Enhancing Bug-Inducing Commit Identification: A Fine-Grained Semantic Analysis Approach

IF 6.5 1区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

IEEE Transactions on Software Engineering Pub Date : 2024-10-09 DOI:10.1109/TSE.2024.3468296

Lingxiao Tang;Chao Ni;Qiao Huang;Lingfeng Bao

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引用次数: 0

Abstract

The SZZ algorithm and its variants have been extensively utilized for identifying bug-inducing commits based on bug-fixing commits. However, these algorithms face challenges when there are no deletion lines in the bug-fixing commit. Previous studies have attempted to address this issue by tracing back all lines in the block that encapsulates the added lines. However, this method is too coarse-grained and suffers from low precision. To address this issue, we propose a novel method in this paper called Sem-SZZ , which is based on fine-grained semantic analysis. Initially, we observe that a significant number of bug-inducing commits can be identified by tracing back the unmodified lines near added lines, resulting in improved precision and F1-score. Building on this observation, we conduct a more fine-grained semantic analysis. We begin by performing program slicing to extract the program part near the added lines. Subsequently, we compare the program's states between the previous version and the current version, focusing on data flow and control flow differences based on the extracted program part. Finally, we extract statements contributing to the bug based on these differences and utilize them to locate bug-inducing commits. We also extend our approach to fit the scenario where the bug-fixing commits contain deleted lines. Experimental results demonstrate that Sem-SZZ outperforms the state-of-the-art methods in identifying bug-inducing commits, regardless of whether the bug-fixing commit contains deleted lines.

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增强错误诱发提交识别：细粒度语义分析方法

SZZ 算法及其变体已被广泛用于根据错误修复提交来识别引发错误的提交。然而，当错误修复提交中没有删除行时，这些算法就会面临挑战。以前的研究试图通过回溯包含新增行的代码块中的所有行来解决这个问题。然而，这种方法粒度过粗，精度较低。为了解决这个问题，我们在本文中提出了一种基于细粒度语义分析的新方法，称为 Sem-SZZ。起初，我们观察到，通过回溯添加行附近未修改的行，可以识别出大量诱发错误的提交，从而提高了精确度和 F1 分数。基于这一观察结果，我们进行了更精细的语义分析。首先，我们对程序进行切分，提取出添加行附近的程序部分。随后，我们根据提取的程序部分，比较上一版本和当前版本的程序状态，重点关注数据流和控制流的差异。最后，我们根据这些差异提取出导致错误的语句，并利用这些语句找出导致错误的提交。我们还扩展了我们的方法，以适应修正错误的提交包含删除行的情况。实验结果表明，无论修复漏洞的提交是否包含删除行，Sem-SZZ 在识别诱发漏洞的提交方面都优于最先进的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Software Engineering 工程技术-工程：电子与电气

CiteScore

9.70

自引率

10.80%

发文量

724

审稿时长

6 months

期刊介绍： IEEE Transactions on Software Engineering seeks contributions comprising well-defined theoretical results and empirical studies with potential impacts on software construction, analysis, or management. The scope of this Transactions extends from fundamental mechanisms to the development of principles and their application in specific environments. Specific topic areas include: a) Development and maintenance methods and models: Techniques and principles for specifying, designing, and implementing software systems, encompassing notations and process models. b) Assessment methods: Software tests, validation, reliability models, test and diagnosis procedures, software redundancy, design for error control, and measurements and evaluation of process and product aspects. c) Software project management: Productivity factors, cost models, schedule and organizational issues, and standards. d) Tools and environments: Specific tools, integrated tool environments, associated architectures, databases, and parallel and distributed processing issues. e) System issues: Hardware-software trade-offs. f) State-of-the-art surveys: Syntheses and comprehensive reviews of the historical development within specific areas of interest.