables even individuals with a minimal level of technical experience to utilize CRM systems to their fullest potential.

Benefits of CRM

CRM systems have several benefits and may assist your business in a variety of ways. Let’s dive deep into the key benefits that CRM could provide.

Benefit #1. Improved customer service

CRM systems were created to enhance relationships between businesses and their customers, and that is still its primary advantage. Tracking customers’ information such as demographics, purchasing history, and messages sent through all channels, allows businesses to gain easy access to all the information they need about their customers. This ensures that employees have all the necessary information to provide a superior customer experience, resulting in higher customer satisfaction.

Benefit #2. Increased sales

Using CRM systems can help you optimize your sales process, create a well-defined sales pipeline, automate time-consuming tasks, and gain visibility into all of your sales data. This can potentially result in increased sales and productivity. You can use CRM to define a dependable sales process that can be modified as needed. It also automates tasks such as lead nurturing and follow-up emails, saving time for your team. Finally, you can use the CRM to track and analyze your sales data, making it easier to find opportunities for improvement and maximize your sales.

Benefit #3. Improved customer loyalty

After you have acquired and converted leads, it is important to keep them as customers and foster customer loyalty. Low customer retention can have harmful effects on your business, such as reduced revenue or a disrupted cash flow. Utilize your CRM and the data it provides about your customers to promote repeat business. The CRM will offer sentiment analysis, automated ticketing, customer support automation, and user behavior tracking to help identify issues and promptly handle them with your customers.

Benefit #4. Extensive analytics

Having plenty of data about your customers is essential, but it is equally important to be able to interpret it and use it to your advantage. CRM systems can help you make sense of the data by providing built-in analytic capabilities. This allows for a deeper understanding of the data, as it is broken down into understandable metrics such as click-through rates, bounce rates, and demographic information. With this knowledge, you can measure the success of your marketing campaigns and make adjustments as needed.  

Benefit #5. Higher efficiency and productivity

CRM software utilizes marketing automation technology to streamline mundane tasks such as drip campaigns, allowing employees to focus on more complex work. This technology can help make sure that nothing falls through the cracks by ensuring that all important emails are sent to the appropriate recipients. Furthermore, CRM software provides a dashboard displaying how your business is running and where your processes can be optimized.

Benefit #6. Information database centralized

CRM software is great for helping businesses manage their customer relationships, providing a single, centralized database with all the information about customers that anyone in the company needs. Sales reps can quickly and easily see what products a customer has shown interest in if the customer has interacted with the company before, records of those interactions will be included in the CRM, which can be used to inform future marketing and sales strategies. This eliminates the need to search through old files and records and ensures a smoother and more efficient customer experience.

Benefit #7. Maintaining communication with prospective leads

Lead nurturing can be a time-consuming and complicated endeavor, with many steps and chances to communicate. Utilizing a CRM simplifies the process, notifying your staff when it is time to contact the prospect and recording every point of contact, from emails to phone calls.

Benefit #8. Improved customer segmentation

Having hundreds of contacts can be daunting and overwhelming. For instance, how do you determine which customers should get your email regarding your new product in-store? A CRM can automatically categorize your contact lists based on your chosen criteria, making it easier to find the ones you need to contact whenever it is needed. You can arrange contacts by place, gender, age, buyer stage, and other factors.

Benefit #9. Automated sales reports

The CRM software’s dashboard and reporting features make it easy for your team to collect and organize data about prospective and current customers. It also helps employees automate and manage their pipelines and processes. With the CRM, team members can track their quotas and goals, evaluate their performance, and check their progress on each project with ease.

Benefit #10. Improved sales forecasting

CRM software’s automated sales reports allow you to review your past performance and strategically plan for the future. With these reports, you can identify trends and gain insight into the potential of your future sales cycle performance. This information can help you adjust your goals and metrics accordingly. 

Benefit #11. Improved internal communication

A CRM not only helps your business communicate with customers but also allows your employees to communicate more effectively with each other. Through the CRM, employees can stay up-to-date on customer interactions, maintain a consistent brand voice, and send notes, alerts, messages, and emails in one easy-to-use system.

Top 10+ CRM Systems That Don’t Require Coding in 2023


When looking for the top CRM applications, you’ll likely come across Salesforce as one of the top choices for small businesses. What makes this software stand out is that it recognizes that businesses have different requirements, unlike other CRM solutions which offer bundles of features. With Salesforce, users can pick and choose which features they need, and can always add or remove features as needed. 


Flatlogic provides a simple method for developing a custom CRM solution with complete control over the source code and scalability. An all-in-one CRM software, Flatlogics, aids businesses in streamlining their customer interaction and support procedures. It includes effective tools for automating marketing and sales processes as well as managing contacts, leads, and sales possibilities. Moreover, it has capabilities like data visualizations, tailored and automated emails, and automatic client segmentation. Businesses may monitor client interactions, foster connections, and streamline manual procedures using Flatlogics CRM to increase customer satisfaction.

Zoho CRM

Zoho was a cloud-based CRM platform, but over the years, it has evolved to become an all-in-one suite to help businesses manage their invoicing and customer management needs. Though not open-source, it can be integrated with other platforms via third-party apps. For example, Zapier can be used to connect to eCommerce platforms like Shopify. For small businesses, the Standard plan is recommended, which offers custom reports and analytics, lead scoring, webhooks, and more. This plan is suitable for startups or new businesses.

HubSpot CRM

In 2014, HubSpot launched its free CRM service, broadening its reach in a fresh direction. With this expansion, you can now seamlessly integrate marketing and sales operations into one platform. The HubSpot suite is not only ideal for running inbound marketing campaigns but also for enhancing customer connections and driving more sales. Moreover, the unlimited team feature enables multiple users to come together and form a productive team. Upon signing up for the HubSpot CRM forever free plan, you can choose your level of expertise with CRM software. Subsequently, you can gain a comprehensive orientation of the software and begin importing your earlier data, sorting contacts, and inviting team members. The HubSpot dashboard collection consolidates sales, marketing, service, and CMS, making it easy to manage. Additionally, you can take advantage of six unique sales reports that help track your monthly progress and keep the contact information organized in a centralized, customizable database. As a bonus, HubSpot CRM offers advanced features such as email tracking, deal pipelines, messenger integrations, and email templates – features not typically found in other free CRMs.


If you’re searching for a free and open-source CRM, Bitrix24 is a great option. Even though the open source side is handled by a Bitrix24 partner, the cost-free features it provides are what make it such an attractive choice. It offers limitless feature records, sales tracking, email marketing, and more – all without charging a top-dollar price. With over 7,000,000 companies using Bitrix24, it has become a go-to tool. The only downside is the learning curve (even with the paid version) compared to platforms like Freshsales. Despite this, it is an ideal CRM software for small businesses that need collaboration software that doesn’t break their budget and still offers almost all of the advanced features. With this in mind, it’s an excellent choice for improving collaboration and communication, as well as managing clients with ease. 


Pipedrive is an easily accessible CRM tool. It provides detailed sales-based reporting tools and a visually appealing user interface that makes it simple to track leads and contacts. The platform also offers automated call tracking and a chatbot feature, allowing for improved communication both internally and externally. This makes Pipedrive an ideal CRM tool for small to medium-sized businesses, providing a comprehensive solution that covers most of their needs. 


Insightly is a great and budget-friendly option for businesses looking for an easy, free, and open-source CRM. It offers fast onboarding and a free lifetime plan and a comprehensive tracking system, perfect for newcomers. While it may have slightly fewer features than some of its competitors, Insightly is an excellent tool for carefully managing customer relationships and monitoring the sales pipeline to gain more insights into the sales process. The main dashboard provides detailed stats on sales and pipelines, and you can store up to 25,000 records, including contacts, leads, organizations, reports, and projects. All in all, I believe Insightly is a sensible choice for those who don’t need a lot of features and are looking to get started on a CRM right away.


Apptivo is an award-winning cloud-based CRM software that provides users with an extensive suite of 50+ applications to help them reduce inaccuracies, save time, and get access to essential information and tools to collaborate with their customers more effectively. This software offers a variety of services such as CRM, project management, invoicing, and much more. With over 200,000+ users, Apptivo is one of the most popular CRMs. It also offers plenty of integrations and solutions that make it stand out from the rest


With over 50,000 companies using it and 7 million downloads, SugarCRM is one of the most sought-after CRM applications on the market. The application is available in 9+ languages and is capable of accommodating organizations of all sizes, from small startups to those with more than 10,000 employees. Open-source in nature, this CRM is highly customizable and adaptable to different structures and hierarchies. As such, it requires a dedicated team to manage, operate and maintain its smooth functioning. 


