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What is Regression Testing?

Posted on November 7, 2022May 17, 2023 by vallish

Delivering a high-performance and bug-free software product every time is the ultimate goal of any development team. Writing code is a part of the overall development. Another aspect is QA testing, which involves accessing the outcome for all possible use cases, which is tough. That’s why software engineering includes testing as one of the building blocks of quality product development.

Most development teams often choose regression testing, given its impactful test cases and approach. According to market reports, software testing was a USD 40 billion market in 2021 and is poised to grow at a CAGR of over 6% between 2022 and 2030.

This article details everything there is to know about regression testing and its contribution to developing high-quality software and applications.

What is regression testing?

Regression testing is a software testing practice that checks an application regressively. Simply put, in regression testing, all recent code changes are successfully integrated, and the end functionality of the product is not affected. But it also reruns all the previously passed test cases to ensure that the full product functions as a single unit.

Most projects run regression tests after code changes, updates, and improvements. Regression testing ensures a product remains sustainable under continuous updates and improvements. If the overall stability and functionality of the existing product features are affected with each update or other testing methods are not successful in catching bugs before the product reaches the end users, regression testing can guarantee bug-free updates.

Verifying the product behavior as a whole, regression testing evaluates and tests dependencies, defects, and malfunctions to proactively mitigate risks. Make regression testing the final step before product deployment to ensure that your code remains operational even after new changes.

When should you apply regression testing?

While it is a highly comprehensive form of QA testing, not every product update requires regression testing as it is pretty resource-intensive. Some typical circumstances to implement regression testing over other QA testing types is:

A new feature or function is added to the existing code base
A current feature is updated with a new requirement
The code base is optimized for performance
Reported bugs or malfunctions are fixed
Patch fixes are added to the product
Any changes are made in the configuration

Also, regression testing in such cases can quickly uncover any old bugs that can regenerate due to any recent changes or improper software version control.

Benefits of regression testing

As software development has become more agile, regression testing brings the same flexibility and speed to QA testing. It is instrumental in testing the code on the go without slowing down the development speed. There are several reasons to include regression testing in your sprint at each phase.

It results in top-notch quality due to regular and thorough testing.
Ensures that no old bugs are regenerated due to recent code changes.
Keeps the software extremely stable after every update.
Ensures that the product remains sustainable due to early bud detection.
Results in faster debugging as it is easy to identify the root cause of a bug.
Developers get feedback faster after every code update.
Several automation regression tools are available that speed up QA testing.

Challenges of regression testing

For the most part, regression testing is carried out manually. While it can be efficient when handling a small-scale project, QA engineers can run into several challenges for large projects.

Usually, as the software evolves, it expands to include hundreds of features and functions. This consequently increases the number of test cases required to continuously enhance the quality of the code and keep the product sustainable. There will come a point when running regression testing proficiently won’t be possible with available resources as it becomes:

1. Time-consuming

Manual regression testing can take days and, at times, months if the project is huge. It can significantly delay the deployment time.

2. More staff

To accommodate the resource requirement of increasing regression test cases, you will have to hire more QA engineers in your team to successfully conduct manual testing before the deadline.

3. Increased cost

More time and increased staff add to the project cost. This can often lead to reduced project scope forcing you to deliver products with less features or incomplete tasks.

4. Accuracy issues

When too many manual testers work on regression testing, there is a high risk of human errors. Also, regression testing is relatively monotonous, which makes it even harder for QA engineers to focus.

Conclusion

Regression testing is key to improving product quality while keeping the deployment time low. Selenium, Katalon Studio, and IBM Rational Function Tester are some great regression tools to automate the QA testing process. These tools apply regression testing effectively to identify all surfacing defects early in the pipeline and eliminate them to improve user experience.

Talent500 is a remote team-building platform where QA engineers can explore career-redefining job opportunities with Fortune 500 companies and fast-growing startups. Sign up here to join our elite pool of talent.

Top 5 Python backend frameworks you must know

Posted on November 4, 2022November 4, 2022 by shreena morjaria

As we all know, Python is a language that makes coding a fun part of life. You might have read that Python is an absolute beginner-friendly language. Second cool feature is that it is completely an Open-Source language. I love to read code of some open source projects. If you want to know how such a wonderful language is built, go to GitHub and read the code.

In this article, we will discuss the most widely used Python backend libraries. So, without further ado, let us get started.

Fun Fact 1: Python is an ocean of libraries.

Yes, Python has excellent libraries that you can use in your software development journey. Python has a great community that constantly develops libraries and makes changes to them. Libraries with a specific focus are also available like nltk for natural language processing, scikit-learn for machine learning applications, or Django for backend applications.

Let’s discuss the top 5 Python frameworks to go for.

Fun Fact 2: Being open-source is awesome. Python libraries listed in this article are completely open-source. It means you can go through their code on GitHub to understand how these libraries are built!

Django

Django is truly the best Python library for development. It’s written purely in Python and it’s fast and secure. You can create web applications within a matter of a few hours. It’s fully loaded with all the required tools to create websites.

