Testing

With the advent of AI systems being utilized to aid testing, the Testing sphere will be undergoing a massive shift where the Data used as the source for AI-based testing systems becomes critical.

Recently I have been set the challenge of trying to work out how to test robots and how to test (an) artificial intelligence. You’ll be pleased to know there are no spoilers in this as, well quite frankly, it remains a work in progress. Under duress, I would say my current thinking focuses on how predictable a robot should be, and how the concept of ‘expected results’ falls apart with artificial intelligence.

In this article, I assume the basic example of a TAS SLDC being applied synchronously to the SLDC of a System under Test (SUT).

Software development (but also the development of Machine Learning and AI) is combat against chaos and complexity. It is bad enough when software becomes chaotic and does unexpected things, but it is even worse, when software becomes too complex to be fixed.

One of the things that are important to consider when you’re testing artificial intelligence is the possibility that you have unintentional bias in your training data. Because if there is, you’ll end up with a biased system… So, how can this happen and what should you look for?

In this webinar, we will discuss the Test Architecture Model in DevOps and walk you through the holistic planning approach, multi-level automated test process in an IDE/CI/CD development pipeline.

Test automation is preventive in nature. It is supposed to prevent bugs from getting into production before they can be detected. In the best case, test automation is invisible to the end user. So how do you evaluate something that shouldn't even appear?

Artificial Intelligence (AI) in one guise or another is beginning to permeate our everyday activities.  Whether it’s a visit to the doctor’s office to get an initial diagnosis of eye problems or trying to find a new job, AI is either already making decisions on your behalf or will be in the not too distant future.

Sogeti's annual TextExpo event discussed the future outlook for the software testing industry, led by top professionals in the field. Read about my compilation of how artificial intelligence, machine learning or intelligent systems affect testing now and in the future.

The usage of the gTAA constantly delivers a good overview which technologies and implementations have to be implemented in which spot in or inter between the TAA layers.

We all know the highly advanced algorithm Google employs to test the millions of pieces of content pieces submitted on its platform every day. But, recently an article mentioned that Google is now planning to hire human readers in the backend to ensure the quality of the content. This strengthened my belief in human testers, who still have an important place in the testing value chain, in spite of growing concerns that automation will take over the role of human testers.

This is the next blog in our practical “Guideline for the Standard Test Levels” used in agile SW-Projects from Sogeti Testlab Hub Stuttgart. In this blog, we want to talk about the user story acceptance test strategy.