With Nutshell, you can streamline your sales process and optimize your efficiency. It has a variety of views to help you manage your leads and sales, including board view, chart view, list view, and map view. This powerful yet simple CRM tool is used by over 25,000 business professionals around the world to keep track of their customer data. 


Freshsales is a great option for those looking for a free and open-source CRM software suite. Focusing solely on scalability and sales, Freshsales can be used to engage with customers, close deals, and attract new sales. The free forever plan offers unlimited users, contacts, and premium support, including email, chat, and phone support. However, Freshsales Free is missing out on some advanced features like email tracking, behavior analytics, and reports. Though there is a minimal learning curve, it is best for startups, and not necessarily the best choice for small to mid-sized businesses. Overall, Freshsales is a good CRM to start with for getting a new lead and nurturing it throughout its life cycle. 


Those seeking essential features in CRM software such as email marketing, calendar/reminder systems, and client tracking should consider Copper. It offers a direct import of records from Gmail communications and allows users to sync meetings with contacts. Additionally, Copper’s integrations with most common applications such as Intuit Quickbooks, Slack, DocuSign, Zapier, and Mailchimp are especially beneficial for G Suite users. 

Summing Up

Although the major part of CRM software is created for non-technical users, coding experience is often not necessary. Businesses of all sizes may utilize CRM software to manage client contacts, streamline procedures, and enhance customer service thanks to pre-built templates, automated workflows, and drag-and-drop interfaces. Coding skills may be required for more intricate adaptations, such as unique integrations or reports.

The post Building a CRM System: Does CRM Require Coding? appeared first on Flatlogic Blog.

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Introducing Open Source AWS CardDemo for Mainframe Modernization  

We are excited to share that AWS has open sourced its AWS Mainframe Modernization CardDemo application for use by the community modernizing mainframe applications.

CardDemo is a sample mainframe application. It is designed and developed to test AWS Mainframe Modernization and partner technology for many modernization use cases such as discovery, migration, modernization, performance test, augmentation, service enablement, service extraction, test creation, test harness, etc. It can be used to showcase modernization using patterns such as automated refactor, replatform, and augmentation. Since it is a relatively small standalone application which uses synthetic data, CardDemo enables our customers and partners to test, evaluate, and show solutions without the risk of exposing customer business logic and data to the public.

By open sourcing CardDemo under the Apache 2.0 license, we want to empower builders and foster innovation around the modernization of mainframe applications. With the CardDemo mainframe application, you can quickly experiment with new solutions and integrations. We also want to develop the knowledge of solutions for modernizing mainframe applications by learning from workshops, tutorials, or demonstrations leveraging CardDemo.

In this post, we describe the functions, the technical components and the structure of the CardDemo application. Then we show how to install the application on a mainframe. Finally, we highlight some additional resources that help understand how you can modernize this application using cloud technology and contribute to this open source application.

Overview of CardDemo

CardDemo is a mainframe credit card management application that allows you to view and update account information, credit card data and transactions for a given date range. You interact with the frontend through typical mainframe green screens using one of two personas:

A back-office user who can view and update a restricted set of account and credit card attributes, create transactions manually, and view reports on transactions.
An administrator who can view, add, update, and delete other users.

A series of batch jobs is included in CardDemo. You can run this batch cycle to process transaction data received from a simulated external transaction feed file. The batch posts transactions, performs interest calculations on outstanding balances and produces reports and extracts that it stores for later reference.

To help you get started with CardDemo, we have provided a starter database populated with synthetic data. The key entities in this system are Account, Customers associated with the account and Cards owned by the account. Visit this link to see the entity relationship diagram.

Though it is possible to have many-to-many relationships between these three entities, we have constrained ourselves to a 1:1:1 relationship between them in the first release. This means that one account can be associated with at most one card and one person.


The CardDemo application is composed of the following components:

Basic Mapping Support (BMS) maps written using the assembler language macro instructions (macros) to create the screens.

Virtual Storage Access Method to store the data. In addition to VSAM, the application also makes use of physical sequential (PS) datasets and generation data groups (GDGs).

Common Business Oriented Language (COBOL) for the logic to perform edits and execute business processes.

Application Flow

The application has two flows based on the type of user logging in to the CardDemo login screen.

Back-office user flow
Administrator flow

When you login as a back-office user to the application, it displays the main menu which gives you the ability to choose from several options to perform various application functions. As a back-office user you can manage accounts, credit cards, transactions and bill payments.

When you log in as an administrator, the admin menu provides a set of options to list, add, update, and delete users.

Installing CardDemo


The README file in the CardDemo GitHub repository has detailed step-by-step instructions about how to install CardDemo on a mainframe.

Here is a high-level overview of the installation process.

Retrieve the repository from GitHub and upload the supplied code and data to the mainframe.
Compile screens and batch programs.
Define CICS resources used by CardDemo.
Start the application and begin testing CardDemo.

Step 1: Uploading and organizing the GitHub artifacts on the mainframe

Take the code under the app folder of the repository and upload it to the mainframe.
Upload the provided sample data usinge a binary mode of transfer.
Run jobs listed in Step 3 of the installation process in the README file to re-structure the raw data provided so that it fits the format that CardDemo expects (VSAM, physical sequential files, and GDG groups.)

Step 2: Compiling the programs

Obtain JCLs to compile maps, CICS programs and batch COBOL from your mainframe administrator. We have provided sample JCLs  for reference.

Execute the following steps:

Compile the maps in the BMS folder.
Compile programs starting with CO using your CICS COBOL compile job.
Compile batch programs starting with CB using the COBOL only compiler.

Step 3: Creating CICS Resource Definitions

Once you finish compiling the programs, you need to make CICS aware of the resources created. Follow the instructions under bullet 5 of the README file to do this.

You can either:

Use the batch system definition utility (DFHCSDUP) to import the CardDemo CSD file

Or use the online resource definition program CEDA to define all the transactions, files, and transient storage queues required by CICS.

Step 4: Start using CardDemo

Clear the CICS screen and type CC00 to invoke the CardDemo main menu screen.
If you see a menu screen prompting you for a User ID and Password, you have successfully installed CardDemo.
To use the application as a back-office user, login with id USER0001 and password PASSWORD.
To use the application as an administrator, log in with id ADMIN001 and password PASSWORD.

Overview of programs provided with CardDemo

The README file has an documented the purpose of each program provided with CardDemo.

Here is a brief overview of the functions provided:

Initial Screen: From the frontend perspective, the first transaction that you encounter in CardDemo is the sign-on screen shown previously. This is where you choose whether you are logging in as a back office or admin user.
Main Menu: If you log in as a back-office user, you see a user function menu. You can configure which programs appear in each slot of this menu by editing a copybook.

3. Admin Menu: If on the other hand, you log in as an administrator, you will see a list of administration functions. This too is a configurable menu which is initially setup to show four functions for managing CardDemo users.

Batch: As is typical in mainframe applications, CardDemo has a batch process to perform calculations. CardDemo expects you to stop all online activity and close the files opened to CICS while batch is in progress.

The batch refreshes data in the application files and then posts transactions received to accounts before calculating interest on outstanding balances.

You can find instructions to set up and run this process under the heading Running Full Batch in the README file  and you can review the impact of the batch by comparing the account balances before and after running it.

Congratulations!! You have setup CardDemo and are ready to start with your own use cases.


By open sourcing CardDemo, we want to make it easier for our customers, partners, and for the mainframe community to learn and experiment with mainframe code and to understand solutions for modernizing mainframe applications.

We also invite builders everywhere to add their own extensions and features to this CardDemo code base. If you would like to add features and contribute to the further development of CardDemo, you are welcome to submit your code for inclusion in CardDemo following the guidelines we have provided for you in this contribution guide.

You can learn more about the AWS Mainframe Modernization service and see CardDemo in action in the following webinars:

AWS Mainframe Modernization Blu Age Refactor with CardDemo demonstration
AWS Mainframe Modernization Micro Focus Replatform with CardDemo demonstration

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Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning

Identifying the root causes of observed changes in complex systems can be a difficult task that requires both deep domain knowledge and potentially hours of manual work. For instance, we may want to analyze an unexpected drop in profit of a product sold in an online store, where various intertwined factors can impact the overall profit of the product in subtle ways.

Wouldn’t it be nice if we had automated tools to simplify and accelerate this task? A library that can automatically identify the root causes of an observed effect with a few lines of code?

This is the idea behind the root cause analysis (RCA) features of the DoWhy open source Python library, to which AWS contributed a large set of novel causal machine learning (ML) algorithms last year. These algorithms are the result of years of Amazon research on graphical causal models and were released in DoWhy v0.8 in July last year. Furthermore, AWS joined forces with Microsoft to form a new organization called PyWhy, now home of DoWhy. PyWhy’s mission, according to the charter, is to “build an open source ecosystem for causal machine learning that moves forward the state of the art and makes it available to practitioners and researchers. We build and host interoperable libraries, tools, and other resources spanning a variety of causal tasks and applications, connected through a common API on foundational causal operations and a focus on the end-to-end-analysis process.”