Django follows the Model View Template Architecture. Django is based on the DRY (Don’t Repeat Yourself) Principle. It’s always better to follow modular design patterns to develop scalable applications.

The Django framework officially supports four databases, namely PostgreSQL, MySQL, SQLite, and Oracle. YouTube, DropBox, Instagram, and Pinterest all these tech giants rely on Django. Django is mainly known to reuse lots of common code and design patterns.

Django has also improved with each release with new functionalities and bug fixes.

GitHub : https://github.com/django/django

Django GitHub page has earned 66.4k stars till now and has 2,272 contributors. Read the official document of Django here.

Scrapy

Scrapy is a free and open-source web crawling framework written in Python. It was originally designed to perform web scraping but can also be used for extracting the data using APIs.

Scrapy can handle bigger tasks with ease when it comes to downloading webpages or scraping groups of URLs within a minute. Scrapy supports concurrency as it is asynchronous so it can load multiple websites in parallel. It supports different data formats such as CSV, JSON, and XML.

GitHub : https://github.com/scrapy/scrapy

An open-source project with 44.6k stars and is used by 31.2k+ users. Check out the Scrapy homepage for more information.

Flask

Flask is a lightweight web application framework written in Python that provides different tools that make creating web apps easier. Flask is known as the WGI framework which stands for Web Server Gateway Interface. This is a way for web servers to pass requests to web applications or frameworks. Flask has strong community support from collaborators and learners. And it is open-source as well.

Can you imagine that you can build a website using a single Python file? It is as simple as that! You just need to learn how to use their tools to make web apps quickly.

GitHub: https://github.com/pallets/flask

A cool open-source library with 60.6k stars and strong support of 1.2M+ users, 681+ contributors trying to make it more usable. To explore their documentation, click here.

Web2Py

Web2Py is a scalable, open-source, and full-stack Python framework. Web2Py comes with its own web-based IDE. Other things include a separate debugger, and one-click deployment. It has the ability to run on different platforms, i.e it’s a cross-platform framework.

It also provides us with data security that prevents vulnerabilities like cross-site scripting, injection flaws, and malicious file execution on devices. Web2Py has its own Error Tracking System. It gives us Role Based Access Control.

GitHub: https://github.com/web2py/web2py

Visit this link to check out Web2Py documentation.

CherryPy

CherryPy is an open-source minimalist web framework that makes Python web applications no different than building any other object-oriented program. CherryPy can handle sessions, cookies, file uploads, statics, or any other typical web app feature. It also supports cross-platform functionality.

CherryPy can run multiple HTTP web servers at a time. It consists of tools for authentication, sessions, caching, and encoding. It has built-in support for coverage and testing.

GitHub: https://github.com/cherrypy/cherrypy

Follow this link to get started with CherryPy.

So these are some powerful and widely used Python Frameworks by Web Developers to make their life easy.

Fun Fact 3: Wanna be an open-source contributor by helping the community? Yes, you can!

As I mentioned, all these libraries are open-source. You can read the code and if you want to contribute to the above libraries, make sure to join their communication channels and go through their READ MEs. Happy learning!

Full Stack v/s specialized developer 2022: The ultimate guide

Posted on November 4, 2022November 4, 2022 by vallish

Increasing digital transformation needs are prompting businesses to look for developers. The market offers two options – hire a ‘jack of all’ full stack developer or go with specialist developers.

Developers who dabble with different programming skills seem to be better. At the outset, they are multitalented, allow businesses to maintain low head, and reduce overhead expenses. So, it’s evident they are better. But are they? Well, not always.

As in any other industry, the mismatch between talent and the role is also common in software development. The struggle between the choices of hiring full stack and specialized developers is real.

This article explores the solution to this dilemma, but first, let’s find out the job profiles of a full stack developer and a specialized developer.

Full stack developer vs. specialist developer

Full stack developers are professionals who have both frontend and backend development skills. Such developers are familiar with multiple technologies and can undertake a project alone. Usually, they possess a broad skill set of project planning, software testing, and software development lifecycle to make crucial development decisions independently.

The common skillset of a full stack developer includes HTML/CSS, JavaScript, database management, and Git for frontend development. They use PHP, Python, Java, or Ruby for backend development. Among other skills, they know the basics of graphics designing, NPM, and essential software developer soft skills.

Speaking of the current market demand, full stack developers are hot in demand. According to the Bureau of Labor Statistics, the demand for full stack developers will increase from 1.35 lakhs to 8.53 lakhs by 2024.

The specialized developers are skilled in either frontend or backend technologies. The frontend developers handle the visual aspect of an application, like the development of web pages, UX/UI interfaces, mobile app designs, and other interactive layers of an app. The backend development takes care of all the server-side development.

Frontend development involves the visual part of the application, user interaction, etc. The backend development consists of the server side of the application. It also involves the management of server resources and databases. The skill set of specialized developers makes them the best for the particular job. This is why technical developers can build a project securely with robust features, as they have in-depth knowledge of the technology they work with. As for the job market, the demand for specialized developers is consistent across frontend and backend domains.

