一句话：提出 NeuroJIT，用开发者神经生理与经验感知特征刻画 commit 可理解性，以提升即时缺陷预测。
现代开发者在理解既有代码上下文的基础上进行修改并审查 commit；若 commit 难以理解，人为失误与引入缺陷的风险随之上升，但基于可理解性难度预测致缺陷 commit 的研究仍然有限。本文提出 NeuroJIT，采集开发者阅读不同代码片段时的神经生理反应与经验性主观评价，并结合代码特征，抽取能够刻画 commit 可理解性的特征。作者从三方面开展评估：这些特征与致缺陷风险的相关性、与广泛使用的预测特征之间的差异，以及它们能否提升即时缺陷预测模型的性能。结果表明，神经生理与经验层面的可理解性可作为有竞争力的预测因子，并能从独特视角为致缺陷 commit 提供更具可操作性的指导。
Modern developers make new changes based on their understanding of the existing code context and review these changes by analyzing the modified code and its context (i.e., commits). If commits are difficult to comprehend, the likelihood of human errors increases, making it harder for practitioners to identify commits that might introduce unintended defects. Nevertheless, research on predicting defect-inducing commits based on the difficulty of understanding them has been limited. In this study, we present a novel approach NeuroJIT, that leverages the correlation between modern developers’ neurophysiological and empirical reactions to different code segments and their code characteristics to find the features that can capture the understandability of each commit. We investigate the understandability features of NeuroJIT in three key aspects: (i) their correlation with defect-inducing risks; (ii) their differences from widely adopted features used to predict these risks; and (iii) whether they can improve the performance of just-in-time defect prediction models. Based on our findings, we conclude that neurophysiological and empirical understandability of commits can be a competitive predictor and provide more actionable guidance from a unique perspective on defect-inducing commits.CCS Concepts• Software and its engineering → Maintaining software.
@inproceedings{LeeJL24,
title = {NeuroJIT: Improving Just-In-Time Defect Prediction Using Neurophysiological and Empirical Perceptions of Modern Developers},
author = {Gichan Lee and Hansae Ju and Scott Uk-Jin Lee},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3695056}
}