In this article, we will have a closer look at these algorithms. Specifically, we want to demonstrate their applicability in the context of root cause analysis in complex systems.

Applying DoWhy’s causal ML algorithms to this kind of problem can reduce the time to find a root cause significantly. To demonstrate this, we will dive deep into an example scenario based on randomly generated synthetic data where we know the ground truth.

The scenario

Suppose we are selling a smartphone in an online shop with a retail price of $999. The overall profit from the product depends on several factors, such as the number of sold units, operational costs or ad spending. On the other hand, the number of sold units, for instance, depends on the number of visitors on the product page, the price itself and potential ongoing promotions. Suppose we observe a steady profit of our product over the year 2021, but suddenly, there is a significant drop in profit at the beginning of 2022. Why?

In the following scenario, we will use DoWhy to get a better understanding of the causal impacts of factors influencing the profit and to identify the causes for the profit drop. To analyze our problem at hand, we first need to define our belief about the causal relationships. For this, we collect daily records of the different factors affecting profit. These factors are:

Shopping Event?: A binary value indicating whether a special shopping event took place, such as Black Friday or Cyber Monday sales.

Ad Spend: Spending on ad campaigns.

Page Views: Number of visits on the product detail page.

Unit Price: Price of the device, which could vary due to temporary discounts.

Sold Units: Number of sold phones.

Revenue: Daily revenue.

Operational Cost: Daily operational expenses which includes production costs, spending on ads, administrative expenses, etc.

Profit: Daily profit.

Looking at these attributes, we can use our domain knowledge to describe the cause-effect relationships in the form of a directed acyclic graph, which represents our causal graph in the following. The graph is shown here:

An arrow from X to Y, X → Y in this diagram describes a direct causal relationship, where X is the cause of Y. In this scenario we know the following:

Shopping Event? impacts:
→ Ad Spend: To promote the product on special shopping events, we require additional ad spending.
→ Page Views: Shopping events typically attract a large number of visitors to an online retailer due to discounts and various offers.
→ Unit Price: Typically, retailers offer some discount on the usual retail price on days with a shopping event.
→ Sold Units: Shopping events often take place during annual celebrations like Christmas, Father’s day, etc, when people often buy more than usual.

Ad Spend impacts:
→ Page Views: The more we spend on ads, the more likely people will visit the product page.
→ Operational Cost: Ad spending is part of the operational cost.

Page Views impacts:
→ Sold Units: The more people visiting the product page, the more likely the product is bought. This is quite obvious seeing that if no one would visit the page, there wouldn’t be any sale.

Unit Price impacts:
→ Sold Units: The higher/lower the price, the less/more units are sold.
→ Revenue: The daily revenue typically consist of the product of the number of sold units and unit price.

Sold Units impacts:
→ Sold Units: Same argument as before, the number of sold units heavily influences the revenue.
→ Operational Cost: There is a manufacturing cost for each unit we produce and sell. The more units we well the higher the revenue, but also the higher the manufacturing costs.

Operational Cost impacts:
→ Profit: The profit is based on the generated revenue minus the operational cost.

Revenue impacts:
→ Profit: Same reason as for the operational cost.

Step 1: Define causal models

Now, let us model these causal relationships with DoWhy’s graphical causal model (GCM) module. In the first step, we need to define a so-called structural causal model (SCM), which is a combination of the causal graph and the underlying generative models describing the data generation process.

To model the graph structure, we use NetworkX, a popular open source Python graph library. In NetworkX, we can represent our causal graph as follows:

import networkx as nx

causal_graph = nx.DiGraph([(‘Page Views’, ‘Sold Units’),
(‘Revenue’, ‘Profit’),
(‘Unit Price’, ‘Sold Units’),
(‘Unit Price’, ‘Revenue’),
(‘Shopping Event?’, ‘Page Views’),
(‘Shopping Event?’, ‘Sold Units’),
(‘Shopping Event?’, ‘Unit Price’),
(‘Shopping Event?’, ‘Ad Spend’),
(‘Ad Spend’, ‘Page Views’),
(‘Ad Spend’, ‘Operational Cost’),
(‘Sold Units’, ‘Revenue’),
(‘Sold Units’, ‘Operational Cost’),
(‘Operational Cost’, ‘Profit’)])

Next, we look at the data from 2021:

import pandas as pd

pd.options.display.float_format = ‘${:,.2f}’.format # Format dollar columns
data_2021 = pd.read_csv(‘2021 Data.csv’, index_col=’Date’)

As we see, we have one sample for each day in 2021 with all the variables in the causal graph. Note that in the synthetic data we consider in this blog post, shopping events were also generated randomly.

We defined the causal graph, but we still need to assign generative models to the nodes. With DoWhy, we can either manually specify those models, and configure them if needed, or automatically infer “appropriate” models using heuristics from data. We will leverage the latter here:

from dowhy import gcm

# Create the structural causal model object
scm = gcm.StructuralCausalModel(causal_graph)

# Automatically assign generative models to each node based on the given data
gcm.auto.assign_causal_mechanisms(scm, data_2021)

Whenever available, we recommend assigning models based on prior knowledge as then models would closely mimic the physics of the domain, and not rely on nuances of the data. However, here we asked DoWhy to do this for us instead.

Step 2: Fit causal models to data

After assigning a model to each node, we need to learn the parameters of the model:

gcm.fit(scm, data_2021)

The fit method learns the parameters of the generative models in each node. The fitted SCM can now be used to answer different kinds of causal questions.

Step 3: Answer causal questions

What are the key factors influencing the variance in profit?

At this point, we want to understand which factors drive changes in the Profit. Let us first have a closer look at the Profit over time. For this, we are using pandas to plot the Profit over time for 2021, where the produced plot shows the Profit in dollars on the Y-axis and the time on the X-axis.

data_2021[‘Profit’].plot(ylabel=’Profit in $’, figsize=(15,5), rot=45)

We see some significant spikes in the Profit across the year. We can further quantify this by looking at the standard deviation, which we can estimate using the std() function from pandas:


The estimated standard deviation of ~259247 dollars is quite significant. Looking at the causal graph, we see that Revenue and Operational Cost have a direct impact on the Profit, but which of them contribute the most to the variance? To find this out, we can make use of the direct arrow strength algorithm that quantifies the causal influence of a specific arrow in the graph:

import numpy as np

def convert_to_percentage(value_dictionary):
total_absolute_sum = np.sum([abs(v) for v in value_dictionary.values()])
return {k: abs(v) / total_absolute_sum * 100 for k, v in value_dictionary.items()}

arrow_strengths = gcm.arrow_strength(scm, target_node=’Profit’)

figure_size=[15, 10])

In this causal graph, we see how much each node contributes to the variance in Profit. For simplicity, the contributions are converted to percentages. Since Profit itself is only the difference between Revenue and Operational Cost, we do not expect further factors influencing the variance. As we see, the Revenue contributes 74.45 percent and has more impact than the Operational Cost which contributes 25.54 percent. This makes sense seeing that the Revenue typically varies more than the Operational Cost due to the stronger dependency on the number of sold units. Note that DoWhy also supports other kinds of measures, for instance, KL divergence.

While the direct influences are helpful in understanding which direct parents influence the most on the variance in Profit, this mostly confirms our prior belief. The question of which factor is now ultimately responsible for this high variance is, however, still unclear. Revenue itself is simply based on Sold Units and the Unit Price. Although we could recursively apply the direct arrow strength to all nodes, we would not get a correctly weighted insight into the influence of upstream nodes on the variance.

