Testing

With ever-increasing agility and automation supporting a move to continuous deployment, the role of the tester is becoming increasingly important as we transition from control to assurance.

Developing more complicated systems including AI means in principle that there will be longer periods of testing. Agile can help to shorten that and does a great job in that.

In Buddhism, Samsara means the endless cycle of death and rebirth. Life is seen as a circle where death is only a gate to rebirth and beginning a new cycle. The Buddhist say that Samsara is suffering. We cycle through this endless path of suffering over and over again. Same cycles repeat until we can’t tell them apart. Feeling like you have already lived this moment. Sound familiar?

It is amazing if you think where we have got to in technology over the last few decades, so too in testing. When I started testing in the previous century it was all about squashing bugs during different types of test phases.

In the world of testing, a number of different test methods are available. The most used solution is the structured test method. But this is not all. In my project, I had a discussion about the difference between explorative testing and ad-hoc testing. Is there a difference and when should I use either solution?

I have the good fortune to work in the centre of the City of London and history is all around me. For me, and a history buff, it’s brilliant.

Seeing how much the world of IT and testing has changed in the last 10 years, got me wondering what sort of testing bucket list I would draw up for the next ten years.

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?