The effectiveness of automated software testing techniques (keynote)

The effectiveness of automated software testing techniques (keynote)

연구 분야: Verification

학회: A-TEST 2020: Proceedings of the 11th ACM SIGSOFT International Workshop on Automating TEST Case Design, Selection, and Evaluation

With the rise of AI-based systems, such as self-driving cars, Google search, and automated decision-making systems, new challenges have emerged for the testing community. Verifying such software systems is becoming an extremely difficult and expensive task, often constituting up to 90% of the software expenses. Software in a self-driving car, for example, must safely operate in an infinite number of scenarios, which makes it extremely hard to find bugs in such systems. In this talk, I will explore some of these challenges, and introduce our work which aims at improving the bug-detection capabilities of automated software testing. First, I will talk about a framework that maps the effectiveness of automated software testing techniques, by identifying software features that impact the ability of these techniques to achieve high code coverage. Next, I will introduce our latest work that incorporates defect prediction information to improve the efficiency of search-based software testing to detect software bugs.