μBERT: Mutation Testing using Pre-Trained Language Models
We introduce μBERT, a mutation testing tool that uses a pre-trained language model (CodeBERT) to generate mutants. This is done by masking a token from the expression given as input and using CodeBERT to predict it. Thus, the mutants are generated by replacing the masked tokens with the predicted ones. We evaluate μBERT on 40 real faults from Defects4J and show that it can detect 27 out of the 40 faults, while the baseline (PiTest) detects 26 of them. We also show that μBERT can be 2 times more cost-effective than PiTest, when the same number of mutants are analysed. Additionally, we evaluate the impact of μBERT’s mutants when used by program assertion inference techniques, and show that they can help in producing better specifications. Finally, we discuss about the quality and naturalness of some interesting mutants produced by μBERT during our experimental evaluation.
Mon 4 AprDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
14:00 - 15:00 | |||
14:00 20mTalk | Augmenting Equivalent Mutant Dataset Using Symbolic Execution Mutation | ||
14:20 20mTalk | μBERT: Mutation Testing using Pre-Trained Language Models Mutation | ||
14:40 20mTalk | Random Mutant Selection and Equivalent Mutants Revisited Mutation Rowland Pitts George Mason University | ||