Recommending open source bug reports towards fixing bugs

As the open source community grows, more and more developers share code and discuss in the open source community. The capability and quality of software depends on the use of numerous users, developers code together, and interactions between them, such as suggestions and bug feedback. However, due to the current home office caused by the pandemic and global business development, developers are naturally physically separated on the planet. Their programming abilities also vary, and even experienced developers could make simple mistakes. Software development involves a variety of collaborations between developers, most of which are limited to the current projects being developed. However, because the open source community has a large code repository and open and diverse discussions, developers will find that their newly developed features are likely to have been implemented by similar programs, or newly discovered bugs are likely to have been discussed and resolved by similar projects. By mining and utilizing redundant or similar information between programs in the open source community, it can help developers reproduce defects and repair programs, thereby helping open source developers to improve the efficiency of software defect repair. In this thesis, using code and bug reports (a.k.a. GitHub issues), through some data processing and algorithm analysis, to mine potential correlations between data, filter and collect valuable information for bug fixes patches or relevant bug reports that help developers understand the cause and context of a defect, thereby improving the overall quality of the software. Specifically, a bug report marked as resolved or a pull request that has been merged represents a more detailed solution, which usually includes: the cause of the problem, steps to resolve it, logs, and even a fix patch, which provides an intuitive understanding of the defect the cause and impact. The difficulties of this work mainly lie in the extraction of bug feature and the similarity measure between bug reports, which affects the recall rate of the results and the ranking precision of candidate bug reports. Based on the above inspiration, aiming at the main difficulties, the main work of this thesis is as follows: （1）By analyzing the common features of UI at different levels in Android programs, combined with UI descriptions and codes in bug reports, A tool called BUGINE is proposed. It successfully retrieves bug reports for Android programs with similar UI components on GitHub, helping developers find and fix bugs, and get positive feedback. （2）In order to extend the work on Android to all programs written in the Java language, similar bug reports are collected that are actively associated by developers in the current open source community, and it is analyzed the characteristics of similar bug reports. Based on the analysis, a tool is introduced, CROSSFIX. It avoids the problem of BUGINE being limited to a local database. It constructs retrieval queries containing bug information to obtain candidate bug reports, and then uses pluggable offline similarity analysis and issue quality evaluation to improve bug report ranking performance. In addition, It has analyzed the results and compares the performance with GitHub and two tools based on Stack Overflow.

随着开源社区的发展，越来越多的开发者在开源社区共享代码和讨论。软件的能力和质量依赖于大量用户的使用，开发者共同开发，以及他们之间的互动，比如建议和错误反馈。然而，目前由于疫情导致的居家办公和全球化业务的开展，开发者天然地物理分隔开来。他们的程序编写能力也各不相同，即便有经验的开发者也会犯简单的错误。开发过程涉及开发者之间的多种合作，大多数合作都局限在开发者开发的当前项目中。但是，由于开源社区拥有庞大的代码仓库和包容多样的讨论，开发者会发现新开发的功能很可能已经被类似的程序实现，或者新发现的错误很可能已经被类似的项目讨论并解决。通过挖掘和利用开源社区中程序之间的冗余或相似信息，可以帮助开发者重现故障和修复程序，从而帮助开源开发者提高软件缺陷修复的效率。

  在本文中，介绍了利用代码和错误报告（又可被称作 GitHub issue），通过一些数据处理和算法分析，挖掘数据之间的潜在相关性，过滤和收集有重要价值的信息，以获得错误修复补丁或相关的错误报告，帮助开发人员了解缺陷的原因和背景，从而提高软件的整体质量。具体来说：标记为已解决的错误报告或已合并的合并请求代表了更详细的解决方案，其通常包括：问题的原因、解决步骤、参考资料，甚至是补丁，这些都可以直观地理解缺陷的前因后果。本文工作的难点主要在于缺陷特征提取与错误报告之间的相似性度量，这会影响结果的召回率和候选错误报告排名准确性。基于上述启发，针对主要难点，本文的主要工作如下：

（1）通过分析安卓程序中不同层次的用户界面的共同特征，结合错误报告中的 UI 描述和代码，提出了一个工具，BUGINE。在 GitHub 上成功检索具有类似 UI组件的安卓程序的错误报告，帮助开发者发现并解决错误，获得正面反馈。

（2）为了把在安卓上的工作推广到所有 Java 语言编写的程序上，本文收集了当前开源社区中开发者主动关联的相似错误报告，分析其中的相关特征。基于分析，重新设计了一个工具，CROSSFIX。它避免了 BUGINE 受限于有限本地数据库的问题，构造包含错误信息的检索请求来获取候选错误报告，然后使用可扩展的离线相似性分析和错误报告质量评估来提高错误报告的排名性能。此外，本文还对结果进行了分析，并与 GitHub 和基于 Stack Overflow 的工具的性能进行了比较。

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