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ICST 2022
Mon 4 - Fri 8 April 2022
Mon 4 Apr 2022 14:20 - 14:40 at Marlyn Meltzer - Mutation I Chair(s): Donghwan Shin

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 Apr

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

14:00 - 15:00
Mutation IMutation at Marlyn Meltzer
Chair(s): Donghwan Shin University of Luxembourg
14:00
20m
Talk
Augmenting Equivalent Mutant Dataset Using Symbolic Execution
Mutation
14:20
20m
Talk
μBERT: Mutation Testing using Pre-Trained Language Models
Mutation
Renzo Degiovanni SnT, University of Luxembourg, Mike Papadakis University of Luxembourg, Luxembourg
14:40
20m
Talk
Random Mutant Selection and Equivalent Mutants Revisited
Mutation
Rowland Pitts George Mason University