Factors to consider when making the choice

A specialized or full stack developer will bring different skill sets, benefits, and challenges to the overall development project. To choose between the two, you need to factor in these considerations.

1. Quality of work

Specialized developers can undisputedly deliver better quality as they are more proficient with the technology they work with. Multiple specialist developers working on a project might drive up the cost, but the result will be superior in quality. Furthermore, while familiar with most technologies, a full stack developer might not be updated with the latest trends and technologies. Developers focusing on a specialized area keep their skills updated to deliver the best product quality.

2. Development time

As full stack developers can easily switch between tasks and handle client-side and server-side simultaneously, they can deliver products faster. A full stack developer always has a clear overview of the scope of a project as they work more independently than specialized developers. The high dependency of specialized developers on other developers makes them slower. Unless all the specialized developers in the team complete the assigned task, they cannot go ahead independently.

3. Project cost

As full stack developers can handle a project’s frontend and backend developer aspects, they offer price economy. Businesses prefer full stack developers as they are versed in all aspects of development. It requires fewer developers in a team, and there is no overhead to maintain. If a company can manage the quality of the end product to keep the cost of development low, a full stack developer is a suitable choice. Hiring specialized developers is expensive because you need to manage larger teams.

4. Availability

Hiring full stack developers is challenging compared to specialized developers. It takes much time to attain full stack development skills, hence talent is scarce. In comparison, finding frontend and backend developers is easier. Another reason that companies must keep in mind is that hiring full stack developers makes them highly dependent on them. If a full stack developer quits, it will be challenging to keep the project going as they usually handle most of the project alone.

Conclusion

While expertise offers certain advantages, a skillful full stack developer can handle several tasks to fix issues faster and bring down development costs and timelines. The final decision to hire the best talent must depend on the project requirements and the expected outcome.

Talent500 is the remote team-building platform global tech companies use to hire specialized and full stack developers. To know more, sign up here.

Manual testing guide: Types, myths & tools

Posted on October 31, 2022November 8, 2022 by supriya

Software development is more than just writing lines of code. There’s planning, strategizing, development, DevOps, and at the final stage, testing. Two approaches can be taken for software testing – manual and automated.

In most projects, you must skillfully combine manual and automated testing for the most comprehensive testing. However, both approaches are not entirely interchangeable and have significant differences.

This article covers only manual testing in detail; you can read more about automation testing here. Let’s start from the beginning.

What is manual testing?

Manual testing is an approach that evaluates the performance of software and examines its features from the end-user perspective to assess its quality. It is much more intensive QA testing as it involves human testers closely monitoring the code quality, identifying existing defects, and anticipating any possible weaknesses.

The profound impact of manual testing results from its four-stage process.

A manual testing project starts with a requirement study that involves testers getting acquainted with quality assurance requirements. QA engineers understand the software’s needs, objectives, operating principles, main features and functions, and possible vulnerabilities at this stage.

The next step is to design test cases to examine all possible use cases of the software. Testers can use Excel sheets to document their test cases, but if the project is large, it is not the most efficient way. There are efficient case management tools for manual testers like Kissflow, Appian, and Alfresco that can help them document test cases and automate the workflow.

Testing and bug reporting follow the test case design. Manual testers create the environment to live test the software. They continuously log bugs that they come across using a bug-tracking system.

The final step is the analysis of manual testing results. Here, the aim is to generate valuable metrics to help developers fix defects and bugs.

When to use manual testing?

Some people believe that test automation is a replacement for manual testers. QA engineers understand that manual testing is indispensable for several reasons.

1. Certain tests can only be successful manually

For instance, user experience testing during quality assurance cannot be complete without manual testing. You can automate the UX/UI testing workflow, but only a human can adequately perform tasks like an end user. Compatibility and interactions are essential for mobile devices, and any app, if deployed without manual testing, risks being buggy.

2. Large software almost always has hidden bugs

Automated testing might be able to discover most bugs testing a small-scale software product, but when things go to the enterprise level, it is impossible to detect hidden bugs. Machines can be programmed to test for specific bugs. But manual testers run into hidden bugs during their routine tasks. Automation testing can never detect such defects.

3. When budget is a constraint

Automated testing can be expensive. If your budget is low, you do not want to spend your initial investments on testing the product before it can hit the market. Due to its low maintenance expenses, manual testing is a more suitable option here.

4. Automation often falls behind sprints

Maintaining automated test cases is no easy job. You have to update the scripts for performance and validity continuously. It is difficult for automated testing to keep up with frequent software update releases. Manual testing can manage to be in sync with sprints.

5. Repetition and variability

Any effective QA testing will have two characteristics – repetition and variability. Automated testing is excellent for repetitive testing processes, but that’s not enough. Without manual testing, you cannot introduce variability in the process. This is why automation is often combined with manual testing.

