µBert: Mutation Testing using Pre-Trained Language Models

[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.

Research center :

Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Security Design and Validation Research Group (SerVal)

Disciplines :

Computer science

Author, co-author :

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)

External co-authors :

Language :

English

Title :

µBert: Mutation Testing using Pre-Trained Language Models

Publication date :

2022

Event name :

15th {IEEE} International Conference on Software Testing, Verification and Validation Workshops {ICST} Workshops 2022

Event date :

April 4-13, 2022

Audience :

International

Main work title :

µBert: Mutation Testing using Pre-Trained Language Models

Author, co-author :

DEGIOVANNI, Renzo Gaston

PAPADAKIS, Mike

Publisher :

IEEE

Pages :

160--169

Peer reviewed :

Peer reviewed

Focus Area :

Computational Sciences

Additional URL :

https://doi.org/10.1109/ICSTW55395.2022.00039

Name of the research project :

INTER/ANR/18/12632675/SATOCROSS

Funders :

FNR - Fonds National de la Recherche

Available on ORBilu :

since 21 July 2022

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Bibliography

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