一句话：提出借助神经语言模型在变异点合成合理代码元素的新型变异算子，提升遗漏类故障的变异故障定位精度。
基于变异的故障定位（MBFL）通过人为注入变异来模拟故障，但现有变异算子只能改动已有代码，难以刻画省略类故障（omission fault）——即本应出现却缺失的代码，因而在这类故障上定位精度受限。本文提出一种新的变异算子：借助神经语言模型，在给定变异点直接合成出合理的代码元素，从而生成传统算子无法产生的变异体。作者将该算子集成到 MBFL 工具 MUSE 中，并在 Defects4J 中 JFreeChart 的 3 个省略类故障上开展案例研究。结果显示，使用新算子的 MUSE 在全部 3 个故障上的定位精度都显著提升，表明以神经生成补齐缺失代码是改进 MBFL 的有效途径。
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.
DOI 原文 ·
@inproceedings{paperbot6614,
title = {Improving Mutation-Based Fault Localization with Plausible-code Generating Mutation Operators},
author = {Juyoung Jeon and Shin Hong},
booktitle = {ASE},
year = {2021},
doi = {10.1109/ase51524.2021.9678577}
}