快速演化软件的在线自适应缺陷定位
Online Adaptable Bug Localization for Rapidly Evolving Software
摘要：
错误定位的目的是通过推荐与特定错误报告相关的程序元素来减少调试时间。到目前为止，研究人员主要通过应用不同的信息检索技术来解决这个问题，这些技术利用了给定错误报告和源代码之间的相似性。然而，随着现代软件开发趋向于软件变化和持续交付给用户的速度的增加，不能快速适应最新版本的软件的当前一代的错误定位技术变得不足。在本文中，我们提出了一种技术，在线错误定位，使快速更新的错误定位模型。更具体地说，我们提出了一个流错误本地化技术，基于一个合奏的在线主题模型，这是能够适应具体的（与明确的代码提到）和更抽象的错误报告。通过使用变更集（diffs）作为输入，而不是源代码的快照，模型自然地将缺陷预测和共同变更信息集成到其预测中。初步结果表明，所提出的方法提高了错误定位性能的42个56个评估项目，平均MAP提高了5.9%。
英文摘要：
Bug localization aims to reduce debugging time by recommending program elements that are relevant for a specific bug report. To date, researchers have primarily addressed this problem by applying different information retrieval techniques that leverage similarities between a given bug report and source code. However, with modern software development trending towards increased speed of software change and continuous delivery to the user, the current generation of bug localization techniques, which cannot quickly adapt to the latest version of the software, is becoming inadequate. In this paper, we propose a technique for online bug localization, which enables rapidly updatable bug localization models. More specifically, we propose a streaming bug localization technique, based on an ensemble of online topic models, that is able to adapt to both specific (with explicit code mentions) and more abstract bug reports. By using changesets (diffs) as the input instead of a snapshot of the source code, the model naturally integrates defect prediction and co-change information into its prediction. Initial results indicate that the proposed approach improves bug localization performance for 42 out of 56 evaluation projects, with an average MAP improvement of 5.9%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
