The advantages of shifting left and testing as early as possible are obvious. But as you automate more testing, the test suite grows larger and larger, and it takes longer and longer to run. Instead, just automate the process of finding the right set of tests to run. The key to that is machine learning. This isn't AI bots finding bugs autonomously without creating tests; this is a different way to use machine learning, and it’s far simpler.
AI-based tools have transformed from a vague, futuristic vision into actual products that are used to make real-life decisions. Still, for most people, the inner workings of deep-learning systems remain a mystery. If you don’t know what exactly is going on while the input data is fed through layer after layer of a neural network, how are you supposed to test the validity of the output? It’s not magic; it’s just testing.
Test coverage is an important metric within test management, and as technology evolves, we‘re able to leverage new trends to predict coverage. Weka, an open source suite of machine learning software, can take your test management beyond spreadsheets to the latest AI technologies, letting you predict your test coverage earlier with greater accuracy.
With the rise of technology like AI and practices like DevOps, teams everywhere are looking for ways to speed up testing without sacrificing quality. The articles in 2017 reflect that, with the most popular topics being test automation, testing machine learning systems, next-generation exercises, and the future of software testing. If you're looking for cutting-edge testing techniques, check out this roundup.
Most machine learning systems are based on neural networks, or sets of layered algorithms whose variables can be adjusted via a learning process. These types of systems don’t produce an exact result; in fact, sometimes they can produce an obviously incorrect result. So, how do you test them? Peter Varhol tells you what you should consider when evaluating machine learning systems.