What new tools and techniques are being used in web application quality assurance?

In order to guarantee the dependability and quality of web applications, new methods, and technologies are continuously being developed in the field of web application quality assurance. Frameworks for automating testing: Cypress: Cypress's quick and dependable end-to-end testing capabilities helped it become well-known. Playwright: Playwright is a browser automation tool that is well-known for supporting a number of programming languages and being cross-browser compatible.

It's worth mentioning that tools described there only concern end-to-end testing (Selenium, Puppeteer etc) However the notion of automated tests extend to unit testing and integration tests as well. These tools differ from framework to framework (e.g NUnit for .NET , JUnit for Java etc) but the idea remains the same, to stop testing functions, endpoints, pages etc manually.

Automated testing is a significant time-saver. Let's consider a scenario: you've painstakingly developed a software with a multitude of features, each thoroughly tested. Now, imagine you're about to release an updated version with an additional five features. You'd need to re-examine all the previous and new test cases to ensure smooth operation. Just think about how much time it would take if you had to do this manually. And what if you had to frequently introduce new features, fix bugs, and conduct thorough tests? It wouldn't take long for this to become a frustrating and monotonous task. This is where automated testing steps in to greatly ease these challenges.

Theh newer tools and techniques in web application quality assurance include automation testing, artificial intelligence, continuous integration/continuous deployment (CI/CD) and security testing tools.

I've been using CircleCI and GitHub Actions for quite some time now, and I can't go back. The ease these tools provide is unmatched; you just have to set them up once, and they support your workflow seamlessly. There's no hassle with bug-free deployments, and a simple code push triggers deployment. With the advancements in AI, setting up these tools has become even easier.

I can identify anomalies or unexpected behavior in the application, helping to discover defects that might go unnoticed with traditional testing. AI can generate realistic test data and use cases to validate how the application handles various scenarios and edge cases. NLP techniques can be used to analyze user feedback and comments, helping to identify common issues and sentiments regarding the web application. AI can assess web applications for accessibility compliance by simulating user interactions and checking for adherence to accessibility standards. To implement AI and ML for web application quality assurance, you'll need data, appropriate algorithms, and a well-defined strategy.

Browser Compatibility testing/validation is must irrespective of the technology stack leveraged to develop a web application. A application can be developed and provide a seamless experience, but without its compatibility at end-users browser it’s no use to get implemented. There are lot of automation tools that can provide reports of each page in a web application with parameters in minimal time consumption.

Shift-Left Testing approach involves moving testing activities earlier in the software development lifecycle, even before coding begins. This helps in identifying and fixing issues sooner, reducing the cost of fixing defects in later stages. Techniques like Behavior-Driven Development (BDD) and Test-Driven Development (TDD) fall into this category.

Performance Testing: To make sure web apps can manage anticipated loads and function well in a variety of scenarios, performance testing is an essential component. Various tools are available for performance testing, each with specific advantages and applications. Apache JMeter: An open-source, widely used Java-based tool for performance, stress, and load testing. It is compatible with many protocols, such as FTP, HTTP, JDBC, etc. Gatling: Open-source load testing tool that uses Scala for scripting performance tests. Suitable for high-concurrency scenarios. Locust: Open-source, Python-based tool, ideal for simulating user behavior in load tests.Suited for distributed load generation.