Improving Mutation-Based Fault Localization with Plausible-code Generating Mutation Operators

DOI 10.1109/ase51524.2021.9678577 dblp

Abstract

This paper proposes a new mutation operator using neural network to generate plausible code elements to improve performance of mutation-based fault localization on omission faults. Unlike the existing mutation operators, the proposed mutation operator synthesizes new code elements at a given mutation site with a neural language model. We extended MUSE to use the proposed mutation operator, and conducted a case study with 3 omission faults found in JFreeChart of Defects4J. As a result, the accuracy of MUSE with the new mutation operator increased significantly in all three faults.

BibTeX

@inproceedings{Jeon-Hong:ASE21,
  author    = {Juyoung Jeon and
               Shin Hong},
  title     = {Improving {Mutation-Based} Fault Localization with Plausible-code Generating Mutation Operators},
  booktitle = {ASE},
  pages     = {1205--1207},
  publisher = {{IEEE}},
  year      = {2021},
}

Improving Mutation-Based Fault Localization with Plausible-code Generating Mutation Operators

Abstract

Related papers