Reference : µBert: Mutation Testing using Pre-Trained Language Models

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/51744

Title :	µBert: Mutation Testing using Pre-Trained Language Models
Language :	English
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)]
Publication date :	2022
Main document title :	µBert: Mutation Testing using Pre-Trained Language Models
Author, co-author :	Degiovanni, Renzo Gaston
	Papadakis, Mike
Publisher :	IEEE
Pages :	160--169
Peer reviewed :	Yes
Audience :	International
Event name :	15th {IEEE} International Conference on Software Testing, Verification and Validation Workshops {ICST} Workshops 2022
Event date :	April 4-13, 2022
Keywords :	[en] Mutation Testing ; Pre-Trained Language Models ; CodeBERT
Abstract :	[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 centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Security Design and Validation Research Group (SerVal)
Funders :	Fonds National de la Recherche - FnR
Name of the research project :	INTER/ANR/18/12632675/SATOCROSS
Target :	Researchers ; Professionals ; Students ; General public
Permalink :	http://hdl.handle.net/10993/51744
DOI :	10.1109/ICSTW55395.2022.00039
Other URL :	https://doi.org/10.1109/ICSTW55395.2022.00039

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