Machine Learning Lends a Hand for Automated Software Testing - The New Stack

Automated testing is increasingly important in development, especially for finding security issues, but fuzz testing requires a high level of expertise -- and the sheer volume of code developers are working with, from third-party components to open source frameworks and projects, makes it hard to test every line of code. Now, a set of artificial intelligence-powered options like Microsoft's Security Risk Detection service and Diffblue's security scanner and test generation tools aim to make these techniques easier, faster and accessible to more developers. "If you ask developers what the most hated aspect of their job is, it's testing and debugging," Diffblue CEO and University of Oxford Professor of Computer Science Daniel Kroening told the New Stack. The Diffblue tools use generic algorithms to generate possible tests, and reinforcement learning combined with a solver search to make sure that the code it's giving you is the shortest possible program, which forces the machine learning system to generalize rather than stick to just the examples in its training set. "What we have built is something that does that for you. If you give us a Java program, which could be class files or compiled Java bytecode, we give you back a bunch of Java code in the form of tests for your favorite testing framework," Kroening said.