Large Language Models for Equivalent Mutant Detection: How Far Are We?-Reference-Cited by-同舟云学术

Large Language Models for Equivalent Mutant Detection: How Far Are We?

Published:2024-09-11 Issue: Volume:29 Page:1733-1745
ISSN:
Container-title:Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis
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Author:

Tian Zhao¹^ORCID,Shu Honglin²^ORCID,Wang Dong¹^ORCID,Cao Xuejie¹^ORCID,Kamei Yasutaka²^ORCID,Chen Junjie¹^ORCID

Affiliation:

1. Tianjin University, Tianjin, China

2. Kyushu University, Fukuoka, Japan

Funder

National Natural Science Foundation of China

CCF Young Elite Scientists Sponsorship Program (by CAST)

JSPS for the KAKENHI

Bilateral Program

the Inamori Research Institute for Science for supporting Yasutaka Kamei via the InaRIS Fellowship

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3650212.3680395

Reference94 articles.

1. Konstantinos Adamopoulos, Mark Harman, and Robert M Hierons. 2004. How to overcome the equivalent mutant problem and achieve tailored selective mutation using co-evolution. In Genetic and Evolutionary Computation–GECCO 2004: Genetic and Evolutionary Computation Conference, Seattle, WA, USA, June 26-30, 2004. Proceedings, Part II. 1338–1349.

2. Wasi Uddin Ahmad Saikat Chakraborty Baishakhi Ray and Kai-Wei Chang. 2021. Unified pre-training for program understanding and generation. arXiv preprint arXiv:2103.06333.

3. Toufique Ahmed Christian Bird Premkumar Devanbu and Saikat Chakraborty. 2024. Studying LLM Performance on Closed-and Open-source Data. arXiv preprint arXiv:2402.15100.

4. Is mutation an appropriate tool for testing experiments?

5. Using Mutation Analysis for Assessing and Comparing Testing Coverage Criteria