What are the important causal factors contributing to the variance in Profit? To find this out, we can use DoWhy’s intrinsic causal contribution method that attributes the variance in Profit to the upstream nodes in the causal graph. For this, we first define a function to plot the values in a bar plot and then use this to display the estimated contributions to the variance as percentages:

import matplotlib.pyplot as plt

def bar_plot(value_dictionary, ylabel, uncertainty_attribs=None, figsize=(8, 5)):
value_dictionary = {k: value_dictionary[k] for k in sorted(value_dictionary)}
if uncertainty_attribs is None:
uncertainty_attribs = {node: [value_dictionary[node], value_dictionary[node]] for node in value_dictionary}

_, ax = plt.subplots(figsize=figsize)
ci_plus = [uncertainty_attribs[node][1] – value_dictionary[node] for node in value_dictionary.keys()]
ci_minus = [value_dictionary[node] – uncertainty_attribs[node][0] for node in value_dictionary.keys()]
yerr = np.array([ci_minus, ci_plus])
yerr[abs(yerr) < 10**-7] = 0
plt.bar(value_dictionary.keys(), value_dictionary.values(), yerr=yerr, ecolor=’#1E88E5′, color=’#ff0d57′, width=0.8)


iccs = gcm.intrinsic_causal_influence(scm, target_node=’Profit’, num_samples_randomization=500)

bar_plot(convert_to_percentage(iccs), ylabel=’Variance attribution in %’)

The scores shown in this bar chart are percentages indicating how much variance each node is contributing to Profit — without inheriting the variance from its parents in the causal graph. As we see quite clearly, the Shopping Event has by far the biggest influence on the variance in our Profit. This makes sense, seeing that the sales are heavily impacted during promotion periods like Black Friday or Prime Day and, thus, impact the overall profit. Surprisingly, we also see that factors such as the number of sold units or number of page views have a rather small influence, i.e., the large variance in profit can be almost completely explained by the shopping events. Let’s check this visually by marking the days where we had a shopping event. To do so, we use the pandas plot function again, but additionally mark all points in the plot with a vertical red bar where a shopping event occured:

data_2021[‘Profit’].plot(ylabel=’Profit in $’, figsize=(15,5), rot=45)
plt.vlines(np.arange(0, data_2021.shape[0])[data_2021[‘Shopping Event?’]], data_2021[‘Profit’].min(), data_2021[‘Profit’].max(), linewidth=10, alpha=0.3, color=’r’)

We clearly see that the shopping events coincide with the high peaks in profit. While we could have investigated this manually by looking at all kinds of different relationships or using domain knowledge, the tasks gets much more difficult as the complexity of the system increases. With a few lines of code, we obtained these insights from DoWhy.

What are the key factors explaining the Profit drop on a particular day?

After a successful year in terms of profit, newer technologies come to the market and, thus, we want to keep the profit up and get rid of excess inventory by selling more devices. In order to increase the demand, we therefore lower the retail price by 10% at the beginning of 2022. Based on a prior analysis, we know that a decrease of 10% in the price would roughly increase the demand by 13.75%, a slight surplus. Following the price elasticity of demand model, we expect an increase of around 37.5% in number of Sold Units. Let us take a look if this is true by loading the data for the first day in 2022 and taking the fraction between the numbers of Sold Units from both years for that day:

first_day_2022 = pd.read_csv(‘2022 First Day.csv’, index_col=’Date’)
(first_day_2022[‘Sold Units’][0] / data_2021[‘Sold Units’][0] – 1) * 100

Surprisingly, we only increased the number of sold units by ~19%. This will certainly impact the profit given that the revenue is much smaller than expected. Let us compare it with the previous year at the same time:

(1 – first_day_2022[‘Profit’][0] / data_2021[‘Profit’][0]) * 100

Indeed, the profit dropped by ~8.5%. Why is this the case seeing that we would expect a much higher demand due to the decreased price? Let us investigate what is going on here.

In order to figure out what contributed to the Profit drop, we can make use of DoWhy’s anomaly attribution feature. Here, we only need to specify the target node we are interested in (the Profit) and the anomaly sample we want to analyze (the first day of 2022). These results are then plotted in a bar chart indicating the attribution scores of each node for the given anomaly sample:

attributions = gcm.attribute_anomalies(scm, target_node=’Profit’, anomaly_samples=first_day_2022)

bar_plot({k: v[0] for k, v in attributions.items()}, ylabel=’Anomaly attribution score’)

A positive attribution score means that the corresponding node contributed to the observed anomaly, which is in our case the drop in Profit. A negative score of a node indicates that the observed value for the node is actually reducing the likelihood of the anomaly (e.g., a higher demand due to the decreased price should increase the profit). More details about the interpretation of the score can be found in our research paper. Interestingly, the Page Views stand out as a factor explaining the Profit drop that day as indicated in the bar chart shown here.

While this method gives us a point estimate of the attributions for the particular models and parameters we learned, we can also use DoWhy’s confidence interval feature, which incorporates uncertainties about the fitted model parameters and algorithmic approximations:

median_attributions, confidence_intervals, = gcm.confidence_intervals(

bar_plot(median_attributions, ‘Anomaly attribution score’, confidence_intervals)

Note, in this bar chart we see the median attributions over multiple runs on smaller data sets, where each run re-fits the models and re-evaluates the attributions. We get a similar picture as before, but the confidence interval of the attribution to Sold Units also contains zero, meaning its contribution is insignificant. But some important questions still remain: Was this only a coincidence and, if not, which part in our system has changed? To find this out, we need to collect some more data.

What caused the profit drop in Q1 2022?

While the previous analysis is based on a single observation, let us see if this was just coincidence or if this is a persistent issue. When preparing the quarterly business report, we have some more data available from the first three months. We first check if the profit dropped on average in the first quarter of 2022 as compared to 2021. Similar as before, we can do this by taking the fraction between the average Profit of 2022 and 2021 for the first quarter:

data_first_quarter_2021 = data_2021[data_2021.index <= ‘2021-03-31’]
data_first_quarter_2022 = pd.read_csv(“2022 First Quarter.csv”, index_col=’Date’)

(1 – data_first_quarter_2022[‘Profit’].mean() / data_first_quarter_2021[‘Profit’].mean()) * 100

Indeed, the profit drop is persistent in the first quarter of 2022. Now, what is the root cause of this? Let us apply DoWhy’s distribution change method to identify the part in the system that has changed:

median_attributions, confidence_intervals = gcm.confidence_intervals(
lambda: gcm.distribution_change(scm,
# Here, we are intersted in explaining the differences in the mean.
difference_estimation_func=lambda x, y: np.mean(y) – np.mean(x))

bar_plot(median_attributions, ‘Profit change attribution in $’, confidence_intervals)

In our case, the distribution change method explains the change in the mean of Profit, i.e., a negative value indicates that a node contributes to a decrease and a positive value to an increase of the mean. Using the bar chart, we get now a very clear picture that the change in Unit Price has actually a slightly positive contribution to the expected Profit due to the increase of Sold Units, but it seems that the issue is coming from the Page Views which has a negative value. While we already understood this as a main driver of the drop at the beginning of 2022, we have now isolated and confirmed that something changed for the Page Views as well. Let’s compare the average Page Views with the previous year.

(1 – data_first_quarter_2022[‘Page Views’].mean() / data_first_quarter_2021[‘Page Views’].mean()) * 100

Indeed, the number of Page Views dropped by ~14%. Since we eliminated all other potential factors, we can now dive deeper into the Page Views and see what is going on there. This is a hypothetical scenario, but we could imagine it could be due to a change in the search algorithm which ranks this product lower in the results and therefore drives fewer customers to the product page. Knowing this, we could now start mitigating the issue.

With the help of DoWhy’s new features for graphical causal models, we only needed a few lines of code to automatically pinpoint the main drivers of a particular outlier and, especially, were able to identify the main factors that caused a shift in the distribution.


In this article, we have shown how DoWhy can help in root cause analysis of a drop in profits for an example online shop. For this, we looked at DoWhy features, such as arrow strengths, intrinsic causal influences, anomaly attribution and distribution change attribution. But did you know that DoWhy can also be used for estimating average treatment effects, causal structure learning, diagnosis of causal structures, interventions and counterfactuals? If this is interesting to you, we invite you to visit our PyWhy homepage or the DoWhy documentation to learn more. There is also an active community on the DoWhy Discord where scientists and ML practitioners can meet, ask questions and get help. We also host weekly meetings on Discord where we discuss current developments. Come join us!

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Building .NET 7 Applications with AWS CodeBuild

AWS CodeBuild is a fully managed DevOps service for building and testing your applications. As a fully managed service, there is no infrastructure to manage and you pay only for the resources that you use when you are building your applications. CodeBuild provides a default build image that contains the current Long Term Support (LTS) version of the .NET SDK.

Microsoft released the latest version of .NET in November. This release, .NET 7, includes performance improvements and functionality, such as native ahead of time compilation. (Native AoT)..NET 7 is a Standard Term Support release of the .NET SDK. At this point CodeBuild’s default image does not support .NET 7. For customers that want to start using.NET 7 right away in their applications, CodeBuild provides two means of customizing your build environment so that you can take advantage of .NET 7.

The first option for customizing your build environment is to provide CodeBuild with a container image you create and maintain. With this method, customers can define the build environment exactly as they need by including any SDKs, runtimes, and tools in the container image. However, this approach requires customers to maintain the build environment themselves, including patching and updating the tools. This approach will not be covered in this blog post.

A second means of customizing your build environment is by using the install phase of the buildspec file. This method uses the default CodeBuild image, and adds additional functionality at the point that a build starts. This has the advantage that customers do not have the overhead of patching and maintaining the build image.

