More Effective Test Case Generation with Multiple Tribes of AI


Software testing is a critical activity in the software development life cycle for quality assurance. Automated Test Case Generation (TCG) can assist developers by speeding up this process. It accomplishes this by evolving an initial set of randomly generated test cases over time to optimize for predefined coverage criteria. One of the key challenges for automated TCG approaches is navigating the large input space. Existing state-of-the-art TCG algorithms struggle with generating highly-structured input data and preserving patterns in test structures, among others. I hypothesize that combining multiple tribes of AI can improve the effectiveness and efficiency of automated TCG. To test this hypothesis, I propose using grammar-based fuzzing and machine learning to augment evolutionary algorithms for generating more structured input data and preserving promising patterns within test cases. Additionally, I propose to use behavioral modeling and interprocedural control dependency analysis to improve test effectiveness. Finally, I propose integrating these novel approaches into a testing framework to promote the adoption of automated TCG in industry.

The 44th International Conference on Software Engineering Companion
Mitchell Olsthoorn
Mitchell Olsthoorn
PhD student

Mitchell Olsthoorn is a Ph.D. student in the Software Engineering Research Group (SERG) at Delft University of Technology. He is also a member of the Computational Intelligence for Software Engineering lab (CISELab) and the Blockchain lab. Mitchell holds an M.Sc. degree in Computer Science – with a specialization in Cyber Security and Blockchain. His interests include network security, computational intelligence, and pen-testing. Mitchell is currently working on Security testing for blockchain.