Performance

AI, AI, AI—it is everywhere. We all read this in the news, see it in politics, in our web traffic, and now it’s coming to our tools. Interestingly, testing has been identified as one of the most essential areas for AI as well as for the safety of the public. This article considers references to start you on your AI test journey, "classic AI" problem areas, and identifies possible concepts to use when testing AI. As usual, it comes down to a willingness to learn new things or apply historical ideas to advance your test career.

Testing continuous technological change can seem like chaos. There are many challenges that need to be managed, such as unavailability of power, excessive temperature, incorrect configuration, unexpected behavior of services, network downtime, and processing slowdown in production. By deliberately engineering chaos, we’ll be able to discover many of our systems’ weaknesses before our users do.

Many performance testers think that after a few years of experience, they automatically become performance engineers. However, it isn't that straightforward; the route to becoming performance engineer is a long and continuous journey. This article details the many things performance engineers need to do beyond performance testing, and it gives an outline for steps to take to advance your career.

In order for performance test results to have value, you should report them in context. There are two main considerations: How do these compare to previous results? And how can we provide early reports on performance while emphasizing that these are preliminary results that may change significantly as we progress? Here are some ideas for responsible reporting.