Author: Tania Zhydkova

The question of how well your testing is running is often answered with a shrug. While you may be measuring the quality of your product, you’re not necessarily measuring the effectiveness of your process. How do you know that a test has been successful? What metrics do you base your decisions on? This article will cover several common testing process mistakes and how to avoid them.

1. Failing To Engage Developers at an Early Stage

Your devs start working in silos because one dev might write new code, but the others are stuck fixing old code. When this happens in a waterfall process, you delay the release date, and no one is happy.

Mobile development can be a challenge to test. There are too many variables to test manually, such as version compatibility, input values, and different environments. Necessary features can be missing or simply wrong in some scenarios, or device features, such as GPS, aren’t accurate enough for automated testing. What’s more, third-party services and integrated components may change constantly.

Read on for tips that will help you nail automated QA and not let it derail the entire project.

AI-powered test automation tools are the next frontier for testers familiar with traditional methods. With a range of features and benefits, these solutions help you break new ground (and save time) when it comes to test automation. Let’s dive into this chapter to discover what they are and how they will help your team be more efficient, accurate, and transparent!

Perfecto Scriptless

Perfecto Scriptless is another solution that enhances Selenium automation — and Selenium is open-source. It promises to be a quick onboarding tool with no coding required that can be used for integration, usability, and performance testing.  Perfecto can work with Jenkins, TeamCity, Jira, and GitLab, among others.

The arrays of produced software are growing exponentially, which means that the amount of test coverage required to guarantee quality is increasing to the same extent. This also leads to that kind of causal relationship — more lines of code, more defects. So, in addition, to prepare the QA team for test automation and optimization, you also need to think about implementing an effective bug reporting tool.

However, even with an effective web-based test case management tool in place, it is necessary to understand what progress metrics in software testing need to be applied in order to get a clearer picture of the efficient efforts of quality assurance. In this article, we will analyze what test metrics and measurements are considered essential, which of them are premier in the agile methodology, and which KPI is necessary to track the performance of the team.

For context, artificial intelligence in this article refers to its modern state and not the ideal goal. We live in a world of narrow or weak AI, which beats humans at individual tasks such as trying out basic troubleshooting options faster than a developer would. We’re still years or decades away from truly strong AI that would do almost anything a human could. It means that artificial intelligence tests won’t happen without human input, but you can minimize the effort that much.

How Does AI Implementation Improve the Software Testing Process?

Artificial intelligence in software testing is the natural evolution of automated QA. AI test automation goes a step further than emulating manual work. “The machine” also decides when and how to run the tests in the first place.