Common myths surrounding manual testing

As a manual tester, you might often hear these myths:

Manual testing is easy: If you think anyone can do manual testing, then you are misinformed. It requires skills and a certain mindset to be a successful manual tester.
Automation will replace manual testing: Again, it’s impossible to simulate human interaction with the software entirely. Manual testers will always remain relevant.
QA engineers are only involved in the post-development stage: Companies with successful products know that testers must work simultaneously with developers to catch bugs early on. Including testers in the final step is a risky venture.

Best tools for manual testing

Depending on the stage at which you are conducting the manual testing, you can pick from these tools:

For case management: Kissflow, Appian, Alfresco

For bug tracking: Jira, Bugzilla, Trello

For cross-browser testing: BrowserStack, Sauce Labs

Screen capture tools: Loom, Skitch, CloudApp

Conclusion

Manual testing remains an essential aspect of successful software products. As software becomes more user-oriented, manual testers are becoming increasingly important in guiding the efficiency of automated testing.

Talent500 is the remote team-building platform for Fortune 500 companies and fast-growing startups to build their tech teams. To join our elite pool of talent, sign up here.

Best web automation tools for QA in 2022

Posted on October 31, 2022November 8, 2022 by supriya

Web automation tools are great for the quality testing of web and mobile products. While manual testing is more rigorous and profound, it’s not always a cost-effective solution for testing applications. That is why development teams widely adopt QA automation which allows them to create low-code or scriptless test cases. Another benefit of automation is that it helps facilitate continuous testing throughout the software development life-cycle, which results in faster and leaner development. QA tools are essential for any software development team.

For this article, our expert QA testers handpicked the best web automation tools for quality assurance in 2022.

1. BrowserStack

BrowserStack is a cloud-based QA automation tool that allows QA engineers to conduct comprehensive web and mobile testing. Many teams use this tool to verify the cross-browser compatibility of their applications to ensure they offer a unified user experience. You can test your applications’ functionality, performance, and visual appeal using BrowserStack.

To further its implementation, the developers of this tool offer Percy, an automated visual testing product that developers can integrate into their CI/CD pipelines. Visual testing in modern application development is essential as it allows QA engineers to identify visual regressions before impacting end users. With Percy, you can capture screenshots and make a pixel-by-pixel comparison of the application’s UI baseline to recognize any defects quickly.

Development teams can integrate BrowserStack with some of the most popular automation frameworks like Espresso and Appium. It also connects to popular CI/CD tools like Bamboo and Jenkins.

2. testRigor

testRigor is a QA automation tool for functional UI regression and exploratory testing. One of the most robust QA tools, it relies on artificial intelligence to autonomously generate test cases by predicting end-user behavior. If you are looking for a low-maintenance testing tool, testRigor offers unmatched efficiency. QA engineers can write test cases in plain English, reducing testing phase time significantly. It is also available as the Chrome browser extension to record your tests. You can register yourself using an application, and this QA automation tool will generate a test case based on the recording. There is no coding or dependency on XPath, so the test cases are highly stable and easy to maintain. You can integrate testRigor with TestRail, Git, Azure DevOps, and Jenkins.

3. Autify

Another AI-powered QA automation tool, Autify, supports no-code testing of web and mobile applications. The tool lets QA engineers create test scenarios by emulating the use of the application as a user. Due to its no-code, intuitive UI, some of the largest tech companies like Unity, DeNA, Radar, and Globis use Autify for testing their applications.

Autify offers parallel execution, visual regression testing, and Shadow DOM elements. One of this testing tool’s unique features is that it can run up to 10 cross-browser tests simultaneously. The AI used in this tool automatically monitors the app UI for any changes, and if detected, the test scenarios are accordingly updated. As for integration, your team can use Autify with Jenkins, Webhook, TestRail, CircleCI, and Slack.

4. Katalon Studio

Katalan Studio is a comprehensive QA automation testing tool that you can use for APIs, web interfaces, mobile platforms, and Windows desktop applications. It is primarily designed as a functional testing tool for QA engineers to create and reuse automated test scenarios for UI elements like iFrames and pop-ups without coding. One of the distinguishing features of Katalan Studio is that it is available for any stakeholder in your team, irrespective of their programming skills. Its dual editor interface allows non-technical users to create test scenarios using the straightforward UI that does not require coding. While for advanced programmers, there is another interface to write test scripts with syntax highlighting, debugging, and code suggestions.

You can use this QA automation tool on any operating system, including Windows, Linux, and Mac OS. It is also compatible with the most commonly used continuous testing tools like Git, Jenkins, Jira, and Docker integrations.

5. IBM Rational Functional Tester (RFT)

The association with a well-known brand is not the only USP of IBM Rational Functional Tester. It is a web automation tool written purely in Java and is one of the best QA automation tools for teams that develop their products in Java, .Net, or C#.