Complete documentation on the syntax of the buildspec file can be found here:


Your application’s buildspec.yml file contains all of the commands necessary to build your application and prepare it for deployment. For a typical .NET application, the buildspec file will look like this:

version: 0.2
– dotnet restore Net7TestApp.sln
– dotnet build Net7TestApp.sln

Note: This build spec file contains only the commands to build the application, commands for packaging and storing build artifacts have been omitted for brevity.

In order to add the .NET 7 SDK to CodeBuild so that we can build your .NET 7 applications, we will leverage the install phase of the buildspec file. The install phase allows you to install any third-party libraries or SDKs prior to beginning your actual build.

– curl -sSL https://dot.net/v1/dotnet-install.sh | bash /dev/stdin –channel STS

The above command downloads the Microsoft install script for .NET and uses that script to download and install the latest version of the .NET SDK, from the Standard Term Support channel. This script will download files and set environment variables within the containerized build environment. You can use this same command to automatically pull the latest Long Term Support version of the .NET SDK by changing the command argument STS to LTS.

Your updated buildspec file will look like this:

version: 0.2
– curl -sSL https://dot.net/v1/dotnet-install.sh | bash /dev/stdin –channel STS
– dotnet restore Net7TestApp/Net7TestApp.sln
– dotnet build Net7TestApp/Net7TestApp.sln

Once you check in your buildspec file, you can start a build via the CodeBuild console, and your .NET application will be built using the .NET 7 SDK.

As your build runs you will see output similar to this:

Welcome to .NET 7.0!
SDK Version: 7.0.100
The .NET tools collect usage data in order to help us improve your experience. It is collected by Microsoft and shared with the community. You can opt-out of telemetry by setting the DOTNET_CLI_TELEMETRY_OPTOUT environment variable to ‘1’ or ‘true’ using your favorite shell.

Read more about .NET CLI Tools telemetry: https://aka.ms/dotnet-cli-telemetry
Installed an ASP.NET Core HTTPS development certificate.
To trust the certificate run ‘dotnet dev-certs https –trust’ (Windows and macOS only).
Learn about HTTPS: https://aka.ms/dotnet-https
Write your first app: https://aka.ms/dotnet-hello-world
Find out what’s new: https://aka.ms/dotnet-whats-new
Explore documentation: https://aka.ms/dotnet-docs
Report issues and find source on GitHub: https://github.com/dotnet/core
Use ‘dotnet –help’ to see available commands or visit: https://aka.ms/dotnet-cli
Determining projects to restore…
Restored /codebuild/output/src095190443/src/git-codecommit.us-east-2.amazonaws.com/v1/repos/net7test/Net7TestApp/Net7TestApp/Net7TestApp.csproj (in 586 ms).
[Container] 2022/11/18 14:55:08 Running command dotnet build Net7TestApp/Net7TestApp.sln
MSBuild version 17.4.0+18d5aef85 for .NET
Determining projects to restore…
All projects are up-to-date for restore.
Net7TestApp -> /codebuild/output/src095190443/src/git-codecommit.us-east-2.amazonaws.com/v1/repos/net7test/Net7TestApp/Net7TestApp/bin/Debug/net7.0/Net7TestApp.dll
Build succeeded.
0 Warning(s)
0 Error(s)
Time Elapsed 00:00:04.63
[Container] 2022/11/18 14:55:13 Phase complete: BUILD State: SUCCEEDED
[Container] 2022/11/18 14:55:13 Phase context status code: Message:
[Container] 2022/11/18 14:55:13 Entering phase POST_BUILD
[Container] 2022/11/18 14:55:13 Phase complete: POST_BUILD State: SUCCEEDED
[Container] 2022/11/18 14:55:13 Phase context status code: Message:


Adding .NET 7 support to AWS CodeBuild is easily accomplished by adding a single line to your application’s buildspec.yml file, stored alongside your application source code. This change allows you to keep up to date with the latest versions of .NET while still taking advantage of the managed runtime provided by the CodeBuild service.

About the author:

Tom Moore

Tom Moore is a Sr. Specialist Solutions Architect at AWS, and specializes in helping customers migrate and modernize Microsoft .NET and Windows workloads into their AWS environment.

Top Tools every Software Developer should know in 2022

With the increase in popularity of software development in the market, the adoption of its tools has also increased. Now, programmers prefer to use the right software developer tool while creating a solution for the client as it makes their lives easier. Besides, the right set of tools can help in getting the maximum output each day. But this choice might be difficult because of the huge number of software development tools available in the market. So, to make this choice easy for you, here, in this blog, we’ll go through a list of top software development tools in 2022 that can be used to boost the professional performance of the software development team.

What is Software Development?

Software development is a simple process that every software programmer uses to create computer programs. The entire process of developing a system for any business organization is known as Software Development Life Cycle (SDLC). This process includes various phases that offer a perfect method for creating products that meet both user requirements and technical specifications. For this, web developers use different types of development tools and the use of the right tool can help in streamlining the entire software development process.

Why Use Software Development Tools?

Developers use software tools to investigate and complete the business processes, optimize them, and document the software development processes. With the use of such tools, the software developers can create a project whose outcome can be more productive. Using the development tools, a software developer can manage the workflow easily.

15 Best Software Development Tools

Some of the top software programming tools every developer can use are:


UltraEdit is one of the best tools when it comes to creating software with proper security, flexibility, and performance. It comes with an all-access package that offers the developers access to various tools like an integrated FTP client, a file finder, and a Git integration solution. It is a very powerful text editor that has the capabilities to handle large files with a breeze.

Key Features:

It can handle and load large files with proper performance, file load, and startup.
Supports complete OS integration like shell extensions and command lines.
You can configure, customize, and reskin the entire application with the help of beautiful themes.
Accesses the server and opens files with SFTP browser/ Native FTP.
Helps in finding, comparing, and replacing inside files at blazing speed.
Spots visual differences between codes easily.
The all-access package of UltraEdit comes at $99.95 per year.


Atom is a top integrated development environment (IDE). And it’s open-source nature makes it run on the majority of the popular operating systems. It is a software development tool that is known for its rich level of customization and vast list of third-party integrations. Atom’s attribute, Autocomplete enables the developers to write the code easily and quickly. Besides this, the browser function of this tool simplifies project file management and this is possible because of its interface that comes with numerous panes to compare, view, edit, and compare files, all at once. Basically, Atom is the best option for developers because it can support every popular framework and programming language.

Key Features:

Atom supports cross-platform editing, this means that it can work for different types of operating systems like OS X, Windows, and Linux.
It uses the Electron framework for offering amazing web technologies.
It is a customizable tool that comes with effective features for a better look and feel.
Some of the important features of Atom like smart autocomplete, built-in package manager, multiple panes, find & replace feature, file system browser, etc.


Quixy is used by enterprises for its cloud-based no-code platform approach. This tool helps businesses automate their workflows and create all types of enterprise-grade applications. Besides, it helps in eliminating the manual processes and turning different ideas into apps to make businesses transparent, productive, and innovative. 

Key Features:

Quixy helps in creating an app interface as per the client’s requirement by easily dragging and dropping 40+ form fields.
It seamlessly integrates the third-party app with the help of ready-to-use Webhooks, connectors, and API integrations.
It can model any process and create simple complex workflows.
It helps in deploying applications with a single click and making changes anytime.
Quixy also enables the developers to use it on any browser and device even in offline mode.
Offers live actionable dashboards and reports with the idea of exporting data in various formats.


Linx helps in creating and automating backend apps with a low-coding approach. This tool has the capability to accelerate the design, automation, and development of custom business processes. It offers services for easily integrating systems, apps, and databases. 

Key Features:

Drag and drop, easy-to-use IDE and Server.
It offers live debugging with the use of step-through logic.
Offers 100 pre-built plugins for rapid development.
It automates processes with the help of directory events and timers.


GitHub is one of the most popular software development and collaboration tool for code management and review. It enables its users to create software and apps, host the code, manage the projects, and review the code. 

Key Features:

With the help of GitHub, web app developers can easily document their source code.
Some of the features of GitHub like access control and code security make it a more useful tool for all the team members.
GitHub’s project management tools enable app developers to coordinate tasks easily.
This tool can be hosted on servers & cloud platforms and can run on operating systems like Mac and Windows. 


Embold is one of the most popular tools when it comes to fixing bugs before deployment. It helps in saving a lot of energy and time in the long run. It is a software analytics platform that helps the developers to analyze the source code and uncovers problems that might impact robustness, stability, security, and maintainability.

Key Features:

Embold offers plugins that can help in picking up code vulnerabilities.
It helps in integrating the system seamlessly with Bitbucket, GitHub, and Git.
Embold comes with unique anti-pattern detection that helps in preventing the compounding of unmaintainable code.
With Emhold, it is possible to get faster and deeper checks for more than 10 languages.

