STUNT - Improving Software Testing Using Novelty

• Applicant: Professor Dr. Gordon Fraser
• Subject Area: Software Engineering and Programming Languages
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 434705464

Software testing is an essential activity for identifying faults and ensuring software quality. Because testing generally is tedious and error-prone, automated test generation aims to support developers and testers in finding the most effective test cases. Many different techniques for test generation have been proposed, and some of the most popular ones are those guided by the source code (i.e., white-box techniques). However, whether classic white- box techniques such as code coverage really correlate with fault finding effectiveness is a much debated topic in research, and the guidance provided by source code metrics during test generation is known to be coarse and insufficient, thus inhibiting effective testing. The aim of this project is to improve the effectiveness of automated test generation by using the concept of novelty evaluated on program behaviour rather than source code to better guide test generation, and to produce more effective test suites. The project will address the problem of quantifying novelty, and will use machine learning techniques to reason about novelty in terms of program behaviour, rather than just in terms of traditional white-box measurements. The concept of novelty will be integrated into effective test generation algorithms, enhancing state-of-the-art white-box techniques with the concept of novelty, thus resulting in more effective test suites.

• DFG Programme: Research Grants