IBM Rational Functional Tester is a serious testing tool that allows you to write test cases for manual and automated testing. It is a quality assurance tool that you can use for enterprise applications. Like other QA automation tools on the list, it also allows test visualization and editing using rendered screenshots. It provides intelligent script maintenance capabilities to enable straightforward functional and regression testing automation. Development teams can use this QA testing tool with other platforms like Jenkins, Apache Maven, and QuickTest.

Conclusion

As important as the code quality of an application is, quality assurance is no less. We hope the QA automation tools listed here will help you create comprehensive testing scenarios to launch high-quality products.

Talent 500 is the platform for QA engineers to explore career-redefining opportunities with Fortune 500 companies and fast-growing start-ups. Sign up here to know more.

Essential backend developer skills to learn for 2023

Posted on October 31, 2022November 8, 2022 by harsha satyakam

Each development project has two components – frontend and backend. Mostly, end users interact with the front end, but backend infrastructure and components go unappreciated. However, in the software development domain, backend developers are generally better paid. It is because of the complex solutions they build. To be successful as a backend developer, you need to acquire the essential backend development skills that are more relevant today like native-cloud development, and low-code application architecture.

This article will list the essential backend developer skills that will be valuable in 2023.

Let’s get started.

1. Pick the correct backend programming language

The web development domain is highly dynamic, with new technology introduced frequently. But, instead of getting distracted by the noise, focus on the programming languages with a solid presence. Java, Python, and PHP are three programming languages preferred for backend development.

Java is most widely used for building robust systems. It is a strictly typed programming language that’s scalable and general-purpose. A backend developer must be skilled in Java to build a solid infrastructure. As for PHP, it is the most commonly used server-side scripting language. As an open-source language, it is one of the most cost-effective technologies that most companies want to use for their projects. Python is another essential programming language for backend developers. It is a premier programming language that is simple to learn but robust enough for any complex project.

2. Knowledge of databases

As proficient as you have to be with the programming languages for backend development, you also need strong database skills. A database is critical for storing data and content in any web application. Your ability to recover, arrange, alter and store information efficiently defines the performance of your application. Any backend developer should have extensive knowledge of various DBMS technologies.

In 2022, the two most commonly used databases are SQL and NoSQL. The SQL database is one in which all data is mapped on a table, and each entry in the table is linked to another entry through recursive methods. While SQL databases are suitable for storing queries and producing results, speed becomes a problem when creating highly scalable applications. The NoSQL database offers the solution as there is no need to structure the data beforehand. Such databases work on JSON (JavaScript Object Notation) and XML (Extensible Markup Language) and are extremely fast.

3. Knowledge of APIs

API stands for Application Programming Interface. It is a medium through which software can communicate and share data. In modern web applications, API plays a crucial role in driving mass adoption as the application can be used for multiple purposes.

Simply put, API is a set of rules and definitions that software, clients, and services follow to communicate with each other over the Internet. The server and client are the two receiving ends, while the API connects them both. For a backend developer, it is essential to understand the HTTP methods of API development. The most commonly used API commands are:

GET: to retrieve a resource
DELETE: to delete a resource
PUT: to replace an existing resource
POST: to create a new resource

Most employers expect backend developers to have extensive knowledge of APIs. To connect your backend with other services, you should know how to create an API in various programming languages such as Python, NodeJS, PHP, etc. JSON and XML are the two most recognized formats of API information generation.

4. Server infrastructure knowledge

Server infrastructure is changing day by day. Today, cloud-native servers are a standard, and businesses no longer want to store and operate their applications through a central location. Since backend developers are working behind the scenes, they must understand various web servers and their technologies. The most commonly used servers are Apache, Nginx, IIS servers, and Microsoft IIS.

Some of the essential backend skills expected from a developer include the ability to manage a hosting environment. As a backend developer, you must be able to scale the applications according to changing demands and configure servers to handle the increased traffic. Accessibility and security compliance related to servers is also essential information. You should know version control among other technologies, such as Git and GitHub.

Another essential skill here is to familiarize yourself with cloud server infrastructures such as AWS, Google Cloud Platform, and Heroku. These leaders in the cloud server market are often used to deploy container-based services such as IaaS (Infrastructure as a Service), PaaS, and SaaS (Software as a Service).

Conclusion

Backend developers are in hot demand. To land your dream job in 2022, you must master the above-listed backend skills. Apart from these, you should also have strong computer science skills, such as data structures, algorithms, and programming patterns.

Talent 500 is a global remote team-building platform that Fortune 500 companies and fast-growing start-ups use to recruit backend developers. Join our elite talent pool to get discovered by some of the largest global companies. Sign up here.

Top full stack development trends to follow in 2023

Posted on October 20, 2022November 9, 2022 by vallish

Software development is becoming an indispensable component of business success. Enterprises are exploring new technologies as they emerge, especially in web and mobile development domains. These are highly dynamic genres where changes occur rapidly.

Full stack development is a crucial area of software development. As companies want more versatile developers who can work on complete technology stacks rather than a specific technology, keeping a tab on the latest full stack development trends as they occur is essential.

This article lists the most recent full stack trends in the industry.

