Developing a tool that runs on artificial intelligence is mostly about training a machine with data. But you can’t just feed it information and expect AI to wave a magic wand and produce results. The type of data sets you use and how you use them to train the tool are important. Here are five pitfalls to be wary of.

Threat modeling‍ has transitioned from a theoretical concept into an IT security best practice. Choosing the right methodology is a combination of finding what works for your SDLC maturity and ensuring it results in the desired outputs. Let’s look at four different methodologies and assess their strengths and weaknesses.

Flaky tests could be the result of issues in the code, but more often they are due to assumptions in the test code that lead to non-relatable results. There are many reasons that tests can fail intermittently, and some can be easily avoided by applying good defensive design strategies. It's all about making your code agile.