Zoho Creator

Zoho Creator, a low-code software development tool enables rapid development and deployment of web applications and assists to create powerful enterprise software apps. Besides, it doesn’t require endless lines of code for creating an app. It comes with different features like JavaScript, Artificial Intelligence, Cloud functions, and more. There are more than 4 million users of this tool all over the world and they use it to enhance the productivity of their business.

Key Features:

Zoho Creator enables the creation of more apps with less effort.
It offers excellent security measures.
Creates insightful reports.
Helps in connecting the business data to different teams. 


GeneXus is a software development tool that offers an intelligent platform for creating applications that enable the automatic development, and maintenance of systems. The applications created using GeneXus can be easily adapted to changes. Besides, it is used when the developer has to work with the newest programming languages.

Key Features:

GeneXus offers an AI-based automatic software approach.
It comes with the highest flexibility which means that it has the capability to support the largest number in the market.
Multi-experience apps can be created using this tool.
It has the best app security module.
It offers business process management support.
With GeneXus, developers get the highest level of deployment flexibility.


NetBeans is a very popular open-source and free software development tool. It is written in Java. Developers use NetBeans to create mobile, web, and desktop applications. This tool uses languages like C / C++, JavaScript, PHP, Java, and more.

Key Features:

With the help of NetBeans, a cross-platform system, developers can create apps that can be used on all different platforms like Mac, Linux, Solaris, Windows, etc.
Java apps can be easily created and updated using NetBeans 8 IDE, the newer edition for code analyzing.
NetBeans is a tool that offers the best features like writing bug-free code, Smart Code Editing, quick user interface development, and an easy management process.
NetBeans allows for the creation of well-organized code that eventually helps the app development team to understand the code structure easily. 


Eclipse is another popular IDE that is majorly used by Java developers. This tool is used to create apps that are not only written in Java but also in programming languages like PHP, ABAP, C, C++, C#, etc.

Key Features:

Eclipse, an open-source tool, plays an important role in the development of new and innovative solutions.
It is used by developers for creating desktop, web, and cloud IDEs.
Eclipse Software Development Kit (SDK) is open-source which means that developers can use it freely for creating any type of application with the help of any programming language.
Eclipse helps in code completion, refactoring, syntax checking, error debugging, rich client platform, industrial level of development, and more.
Integrating Eclipse with other frameworks like JUnit and TestNG is very easy.


Bootstrap is another open-source framework that is used by software development companies for creating responsive websites and mobile-first projects. For this tool, the developers can use technologies like HTML, CSS, and JS. It is widely used and is designed to make websites simpler. 

Key Features:

Bootstrap is a tool that offers built-in components that can be used in accumulating responsive websites.  by a smart drag and drop facility.
This open-source toolkit comes with various customization options.
It comes with some amazing features like a responsive grid system, pre-built components, plug-ins, sass variables & mixins, and more.
With Bootstrap, the developers get a guarantee of consistency,
Bootstrap, a front-end web framework is used by developers for quick modeling of the ideas.

Cloud 9

Cloud 9 was introduced in the year 2010 Cloud 9. At that time, it was an open-source, cloud-based IDE that supported different programming languages like Perl, C, Python, PHP, JavaScript, and more. But in the year 2016, AWS (Amazon Web Service) acquired this tool and it turned into a chargeable system. 

Key Features:

Cloud 9 IDE, a web-based platform is used by software development companies for scripting and debugging the app code in the cloud.
It comes with various features like code completion suggestions, file dragging debugging, and more.
With the use of Cloud 9, the developers can work with serverless applications.
Cloud 9 IDE is used by both web and mobile developers.
It enables one to create a replica of the entire software development environment.
Developers who use AWS Cloud 9 can share the environment with team members. 


Adobe Dreamweaver, an exclusive software programming editor is used to develop both simple and complex websites. It supports languages like CSS, HTML, XML, and JavaScript.

Key Features:

Dreamweaver is used in different operating systems like Windows, iOS, and Linux.
The latest version of this tool can be sued by the developers for creating responsive websites.
Dreamweaver CS6 offers a preview option that enables one to have a look at the designed website.
Dreamweaver CC, another version of this tool is a combination of a code editor and a design surface. It comes with features like code collapsing, auto-completion of code, real-time syntax checking, code inspection, and syntax highlighting.


Bitbucket, a web-based version control tool is used by the developers for collaboration between teams. It is utilized as a repository for the source code of projects.

Key Features:

Bitbucket is a powerful tool that comes with features like flexible deployment models, code collaboration on steroids, and unlimited private repositories.
With the use of Bitbucket, developers can organize the repositories into different projects.
Bitbucket supports a few services like issue tracking, code search, Git large file storage, integrations, bitbucket pipelines, smart mirroring, and more.


CodeLobster is another popular software development tool that is free to use and is a very convenient PHP IDE. Developers use it to create fully-featured web applications. This tool supports technologies like HTML, Smarty, JavaScript, Twig, and CSS.

Key Features:

This PHP Debugger facilitates the developers in debugging the systems easily at the time of coding.
CodeLobster PHP Edition makes the development process easy by supporting CMS like Magneto, Joomla, Drupal, and WordPress.
Some of its best features are PHP Debugger, CSS code inspector, PHP Advanced autocomplete, auto-completing of keywords, and  DOM elements.
This tool offers file explorer features and browser previews.


As seen in this blog, there are many different types of software development tools available in the market. And all of them are robust, fully-featured, and widely used. Here, we have listed some of the most popularly used development tools that are used by developers for creating unique solutions for their clients. The choice between these tools might be difficult at first, but if the developer properly studies the project and its requirements, choosing the right software developer tool can be really easy. And it can help in creating the finest project.

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Starting a Web App in 2022 [Research Results]

We are finally happy to share with you the results of the world’s first study on how developers start a web application in 2022. For this research, we wanted to do a deep dive into how engineers around the globe are starting web apps, how popular the use of low-code platforms and what tools are decisive in creating web applications.

To achieve this, we conducted a survey with 191 software engineers of all experience around the globe. We asked questions around the technology they use to start web applications.

Highlights of the key findings:

The usage of particular technologies in the creation of web apps is closely related to engineers’ experience. New technologies, such as no-code/low-code solutions, GraphQL, and non-relational databases, appeal to developers with less expertise;

Engineers with less experience are more likely to learn from online sources, whereas developers with more expertise in software development prefer to learn from more conventional sources such as books;

Retool and Bubble are the most popular no-code/low-code platforms;

React, Node.js, PostgreSQL, Amazon AWS, and Bootstrap are the most popular web application development stacks.

To read the full report, including additional insights, and full research methodology, visit this page

With Flatlogic you can create full-stack web applications literally in minutes. If you’re interested in trying Flatlogic solutions, sign up for free

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React Labs: What We’ve Been Working On – June 2022

React 18 was years in the making, and with it brought valuable lessons for the React team. Its release was the result of many years of research and exploring many paths. Some of those paths were successful; many more were dead-ends that led to new insights. One lesson we’ve learned is that it’s frustrating for the community to wait for new features without having insight into these paths that we’re exploring.

We typically have a number of projects being worked on at any time, ranging from the more experimental to the clearly defined. Looking ahead, we’d like to start regularly sharing more about what we’ve been working on with the community across these projects.

To set expectations, this is not a roadmap with clear timelines. Many of these projects are under active research and are difficult to put concrete ship dates on. They may possibly never even ship in their current iteration depending on what we learn. Instead, we want to share with you the problem spaces we’re actively thinking about, and what we’ve learned so far.

Server Components

We announced an experimental demo of React Server Components (RSC) in December 2020. Since then we’ve been finishing up its dependencies in React 18, and working on changes inspired by experimental feedback.

In particular, we’re abandoning the idea of having forked I/O libraries (eg react-fetch), and instead adopting an async/await model for better compatibility. This doesn’t technically block RSC’s release because you can also use routers for data fetching. Another change is that we’re also moving away from the file extension approach in favor of annotating boundaries.

We’re working together with Vercel and Shopify to unify bundler support for shared semantics in both Webpack and Vite. Before launch, we want to make sure that the semantics of RSCs are the same across the whole React ecosystem. This is the major blocker for reaching stable.

Asset Loading

Currently, assets like scripts, external styles, fonts, and images are typically preloaded and loaded using external systems. This can make it tricky to coordinate across new environments like streaming, server components, and more.
We’re looking at adding APIs to preload and load deduplicated external assets through React APIs that work in all React environments.

We’re also looking at having these support Suspense so you can have images, CSS, and fonts that block display until they’re loaded but don’t block streaming and concurrent rendering. This can help avoid “popcorning“ as the visuals pop and layout shifts.

