App review driven collaborative bug finding

IF 3.5 2区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Empirical Software Engineering Pub Date : 2024-07-26 DOI:10.1007/s10664-024-10489-x

Xunzhu Tang, Haoye Tian, Pingfan Kong, Saad Ezzini, Kui Liu, Xin Xia, Jacques Klein, Tegawendé F. Bissyandé

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引用次数: 0

Abstract

Software development teams generally welcome any effort to expose bugs in their code base. In this work, we build on the hypothesis that mobile apps from the same category (e.g., two web browser apps) may be affected by similar bugs in their evolution process. It is therefore possible to transfer the experience of one historical app to quickly find bugs in its new counterparts. This has been referred to as collaborative bug finding in the literature. Our novelty is that we guide the bug finding process by considering that existing bugs have been hinted within app reviews. Concretely, we design the BugRMSys approach to recommend bug reports for a target app by matching historical bug reports from apps in the same category with user app reviews of the target app. We experimentally show that this approach enables us to quickly expose and report dozens of bugs for targeted apps such as Brave (web browser app). BugRMSys ’s implementation relies on DistilBERT to produce natural language text embeddings. Our pipeline considers similarities between bug reports and app reviews to identify relevant bugs. We then focus on the app review as well as potential reproduction steps in the historical bug report (from a same-category app) to reproduce the bugs. Overall, after applying BugRMSys to six popular apps, we were able to identify, reproduce and report 20 new bugs: among these, 9 reports have been already triaged, 6 were confirmed, and 4 have been fixed by official development teams.

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应用程序审查驱动的协作式错误查找

软件开发团队通常欢迎任何暴露代码库中错误的努力。在这项工作中，我们提出了一个假设，即同一类别的移动应用程序（如两个网络浏览器应用程序）在其演化过程中可能会受到类似错误的影响。因此，有可能将一个历史应用程序的经验用于快速查找其新的对应程序中的错误。这在文献中被称为协作式错误查找。我们的新颖之处在于，我们通过考虑应用程序评论中提示的现有错误来指导错误查找过程。具体来说，我们设计了 BugRMSys 方法，通过匹配同类应用程序的历史错误报告和用户对目标应用程序的评论，为目标应用程序推荐错误报告。我们通过实验证明，这种方法能让我们快速揭露和报告目标应用程序（如 Brave（网页浏览器应用程序））的数十个错误。BugRMSys 的实现依赖于 DistilBERT 生成自然语言文本嵌入。我们的管道考虑了错误报告和应用程序评论之间的相似性，以识别相关的错误。然后，我们关注应用程序评论以及历史错误报告（来自同类应用程序）中的潜在重现步骤，以重现错误。总体而言，在将 BugRMSys 应用于六款流行应用程序后，我们能够识别、重现并报告 20 个新错误：其中，9 个报告已被分流，6 个被确认，4 个已由官方开发团队修复。

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

Empirical Software Engineering 工程技术-计算机：软件工程

CiteScore

8.50

自引率

12.20%

发文量

169

审稿时长

>12 weeks

期刊介绍： Empirical Software Engineering provides a forum for applied software engineering research with a strong empirical component, and a venue for publishing empirical results relevant to both researchers and practitioners. Empirical studies presented here usually involve the collection and analysis of data and experience that can be used to characterize, evaluate and reveal relationships between software development deliverables, practices, and technologies. Over time, it is expected that such empirical results will form a body of knowledge leading to widely accepted and well-formed theories. The journal also offers industrial experience reports detailing the application of software technologies - processes, methods, or tools - and their effectiveness in industrial settings. Empirical Software Engineering promotes the publication of industry-relevant research, to address the significant gap between research and practice.