Mutation Testing; Pre-Trained Language Models; CodeBERT
Résumé :
[en] 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.
Centre de recherche :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Security Design and Validation Research Group (SerVal)
Disciplines :
Sciences informatiques
Auteur, co-auteur :
DEGIOVANNI, Renzo Gaston ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal
PAPADAKIS, Mike ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
µBert: Mutation Testing using Pre-Trained Language Models
Date de publication/diffusion :
2022
Nom de la manifestation :
15th {IEEE} International Conference on Software Testing, Verification and Validation Workshops {ICST} Workshops 2022
Date de la manifestation :
April 4-13, 2022
Manifestation à portée :
International
Titre de l'ouvrage principal :
µBert: Mutation Testing using Pre-Trained Language Models
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