Static Server Rendering Optimizations

Static Site Generation (SSG) and Incremental Static Regeneration (ISR) are great ways to get performance for cacheable pages, but we think we can add features to improve performance of dynamic Server Side Rendering (SSR) – especially when most but not all of the content is cacheable. We’re exploring ways to optimize server rendering utilizing compilation and static passes.

React Optimizing Compiler

We gave an early preview of React Forget at React Conf 2021. It’s a compiler that automatically generates the equivalent of useMemo and useCallback calls to minimize the cost of re-rendering, while retaining React’s programming model.

Recently, we finished a rewrite of the compiler to make it more reliable and capable. This new architecture allows us to analyze and memoize more complex patterns such as the use of local mutations, and opens up many new compile-time optimization opportunities beyond just being on par with memoization hooks.

We’re also working on a playground for exploring many aspects of the compiler. While the goal of the playground is to make development of the compiler easier, we think that it will make it easier to try it out and build intuition for what the compiler does. It reveals various insights into how it works under the hood, and live renders the compiler’s outputs as you type. This will be shipped together with the compiler when it’s released.


Today, if you want to hide and show a component, you have two options. One is to add or remove it from the tree completely. The problem with this approach is that the state of your UI is lost each time you unmount, including state stored in the DOM, like scroll position.

The other option is to keep the component mounted and toggle the appearance visually using CSS. This preserves the state of your UI, but it comes at a performance cost, because React must keep rendering the hidden component and all of its children whenever it receives new updates.

Offscreen introduces a third option: hide the UI visually, but deprioritize its content. The idea is similar in spirit to the content-visibility CSS property: when content is hidden, it doesn’t need to stay in sync with the rest of the UI. React can defer the rendering work until the rest of the app is idle, or until the content becomes visible again.

Offscreen is a low level capability that unlocks high level features. Similar to React’s other concurrent features like startTransition, in most cases you won’t interact with the Offscreen API directly, but instead via an opinionated framework to implement patterns like:

Instant transitions. Some routing frameworks already prefetch data to speed up subsequent navigations, like when hovering over a link. With Offscreen, they’ll also be able to prerender the next screen in the background.

Reusable state. Similarly, when navigating between routes or tabs, you can use Offscreen to preserve the state of the previous screen so you can switch back and pick up where you left off.

Virtualized list rendering. When displaying large lists of items, virtualized list frameworks will prerender more rows than are currently visible. You can use Offscreen to prerender the hidden rows at a lower priority than the visible items in the list.

Backgrounded content. We’re also exploring a related feature for deprioritizing content in the background without hiding it, like when displaying a modal overlay.

Transition Tracing

Currently, React has two profiling tools. The original Profiler shows an overview of all the commits in a profiling session. For each commit, it also shows all components that rendered and the amount of time it took for them to render. We also have a beta version of a Timeline Profiler introduced in React 18 that shows when components schedule updates and when React works on these updates. Both of these profilers help developers identify performance problems in their code.

We’ve realized that developers don’t find knowing about individual slow commits or components out of context that useful. It’s more useful to know about what actually causes the slow commits. And that developers want to be able to track specific interactions (eg a button click, an initial load, or a page navigation) to watch for performance regressions and to understand why an interaction was slow and how to fix it.

We previously tried to solve this issue by creating an Interaction Tracing API, but it had some fundamental design flaws that reduced the accuracy of tracking why an interaction was slow and sometimes resulted in interactions never ending. We ended up removing this API because of these issues.

We are working on a new version for the Interaction Tracing API (tentatively called Transition Tracing because it is initiated via startTransition) that solves these problems.

New React Docs

Last year, we announced the beta version of the new React documentation website. The new learning materials teach Hooks first and has new diagrams, illustrations, as well as many interactive examples and challenges. We took a break from that work to focus on the React 18 release, but now that React 18 is out, we’re actively working to finish and ship the new documentation.

We are currently writing a detailed section about effects, as we’ve heard that is one of the more challenging topics for both new and experienced React users. Synchronizing with Effects is the first published page in the series, and there are more to come in the following weeks. When we first started writing a detailed section about effects, we’ve realized that many common effect patterns can be simplified by adding a new primitive to React. We’ve shared some initial thoughts on that in the useEvent RFC. It is currently in early research, and we are still iterating on the idea. We appreciate the community’s comments on the RFC so far, as well as the feedback and contributions to the ongoing documentation rewrite. We’d specifically like to thank Harish Kumar for submitting and reviewing many improvements to the new website implementation.

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Next.js vs React: Which One to Choose for Your App?

The burning question today is What’s better: React or Next.js? Let’s have a look closely at both, compare them and see the difference between library and framework. In the React world, Next.js is one of the most popular frameworks for “hitting the ground running.”

What is Next.js?

Next.js is an open-source JavaScript framework for developing fast, lightweight, and easy-to-use web applications and static websites (one-pages) using React. Next.js was created by Vercel in 2016. Next.js requires Node.js and can be initialized using npm. There are a lot of reasons why Next.js has such a strong reputation in the world of application development. They are known for being reliable as they offer image optimization, internationalization, zero-config, Next.js analytics, hybrid: SSR and SGG, fast refresh, API routes, TypeScript support, file-system routing, code-splitting and bundling, incremental static regeneration, and built-in CSS support, etc.

Next.js includes all the features needed to build an application. Moreover, the documentation is excellent and it is becoming very popular among developers for frontend development.Here are the most popular platforms and apps of Next.js: Twitch.tv, TikTok, Hulu, Binance, and many others that involve a massive number of users engaging with complex data influxes.

What is React?

React is an efficient, declarative and flexible JavaScript library for building interactive UI, inspired by xHP, the HTML component library for PHP. React was created by Facebook in 2011 and then open-sourced in 2013.React is used to create dynamic, mobile apps, one-pages, visualization tools, and dashboards. Here are some of the most popular platforms and apps created with React: Facebook, Netflix, Atlassian, Airbnb, Dropbox, Reddit, etc.

Next.js vs React

Even in a sea of JavaScript frameworks and libraries, React and NextJS stand out. React is the most popular JavaScript library for frontend developers. NextJS, although smaller than React, has grown continuously over the years and is well on its way to becoming the most-used JavaScript framework. So, let’s compare React and Next.js. React – is a JavaScript library for building UI. Next.js – is the React framework. NextJS is used on top of React, extending its features and optimizing the development process: React doesn’t even have to work with NextJS, but NextJS uses React to deploy applications.

React has a special framework – Create React App, an application used to create React projects and includes tools such as Babel, Webpack, and ESlint. Next.js is a React-based framework that allows you to build applications with server-side rendering. React is still the core of the application, but the structure and navigation mechanisms (architecture) – are defined by Next.js. The difference between a framework and a library is that a framework has more features and focuses on several aspects of development, and gives you rules and guidelines for writing code and structuring files.

React & Create React App (CPA)

Server-Side Rendering (SSR)
Supports different types of SSR.
– Static generation: obtaining data at build time. Best suited for use cases such as blogs or static websites.
– Server-side rendering: sampling data and rendering for each request. May be needed when you need to serve different views to different users.
Doesn’t support SSR out of the box. 
However, you can still set it up. 
It just takes more effort to configure SSR with your preferred server and configuration.

Almost everything is configurable
If you check the example NextJs templates, you can see files like 
babelrc, jest.config, eslintrc etc. that you can configure.
Doesn’t leave you much space to configure it. 
Configurations, such as webpack config, cannot be changed unless you do not deviate from the usual CRA path (eject, rescripts, rewired, craco). 
You should use what is configured in 
react-scripts, which is the core of CRA.

Well maintained. Release regular updates.
Very sensitive. 
If you keep up with updates of CRA releases, it is not difficult to maintain.


Supports typescript out of the box. 
Configurations for TypeScript:
touch tsconfig.json

Supports. You can initialize the CRA app with typescript like this:
npx create-react-app my-app —template typescript

Hooks Support

Redux Support

Incredibly fast apps thanks to static sites and server-side rendering.
Decent, but the lack of code splitting results in poor performance.

Tiny, but friendly
Both huge and friendly

Support static exports, and pre-rendering, and has a lot of features, for example, automatic building size optimization, fast development compilation and preview mode.
Easily extensible, can include routing as well as management patterns with libraries.

Talent pool

Easy to learn

Development costs

Which one is better?

It’s hard to say that one is better than the other. Remember, React.js is a JS library – a set of tools you can use to build UI – and Next.js is a framework – the blueprints and rules you need to build an entire app – based on React so it’s not a pick this one instead of the other situation.

