We all know that test automation is a must if you hope to perform software testing on a large e-commerce project. However, there is more than one way to write tests for your software, and each has its own pros and cons.
Artificial Intelligence (AI) and Machine Learning technologies are slowly finding a place in many aspects of digital organizations. In this article, Stella Murugesan explains to us what their impact on software testing could be. Automatic test cases creation anyone?
The first part of this article presented some of the current challenges of performance testing. It discussed also the data and times pillar of performance testing. This second part covers the resource and cost aspects. The author shares some final thoughts on the future of performance testing.
Are your full stack acceptance tests slow, non-deterministic and hard to maintain? You’re not alone. Imagine running hundreds of them in a few seconds, giving the same result every time. How do you think a feedback loop that fast would that affect your team’s productivity?
Faster software delivery through approaches like DevOps or Continuous Delivery impacts software testing practices. This article summarizes the steps that software testing teams can undertake in order to adapt to the fast development process in projects with Continuous Integration/Continuous Delivery (CI/CD).
More and more organizations build test automation frameworks based on the WebDriver and Appium tools to perform software testing their web and mobile projects. A big part of why there are so many flaky tests is that we don’t treat our tests as production code. Moreover, we don’t treat our framework as a product.
Agile, DevOps, Continuous Delivery, Testing in Production: all these modern trends put pressure on the traditional approach to software testing in general and more specifically on the performance testing activity. In this article, V.M.Guruprasath explains issues created by the new context and proposes some possible solutions.
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