EAGER: Automatic Identification of Bug Description Elements

EAGER：自动识别错误描述元素

• 批准号: 1848608
• 负责人: Andrian Marcus
• 金额: $ 20万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1848608&HistoricalAwards=false
• 关键词: EAGER; Automatic; Identification; Bug; Description

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.

当一个应用程序的行为不符合用户的预期时，他们通常会通过bug报告来传达问题，然后开发人员使用bug报告来识别问题并修复它。为了提交bug报告，用户使用问题跟踪器，它允许他们用自然语言描述他们遇到的问题。bug报告中的一个问题是bug报告者和开发人员之间存在的感知差距。那些报告bug的人通常只具有应用程序的功能知识，即使他们自己有开发经验，而软件开发人员则具有详细的代码级知识。因此，错误报告中的信息通常是不完整的，可能是不正确的，或者难以理解的，这导致开发人员在试图识别问题的真实的来源时花费了过多的手动工作。该项目旨在自动分析自然语言中的错误描述，并识别与应用程序的观察行为，预期行为以及描述用户遇到问题时所做的步骤相对应的部分。自动识别bug描述的这些部分的能力很重要，因为它允许进一步分析，这将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，该奖项将导致一种新型的错误报告系统，能够自动使用户更好地描述他们注意到的问题行为，从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错误报告的最佳实践，供世界各地的软件用户使用。该项目结合了自然语言处理，自动话语分析和机器学习等成熟和高度创新的研究解决方案。具体来说，该项目在语句级别而不是错误报告级别解决了话语语义，并解决了错误内容消歧的困难挑战。此外，它还解决了识别错误描述元素之间的关系的问题，这是必不可少的，在支持未来的工作自动错误再现。主要的解决方案依赖于神经网络的使用，这需要大量的bug报告的手动编码。由此产生的一组注释错误报告可用于支持本项目以外的研究，例如将自然语言测试序列或场景翻译为全自动测试用例。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Using bug descriptions to reformulate queries during text-retrieval-based bug localization

• DOI: 10.1007/s10664-018-9672-z
• 发表时间: 2019-01
• 期刊: Empirical Software Engineering
• 影响因子: 4.1
• 作者: Oscar Chaparro;Juan Manuel Florez;Andrian Marcus

Predicting Licenses for Changed Source Code

预测更改源代码的许可证

• DOI: 10.1109/ase.2019.00070
• 发表时间: 2019
• 期刊: Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering
• 作者: Liu, Xiaoyu;Huang, LiGuo;Ge, Jidong;Ng, Vincent
• 通讯作者: Ng, Vincent

Assessing the quality of the steps to reproduce in bug reports

• DOI: 10.1145/3338906.3338947
• 发表时间: 2019-06
• 期刊: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
• 作者: Oscar Chaparro;Carlos Bernal-Cárdenas;Jing Lu;Kevin Moran;Andrian Marcus;M. D. Penta;D. Poshyvanyk;Vincent Ng

Combining Query Reduction and Expansion for Text-Retrieval-Based Bug Localization

• DOI: 10.1109/saner50967.2021.00024
• 发表时间: 2021-03
• 期刊: 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)
• 作者: Juan Manuel Florez;Oscar Chaparro;Christoph Treude;Andrian Marcus

Reformulating Queries for Duplicate Bug Report Detection

• DOI: 10.1109/saner.2019.8667985
• 发表时间: 2019-02
• 期刊: 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER)
• 作者: Oscar Chaparro;Juan Manuel Florez;Unnati Singh;Andrian Marcus