Guess What: Test Case Generation for Javascript with Unsupervised Probabilistic Type Inference

Abstract

Search-based test case generation approaches make use of static type information to determine which data types should be used for the creation of new test cases. Dynamically typed languages like JavaScript, however, do not have this type information. In this paper, we propose an unsupervised probabilistic type inference approach to infer data types within the test case generation process. We evaluated the proposed approach on a benchmark of 98~units under test (i.e., exported classes and functions) compared to random type sampling w.r.t. branch coverage. Our results show that our type inference approach achieves a statistically significant increase in 56% of the test files with up to 71% of branch coverage compared to the baseline.

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.