1. Low-code development

Full stack development is witnessing the use of less code to build applications. This is called low-code development, and companies prefer low-volume code as it’s more maintainable. Also, full stack developers can deliver products faster with minimal coding. Gartner predicts that by the end of 2021, the low-code development market will grow by 23%, and low-code app platforms will account for over 65% of development by 2024.

As a full stack developer, you must focus on involving a more visual approach to software development. WordPress is one example of a low-code platform developers use to deploy large-scale applications with all the available functionalities.

2. Artificial Intelligence

Artificial intelligence has been the buzzword in the industry for a long time. Today, AI is implemented in all mobile and web applications, such as chatbots, code editors, shopping, OTT platforms, and more. Businesses use AI to automate many business processes, boosting productivity and revenue.

Full stack developers are preferred for AI app developers because they are qualified to understand the needs of such projects as user expectancy, design requirements, and knowledge engineering. These developers take an all-round approach towards the AI project, so they are most suitable for the role. Artificial intelligence is an emerging trend in healthcare, retail, eCommerce, logistics, and transportation.

As the project requirements for AI are high, teams want full-stack developers who can innately understand the project requirements and offer solutions.

If you are a beginner full stack developer planning to explore AI technology, here is an excellent AI full stack development roadmap. Start with mastering the programming language and move up the ladder gradually through machine learning and data science.

3. IoT

The world was reimagined with the Internet of Things (IoT) technology. It is an ecosystem where all devices can connect, share information, and interact with humans. According to a study, 127 new devices are connected to the internet every second, accelerating growth. This is the reason why experts tout IoT as the Industrial Revolution 4.0. We are still not fully prepared to exploit the benefit of IoT technology which is why full stack developers are needed. They are the engineers who can handle both the front end and back end of an IoT application.

The manufacturing and mining industry is increasingly using Internet of Things applications. Full stack IoT development with straightforward and outspoken frontend, backend, and UI/UX is required for such sectors. Unsurprisingly, companies target businesses in these industries with innovative products like remote fault locators, automated equipment controls, and data mining from thousands of field sensors. Full stack developers are ideal candidates to support the development of such IoT products as they can handle both frontend and backend applications.

4. Blockchain

The success of Bitcoins popularized the concept of blockchain. It is one of the full stack trends that developers must follow. Blockchain is an umbrella term, and there are infinite possibilities for this technology in real work. It offers myriad benefits such as security, speed, distribution, immutability, and transparency. This is why industries like finance, banking, ERP, healthcare, and many more are rushing to adopt this technology.

Statista projects that the market revenue for blockchain technology will grow multifold to reach $39 billion by 2025. The 60% growth of the technology will be in the finance sector alone because “the technology has also proven itself to have great promise in the field of digital currency and payments, with the most successful cryptocurrencies in the world built using the platform and many businesses using some type of blockchain system for inter-company transactions.”

Full stack blockchain development is one of the processes that banks and finance firms are exploring to deliver more innovative products to customers. Building private blockchain platforms and networks without interdependency is one application that developers should explore.

Full-stack developers with skills and experience in creating decentralized applications and enterprise architecture can efficiently work on Blockchain application development.

Conclusion

Full stack developers should track these trends because even a minor technological shift creates challenging opportunities to move up the career ladder. Focus on learning skills to provide solutions within the scope of these trends. Full stack development in emerging technologies might not be easy, making it a more satisfactory venture.

Talent500 is a platform for full stack developers to explore global opportunities at some of the fastest-growing startups and Fortune 500 companies. Sign up here to know more.

Microservices v/s APIs: Clear differences

Posted on October 19, 2022November 9, 2022 by vallish

In software architecture and integration, microservices and APIs are often-used terms. The terms might sound new to beginners, but they are the core concepts of every online application today. Experienced developers have a deeper understanding of programs based on microservices and APIs. To use either of these technologies in your product, you must understand how they are different and what their end goals are.

This article will examine the critical differences between microservices and APIs.

What is a Microservice?

Microservices have a misleading name that often seems like a minor development task, but in reality is a broad development domain that covers full-fledged, optimized, and secure software solutions. In software development, microservices can be defined in two contexts:

An independent, tiny development component that is represented by a single microservice.
A microservice can also be used in loosely-coupled software development architecture to facilitate the development of large and complex software solutions. It does so by creating and integrating multiple small developments.

Simply put, microservices are a collection of small development components that, when integrated, create an extensive and robust software product.

What is an API?

API stands for Application Programming Interface. It is not a component like a microservice but a protocol that defines the structure following which two applications can communicate and interact with each other’s data. APIs are software solutions that focus on a particular service. In the software infrastructure, APIs are usually put between the core code components and the front-end interface of the application.

The APIs are added to the software to provide the ability to perform a specific task or borrow functionality from third-party service providers. Whenever software is required to lend its functionality or data to another product, developers must create APIs and write high-level API reference documents and best practices to guide other developers using their APIs.

Microservices and APIs: How do they differ?