Use React when:

You need a highly dynamic routing
You’re already familiar with JSX
You need offline support

Use Next.js when:

You need an all-inclusive framework
You require backend API endpoints
You need server-side rendering

What do React vs Next.js projects look like


You can get started with React by installing Node.js on your machine and running npx create-react-app react-app. This will create a basic project structure with the src/App.js file as the entry point for the application. You’ll also have a public folder where you can store assets, and the initial scaffold looks like this:


With Next.js, you can get started by running npx create-next-app next-app. This will scaffold out a project that already has a pages folder for the pages or routes and a public directory that hosts your assets. The initial scaffold looks like this:

The files in the pages directory relate to routes in your application. The public directory stores your static files or images that you want to serve and can be directly accessed – no need to use require or other React traditional methods to import images into components.

Building Next.js and React projects with Flatlogic

The Flatlogic platform is a great way to bridge the gap between developing your applications. Applications usually use the same elements and components, when using the same technologies. The main thing that distinguishes them on a technical level is the database schema, which implements different data processing and storage mechanisms. The Flatlogic Platform allows you to create applications by combining parts and creating only those that need to be unique. Here you can see how to use the Flatlogic Platform to create Next.js, React applications, and other options for creating CRUD applications on the React. To generate your Next.js or React application, tap here and let’s go.

Step 1

Name your project and choose the tech stack: React as frontend and No-backend as backend.

Step 2

Choose the Starter Kit. Here you need to decide which starter kit is best for your project: Next.js or Create React App.

Next, you need to connect your GitHub repository and check the stack and starter kit and Finish the creation process.

Then you will be redirected to the project settings where you will need to deploy your application.


React and Next.js are new and useful tools for your project, but only for certain tasks. When you choose Next.js, it offers the best solutions for server-side rendering and static website development. It also makes it easy to manage projects with a variety of tools and features.

On the other hand, React is the best choice for developing UIs for one-page applications. Being mobile and web-enabled, it works with a layer of mobile and web applications to create more appealing and intuitive ones. In a nutshell, Next.js offers various tools and features to minimize the development process while React has better resources for the frontend development of your mobile and web applications.

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React vs Vue: What to Choose in 2022?

React and Vue are the two most popular and widely used JavaScript frontend frameworks today. These frameworks allow developers to create a wide variety of web applications with JavaScript. Choosing between the two can be confusing if you are building a new application. Each has its own use case and serves different business needs. 

Most web developers prefer to use Vue.js and React frameworks. Using React or Vue.js provides a quality approach to web development that is consistent and performant. Both Vue and React have their own best scripts and meet different kinds of business needs.

In this article, we will learn about each and explore which is best suited for your business.

What is React?

React.js combines a high degree of concentration with simplicity when it comes to user experience. It was developed by Facebook as an open-source JavaScript library to help developers build user interfaces. React follows a declarative programming style and a component-based approach. It allows for the creation of complex web applications with the highest flexibility and speed. 

React.js is the most used web framework in 2021 with 40.14% of the software developers globally using React. 

React holds the top spot for the fourth year. Popular companies that are currently using React.js are AirBnB, Netflix, Instagram, and Udemy.

Source: https://2021.stateofjs.com/en-US/libraries/front-end-frameworks

What is Vue.js?

Vue.js is a progressive JavaScript framework that developers use to build interfaces. Unlike the Angular framework, Vue is incrementally adaptable by design. It scales easily between a library and a fully-featured framework. Companies using Vue.js include GitLab, Behance, Upwork, and Grammarly.

Here are a few Vue.js statistics

Vue.js is used by 0.8% of all the websites amongst JavaScript libraries.
Vue.js is used by 1.2% of all the websites amongst the JavaScript library we know and that rank in the top 1,000,000.
In 2021, 42% of the developers admitted that they used Vue in their company. 55% of the developers are of the view that Vue will get more popular in their organization. 

What are the Similarities between React and Vue?

Before looking at the differences between the two, let us look at some similarities. Both React and Vue possess component-based architecture. Also, both the frameworks are lightweight and have an exposure to lifecycle methods. Their performance is fairly similar. Both technologies work with any existing web application, even if it’s not a Single Page Application.

Below are some similarities:

They have composable and reactive view components
They use virtual DOM
JavaScript Code and PWA support
They concentrate on a core library. They also involve handling of routing and global state management with companion libraries
Possibility of working with any existing web applications
Both React and Vue have large proactive communities and plenty of libraries and tools available.

Comparison between React and Vue

You can pick one after knowing how they differ. The most obvious distinction: React is a library, whereas Vue is a framework.

Let us compare them taking into account some essential parameters. 

Data Binding 

Vue uses two-way data binding. It means whenever you change any interface element, your model state automatically changes with it. It may look simple to read about it. However, for a large project, it is not. If you want a good data overview and want easy debugging, you may prefer React as it has one-way data binding. After the model state is updated, React then renders the change in the interface element.

Desktop and Mobile Development 

React Native is the perfect platform for you if you want to build mobile apps that work on Android or iOS. Developers can use their existing skills to get started. The biggest advantage of using React is that you can reuse 99% of code between Android and iOS.

On the other hand, Vue does not have a dedicated platform like React Native. However, it does not mean you cannot develop native applications. Vue developers can use a cross-platform UI framework or plugins and write Vue applications and compile them into native apps – iOS and Android.

Major Differentiator – Syntax

One of the major differences between them is the way the view layer is built. Vue uses HTML templates by default, and there is no option to write in JavaScript Expressions (JSX). On the other hand, React solely uses JSX. Vue is easier to use even for beginner frontend developers as it uses only HTML. React’s JavaScript Expressions combine CSS and HTML into JavaScript. The XML-like syntax allows developers to create self-contained UI components with view-rendering instructions included.


React provides third-party tools to help developers create React apps, and it allows them to speed up app development by adding scripts. Earlier, React developers had to copy the files from previous apps and configure everything from zero. It was not only time-consuming but a boring task that no developer liked doing. 

Vue, on the other hand, uses a tool called Vue CLI. It enables the user to create any project quickly. It comes with many benefits like easy improvements, adding plugins anytime during the project, etc.


It is not easy to pick a winner on popularity, but if you check online communities, React wins this battle. It is mainly because it is backed by Facebook. The Vue community is smaller compared to React with fewer packages and resources. However, it is maintained by the creator himself – Evan You and his team.

Template and Styling

The UI library is incomplete without templates and styles. Additionally, they’re the places where the difference between React and Vue is most apparent, since the code design is different. The approaches both Vue and React take are also quite different.

While Vue has a more declarative approach, React is more functional. Since the logic and markup are both considered as one, they are mixed. It is accomplished with JSX, which is an abstraction of React.createElement, which is used to create Virtual DOM entities. 

Templates and styles are treated separately with Vue – a more conservative approach. Here, the templates are viewed as old-fashioned HTML elements.


If you want to make the decision of choosing anyone between Vue and React based on performance, you will not be able to since both frameworks have remarkable speed. However, as mentioned at the start, you can check for specific use cases to decide which is a better option for you.

Learn more about Vue.js Best Use Cases 

Vue has to be integrated into an existing project incrementally. This means there is a per project requirement since it is a progressive framework. For example, you can use Vue.js as a lightweight library to add some interactivity to a web app. Ease of integration is one of Vue’s top assets. 

You can hire Vue js developer to easily implement Vue.js into a project – it is a lot faster. The learning curve is easy, and tools like Vue UI and CLI make it a great tool to use for quick MVP delivery and start-up ideas. It is a cost-effective solution for all small to medium applications. However, it does not mean it is not good for large web apps. It has a vast ecosystem of tools and companion libraries, allowing the framework to respond to the complex needs of enterprise-grade applications.

Learn more about React.js Best Use Cases 

React was initially created for large-scale web projects, and hence using it for a small and simple app would not justify its usage. Setting up a project using React.js is not easy, and you need some level of expertise to do it, but its architecture ultimately pays off in the long run.

JSX is powerful, and it gives developers a range of powers – flow controls and advanced IDE features such as auto-completion or listing are included in the component view templates. React does not have official packages for routing or state management like Vue. If you are developing complex apps, you have to use third-party solutions in many places. It gives a range of choices to developers. If you have experienced developers, they will know better which libraries are optimum and can be used to meet the business demands of a particular web application.

Choose a framework/library according to your needs

React is a library, and hence the users gain more control, such as the manual re-rendering control. The library heavily utilizes functional programming principles, which is evident in how it handles state and communicates between components. On the contrary, Vue is a framework that brings to the table many more built-in features and companion libraries which come from the core team. Hence, this helps in making the development experience smoother for the user.

Both Vue and React are great tools for building interactive user interfaces. You cannot pick one randomly, you will need to take into account many factors like your business needs, developers’ experience, budget, timeframe to deliver the project, and much more. Performance-wise both are at par, and you won’t be disappointed in this aspect whether you choose React or Vue.

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