Tutorials and resources on how to apply test automation in software testing

Open Source Bug Tracking Tools

July 16, 2025 2

Bugs, issue, defect, there are many names to define the problems you have with software, either during the code development phase or when the application is already in production. Bug tracking tools are an integrated part of a software quality and software testing strategy and there are many open source tools available.

Cross-Browser Testing Tools (Free, Open Source, Paid)

July 8, 2025 8

When today’s applications run on various browsers (Chrome, Firefox, Safari, Edge, etc.) on different mobile or desktop devices with specific screen settings. This is why you need to perform cross-browser testing for your automated tests. This article lists the main desktop or online cross-browser testing tools available today.

Accessibility Testing at Scale Powered by Appium

July 2, 2025 0

To date, web applications have been the focus of digital accessibility tooling. Attention is now pivoting to mobile apps that are usually tested with Appium. So how do we test mobile apps for accessibility? Many organizations test their apps with assistive technologies.

Open Source Android Testing Tools

June 17, 2025 2

The shift towards mobile platforms is a strong trend currently and Android is the most widely adopted mobile OS with an estimated market share above 80% in 2014. You should naturally test all the apps developed for Android and a large number of open source testing tools and test automation frameworks have been developed to achieve this goal.

Your Data Quality Needs Testing Too

June 2, 2025 1

Application developers typically test their code, often with large suites of automated tests, before each new release. But, when the application is running live, it will be receiving data from multiple sources, over which we may have little control, and that data will sometimes be unexpected and invalid in some way.

Strengthening User Interaction Systems with Scalable ML and Data Testing

April 22, 2025 1

In today’s digital platforms-from shopping apps and streaming services to health trackers and customer portals-machine learning is central to how systems personalize experiences, automate decisions, and respond to user actions. But no matter how advanced a model is, it can fail if the data feeding it isn’t reliable.

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