It is difficult to compare or distinguish between a microservice and an API because of several similarities and their use within the development architecture.

Microservices is a software development approach that breaks the product into several modular components. Conversely, APIs are application communication tools that make cross-application communication possible.

1. Microservices rely on APIs

An API is not of much use as it lies in the system dormant unless it is connected to services or microservices to attain functionality. From a different perspective, we can also suggest that microservices depend on APIs to extend the functionality to the users. Software as a service, function as a service, and machine learning as a service are all examples of how microservices use APIs as an essential infrastructural component for their design.

2. Characteristics

Microservices are loosely coupled to allow easier integration and faster creation of robust software. They are decentralized in nature, highly manageable, and scalable. An API is lightweight, fast, and extensively specified but does not have a modular design. Keeping an API light is essential to allow other applications to integrate without crashing the host application.

3. Function

In functionality, both services are very distinct.

A software architecture based on microservices is defined by the business capabilities and priorities, which are usually designed to solve big and complex problems. But, using an API, a software system can be easily extended, connected to, or integrated with several other applications. APIs are designed to allow reusable interfaces to connect to any number of services as long as they follow the API integration guidelines. APIs are not visible to the end users. Instead, they are under the hood and directly accessible to other applications that want to connect to your software application.

4. Versatility

Microservices are used to integrate specific business capabilities into a product, limiting their versatility. They are not robust components but highly specified functional units that give software its purpose.

APIs, on the contrary, are highly versatile. You can create an API using any containerized service with any programming language, such as Java, Python, and PHP. Also, APIs can be deployed on any cloud infrastructure, such as AWS and Google Cloud Platform. Furthermore, there are several kinds of APIs to choose from for your application. The most popular are RESTful APIs that allow communication between the two applications over the HTTP endpoints. Such an API uses GET, POST, PUT, and DELETE commands to fetch data. Some other popular API formats are RPC, SOAP, and GraphQL.

Conclusion

There is no choice between microservices and APIs for building a practical and robust application or software. You have to use both: microservices to create business-specific software or application, while APIs are necessary for these microservices to communicate within its layers and third-party applications.

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What is the future of DevOps technologies?

Posted on October 10, 2022November 10, 2022 by vallish

DevOps is one of software development’s most widely adopted automation processes. DevOps engineers are responsible for accelerating the development of products and frequent releases of updates. Some of the most renowned companies in the world, such as Google, Netflix, and Amazon, use this methodology to increase the efficiency and productivity of their teams.

According to market research, the expansion of DevOps is expected to reach $287.9 billion by the year 2026. This growth will demand more DevOps engineers to join the engineering teams. If you are wondering about the future of DevOps technologies, the prospects are up-and-coming.

This article explores the future of DevOps technologies and how organizations are improving the development culture by implementing this methodology.

What is DevOps?

DevOps is the methodology that provides an alternative to the agile development environment for creating faster and smoother development of applications. It is a continuous process that also includes last-mile delivery. This methodology ensures that product testing, development, and operation are in sync and that delivery can be made in less time. Several processes are involved in DevOps, such as voting, building and integration, continuous testing, reporting, monitoring, and configuration.

For a successful career as a DevOps engineer, you need to focus on building skills in the following technologies.

1. AI/ML in the DevOps

The DevOps methodology has made the software development life cycle much more efficient. Combined with a cloud-native approach and microservice architecture, it is possible to build any software with limited resources and time. With this approach, you can integrate testing and production environments to find problems with the application before it goes live proactively.

But there is scope for further improvement in DevOps pipelines by applying artificial intelligence and machine learning. With these technologies, you can build and automate much better, which is why the industry is gradually transitioning from DevOps to DataOps and AIOps. The focus is shifting to using artificial intelligence and machine learning technologies to log and monitor metrics that give more control automatically. It would help if you learned tools like Moogsoft and BigPanda, a leader in AIOps.

2. Serverless computing

DevOps teams are actively using serverless architecture for maintaining their production pipelines because of the massive operational cost of server infrastructure. DevOps automates software development and deployment based on several components. When you get a modular component view of the pipeline, it becomes much easier to identify the bottlenecks and unnecessary server resources. The serverless architecture can mitigate the challenges common to the product’s deployment and development.

If you plan to become a DevOps engineer, you should learn how serverless computing can streamline overall DevOps operations. Companies prefer serverless architecture because it dramatically reduces server maintenance costs and workloads. The flexibility, reliability, cost efficiency, and speed of serverless DevOps infrastructure improve the efficiency of the complete software life cycle from development to deployment.

3. Low-Code/No-Code applications

Many organizations are adopting the low-code/no-code development approach to deploy applications swiftly and to keep up with the demand for new features and applications from users. DevOps engineers experience higher agility and can develop fast-track applications to meet the market’s demand. Adopting a low-code approach in the DevOps framework is a game changer.

With no-code application development, you can allow non-technical contributors to be part of software development through the help of visual interface integrations. As DevOps engineers play an essential role in connecting end users with developers, they play a crucial role in adopting this trend. This is another DevOps technology that you should focus on.

