Systems | Development | Analytics | API | Testing

In this blog post, we will run JMeter tests from code. First we will look at how we can create an IntelliJ project in Java to build a JMeter performance test. Once you understand how JMeter tests in code can be written you can start to build tests in your development code base to compliment unit tests or functional tests written by developers during the creation of application and services.

Traditionally, manual test management and automated testing generate independent quality reporting data that has to be either manually integrated in spreadsheets or programmatically via scripts and reporting tools. This process takes valuable QA time away from actual testing and can also create inconsistency and inaccuracy when reporting on release readiness due to both the lag time in combining data from both sources and general human error.

Ready for your monthly debrief? We've got Appium 2.0 on virtual devices, error and crash reporting on real devices, and much more exciting news to share.

Following is a transcription of the AMA session with Rahul Parwal on the topic – Bug Advocacy. Here Rahul brings forth the importance of bug advocacy, its step-by-step implementation, and tips on making this process easier. We recently launched a course on Bug Advocacy instructed by Rahul Parwal. You can check the course out from our courses section.

Model-based test automation and record and playback — have you heard these buzz phrases before in the world of software testing? They’re two similar approaches to testing, yet they’re vastly different in their execution. But what exactly is the difference between these two techniques and why does it matter?

Visual regression testing is a type of regression testing to ensure that no changes to the program adversely affect the Graphical User Interface (GUI). It is also referred to as visual validation testing. Instead of focusing on the functionality of the software, it aims to validate the software’s aesthetic side. Visual regression testing verifies that all visual elements are displayed properly across all available browsers, devices, and platforms.

If you don't understand how an API works, you can't get the best out of it. That's one problem. The other is that they keep increasing in number, but the quality of the documentation for each of them doesn't. Lack of clear, quality documentation leads to more difficulty for developers, and with more difficulty comes less chance they integrate the API. Which could also be your API. That's where API exploration tools like SwaggerHub Explore come in handy to make this all way simpler.

Load testing is a big category, and how do you even start preparing for it? This post covers it all from planning to implementation.

In Part 1 of this series about Test Case Design, you learned that by using the Test Case Designer tool in Xray Enterprise, you take a model-first approach to designing more thorough and efficient tests. But how does the tool generate optimized scenarios once we define those models? And what do we mean by “optimized”?