Automation testing is a software testing technique that utilizes specialized automation testing tools to automatically run a suite of test cases, delivering faster and more accurate results compared to manual testing methods. The process of running the same test suite repeatedly is time-consuming, so by leveraging a test automation tool, it is much faster to write the test suite, re-play it as required, reduce human intervention, and improve testing ROI.

TestProject, the community-based test automation platform, has announced that it will be reaching its end of life (EOL) on March 31, 2022. While this may come as a surprise to many users who have come to rely on TestProject, the decision of discontinuing the platform was made after careful evaluation by the TestProject team. In this blog post, we will discuss what TestProject end of life means for your QA teams and how you can prepare for the future.

Imagine you open a shopping website, browse around and see some products of your liking. After placing them in the cart, you're all ready to check out, only to discover that your favorite items are nowhere to be found on the list. This is one of many examples of a software bug that could be caught using integration testing. There are multiple testing stages that software goes through before release, the two most popular being unit and integration testing.

Midnight, December 31, 1999, marked the beginning of a millennium where aircrafts would not fall out of the sky and bank ATMs would not spew millions of dollars from their cash withdrawal slots. Preemptive software testing and remediation on a global scale provided the automated world with some assurance that the machines would continue to run as they had for decades.

Software testing is the process of checking the quality of a product before launching. It measures the software's completeness to the single functional (or non-functional) attributes and whether it has fulfilled the business logic or shown us the missing gaps in requirements that need immediate tackles. Software testing is an integral part of the software development life cycle. Over time, software testing activities have evolved to meet the ever-increasing complexity of applications.

Every software engineer dreams of the day when they can simply type English-language sentences into a development platform and out pops a new, production-ready app. The app will be free of bugs because the natural language processing (NLP) functionality of the platform will have automatically created the test cases, run the tests, and self-corrected for changes to the app features. AI researchers in software testing are heavily invested in making testing as autonomous and natural as possible.

The promise of Artificial Intelligence (AI) in software testing is that an intelligent agent will one day replace humans. Instead of the struggle of manual labor involved in the endless unit and integrated proofing of software quality, machines will test computer systems without human intervention.

While ‘Smoke Testing’, ‘Sanity Testing’, and ‘Regression Testing’ are widely practiced in the world of QA, there still exist some misinterpretations or misconceptions around these concepts. For your team to make the most optimal use of the 3 methods, this article will help you to understand and differentiate them from one another.

“If you don’t care about quality, you can meet any other requirement” Gerald M. Weinberg Software testing has never been a walk in the park. It demands expertise in software development, users’ behavior, the software under test architecture, stakeholders’ requirements, and programming knowledge. Quality teams know firsthand how challenging it is to ensure software quality when its architecture is becoming more complex and teams are releasing at a pace faster than ever.

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