4. Container technology

Another technology that is evolving fast and emerging strongly is container technology. Containers are handy for sandbox applications for security or resource constraints reasons. There are also instances when companies use containers per user or user session to micro-monitor the behavior of their products. Including container technology in DevOps allows limitless possibilities to improve user and system security and enhance user analytics.

Simply put, container technology allows putting all the software components in a single unit that can be used to run microservices or larger applications alike. All the code, libraries, configuration information, and other dependencies can be withheld in this container, making it much smoother to deploy software across the network. Another reason why this DevOps technology is becoming popular is because of its lightweight architecture. Unlike deploying full-scale servers to test applications, containers make it possible to deploy multiple versions of the same application and multiple operating systems.

Conclusion

DevOps is evolving fast and driving a significant transformation in the software industry. The DevOps technologies we have listed here are helping organizations to move rapidly beyond simple automation and enhance their ability to design, produce, launch, and maintain high-quality software products. As a DevOps engineer, you must familiarize yourself with these technologies for a successful career.

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Best practices for speeding up code reviews

Posted on October 10, 2022November 10, 2022 by supriya

Code review is a natural process of software development. It is required to ensure the quality of the code and functionality of the final product. But for software engineers, it is often a painful experience as reviews can be extremely slow, causing delays and switching between tasks. For developers, code reviews can be extremely frustrating, especially when the reviewer is nitpicking or bikeshedding.

Software development teams employ several tactics to correct this problem. While smaller groups working on small-scale projects can eliminate the code review process, it is not an option for enterprise-level companies. Rather than removing pull requests and code reviews altogether, you can improve the experience for both developers and code reviewers by adopting some best practices.

This article will explore some best practices for speeding up code reviews.

Keep pull requests small

One of the biggest reasons developers dread reviewing pull requests is the high quantity of changes in the code base. When there are 1000+ lines of code changes, reviewing these changes can take hours or even days. Often, a reviewer skims through the code rather than carefully reviewing the changes. As a result, the quality of the code is compromised.

Keeping pull requests small is an excellent idea to have high review quality. When a small number of changes are to be reviewed, engineers do not have to spend too much time creating a mental map of how these changes interact with the code. Less number of changes means fewer errors and comments. The reviewer doesn’t have to run to the author often when pull requests are kept smaller.

Keeping the pull requests small can seem daunting if you are not efficiently breaking down your work into smaller tasks. Try to segregate tasks such as separate re-factoring from new features or bug fixes. You can also use feature flags in your code to directly merge small changes into the main branch of the code without showing up in the production app.

Implement Response Time SLAs

The code review cycle becomes inefficient when pull requests are regularly unreviewed for long durations. You must set clear expectations for how quickly a new pull request should be reviewed. What is the maximum time your team allows a code review request to stay pending? Is it an hour? or 24 hours? Two days?

The answer most likely depends on the size of your team, but the best practice for code review is to establish a precise, responsive time SLA (service level agreement). An SLA usually refers to how quickly our response is given when a technical issue is raised. There should be the right balance in deciding response time SLA.

You cannot expect the code reviewers to attend to your new pull requests immediately and drop whatever they are doing. At the same time, it is detrimental to wait for hours to get the changes reviewed. The right approach is to set a regular response time SLA for pull request reviews. For instance, your team can establish a three-hour response time SLA for internal team code reviews and a 24-hour response time SLA for external pull requests.

The right balance of response time SLA puts your team in a flow state where every developer can work on the report and review pull requests throughout the day without affecting their productivity.

Set up continuous integration pipelines

If code reviewers have to go through minimal changes such as comments like “Missing Semicolon” or “Incorrect Indentation”, it will be highly tedious for them to complete the review. Ensure that developers are not spending time on issues that code formatters and code linters can easily take care of. Several tools are available to ensure that code is formatted correctly so developers can focus on essential things.

For example, suppose you are working on a JavaScript project. In that case, you can configure a formatter like Prettier and a linter like ESLint low to automatically format the code in your repo. Additionally, you can set up continuous integration for the repo using tools like Travis CI and GitHub Actions. This CI pipeline will run the formatting and linting task when you’re unit testing. The CI pipeline will block the pull request from merging with the main branch if the pipeline fails at any step.

This approach can automate several essential parts of the code review process, saving hours.

Train junior and mid-level engineers

One way to improve the capability of your team to conduct quick and effective code reviews is to teach less experienced engineers. During a code review, help junior and mid-level engineers understand what’s important and what’s not. Teach them the grammar of code review comments to communicate efficiently. You can also take references from Google’s Code Review Developer Guide, an excellent guide for code authors and reviewers.

Conclusion

Code review is an integral part of the software development lifecycle, but in the absence of optimization, it can result in unwarranted delays. We hope these steps will help you streamline the code review process.

To learn more, refer to the guide on understanding and improving code quality from Talent500. We are a platform for software engineers to find career re-defining opportunities with global companies. Join us here.