EAGER: Automatic Identification of Bug Description Elements

当应用程序的行为不符合用户的预期或预期时，他们通常通过错误报告来传达问题，然后开发人员使用错误报告来识别问题并修复问题。要提交错误报告，用户可以使用问题跟踪器，这允许他们用自然语言编写他们遇到的问题的描述。错误报告中的一个问题是错误报告者和开发人员之间存在的感知差距。那些报告错误的人通常只了解应用程序的功能知识，即使他们自己也有开发经验，而软件开发人员拥有密切的代码级知识。因此，错误报告中的信息通常是不完整的、可能不正确的或难以理解的，这导致开发人员在尝试确定问题的真正来源时花费了过多的手动努力。该项目旨在自动分析自然语言中的错误描述，并识别与观察到的应用程序行为、预期行为以及描述用户在遇到问题时所做的步骤相对应的部分。自动识别错误描述的这些部分的能力很重要，因为它允许进行进一步的分析，以确定报告信息的质量，并支持开发人员解决问题。从长远来看，这一奖项将带来一种新型的错误报告系统，该系统能够自动使用户能够更好地描述他们注意到的问题行为，进而帮助开发人员更有效地解决软件问题。该项目还将支持定义缺陷报告的最佳实践，供世界各地的软件用户使用。该项目结合了来自自然语言处理、自动语篇分析和机器学习的成熟和高度创新的研究解决方案。具体地说，该项目解决了语句级别的话语语义，而不是错误报告级别，并解决了错误内容消除歧义的难题。此外，它还解决了确定错误描述元素之间的关系的问题，这对于支持未来关于自动错误复制的工作至关重要。主要的解决方案依赖于神经网络的使用，这需要对错误报告进行大量的手动编码。生成的带注释的错误报告集可用于支持该项目以外的研究，例如将自然语言测试序列或场景转换为全自动测试用例。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

When an application does not behave the way it is meant to or as expected by the users, they often communicate the problem via a bug-report, which is then used by developers to identify the problem and fix it. To submit a bug-report, users utilize issue-trackers, which allows them to write in natural language a description of the problem they encountered. One problem in bug reporting is the perception gap that exists between bug reporters and developers. Those who report a bug typically only have functional knowledge of an application, even if they have development experience themselves, whereas the software developers have intimate code-level knowledge. Consequently, information in bug-reports are often incomplete, potentially incorrect, or hard to comprehend, which leads to excessive manual effort spent by developers in trying to identify the real source of the problem. This project aims to automatically analyzing bug descriptions in natural language and identifying parts that correspond to the observed behavior of the application, the expected behavior, and the steps that describe what the user did when encountering the problem. The ability to automatically identify these parts of a bug description is important as it allows further analysis which will determine the quality of the reported information and supports developers in solving the problem. In the long run, this award will lead to a new type of bug reporting system that is able to automatically enable users to better describe the problem behaviors that they notice, and in turn, help developers address software problems more productively. The project will also support defining best practices in bug reporting, to be used by software users across the world.The project combines well-established and highly innovative research solutions from natural language processing, automated discourse analysis, and machine learning. Specifically, the project addresses discourse semantics at statement level, rather than bug report level, and solves the difficult challenge of bug content disambiguation. In addition, it also addresses the problem of identifying relationships between bug description elements, which is essential in supporting future work on automated bug reproduction. The main solution relies on the use of neural networks, which require a substantial amount of manual coding of bug reports. The resulting set of annotated bug reports could be used to support research beyond this project, such as, the translation of natural language test sequences or scenarios into fully automated test cases.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.