SZZ revisited: verifying when changes induce fixes

Chadd C. Williams, Jaime Spacco
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引用次数: 102

Abstract

Automatically identifying commits that induce fixes is an important task, as it enables researchers to quickly and efficiently validate many types of software engineering analyses, such as software metrics or models for predicting faulty components. Previous work on SZZ, an algorithm designed by Sliwerski et al and improved upon by Kim et al, provides a process for automatically identifying the fix-inducing predecessor lines to lines that are changed in a bug-fixing commit. However, as of yet no one has verified that the fix-inducing lines identified by SZZ are in fact responsible for introducing the fixed bug. Also, the SZZ algorithm relies on annotation graphs, which are imprecise in the face of large blocks of modified code, for back-tracking through previous revisions to the fix-inducing change. In this work we outline several improvements to the SZZ algorithm: First, we replace annotation graphs with line-number maps that track unique source lines as they change over the lifetime of the software; and second, we use DiffJ, a Java syntax-aware diff tool, to ignore comments and formatting changes in the source. Finally, we begin verifying how often a fix-inducing change identified by SZZ is the true source of a bug.
SZZ重访:验证更改何时引起修复
自动识别引起修复的提交是一项重要的任务,因为它使研究人员能够快速有效地验证许多类型的软件工程分析,例如用于预测错误组件的软件度量或模型。Sliwerski等人设计并由Kim等人改进的SZZ算法提供了一个过程,用于自动识别在bug修复提交中更改的修复诱导的前导行。然而,到目前为止,还没有人证实SZZ标识的修复诱导行实际上是引入修复错误的原因。此外,SZZ算法依赖于注释图,通过以前的修订来回溯到引起修复的更改,而注释图在面对大量修改的代码块时是不精确的。在这项工作中,我们概述了对SZZ算法的几项改进:首先,我们用行号图取代注释图,这些行号图在软件的生命周期中跟踪唯一的源行;其次,我们使用DiffJ(一个Java语法感知的diff工具)来忽略源代码中的注释和格式更改。最后,我们开始验证由SZZ识别的引起修复的更改是bug的真正来源的频率。
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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