Collaborative Research: SHF: Medium: Improving Software Quality by Automatically Reproducing Failures from Bug Reports

协作研究：SHF：中：通过自动重现错误报告中的故障来提高软件质量

• 批准号: 2403747
• 负责人: Tingting Yu
• 金额: $ 61万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-15 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2403747&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Medium; Improving

The large demand for mobile device based services emphasizes the importance of software quality for mobile applications (apps). Because testing and other verification techniques cannot generally detect all bugs, it is common for app users to experience failures during normal operation. Developers rely on users reporting these bugs in issue-tracking systems to understand and resolve the failures. However, in current practice the process of reproducing the reported bugs must be done manually by developers, making app maintenance inefficient. This project will develop a family of techniques and tools that can extract relevant information for steps to reproduce from bug reports, dynamically search for reproducing sequences in the app to successfully reproduce the reported failure, and improve the quality of information used for failure reproduction. The products of these research initiatives will be used in several diverse software-engineering applications, including bug-report mining, bug-report reproduction, dynamic GUI exploration, and static analysis. This project aims to transform the way developers debug, reproduce, and understand software bugs from bug reports, and thus lead to more reliable software. The overall goal of this project is to improve the process of resolving mobile-app failures by automating the task of reproducing, creating, and generating tests from bug reports. The analytical components of this project involve: (1) a novel approach for accurately extracting steps to reproduce and their contextual information, (2) a novel GUI exploration technique to automatically search for reproducing event sequences, (3) a novel static analysis to help the reproduction search avoid locally-optimal but globally sub-optimal searches and lead to better overall and more successful reproductions. The integration of static and dynamic analyses, machine learning, and natural-language processing constitutes a novel reproduction framework that promises to provide not only practical solutions, but also theoretical advances in the field of software mining. The techniques developed in this project will be evaluated for effectiveness via large-scale experiments on real-world mobile apps.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.

对基于移动设备的服务的巨大需求强调了软件质量对移动应用程序（应用程序）的重要性。 由于测试和其他验证技术通常无法检测到所有错误，因此应用程序用户在正常操作期间经历失败是常见的。开发人员依靠在问题跟踪系统中报告这些错误的用户来理解和解决故障。但是，在当前实践中，必须通过开发人员手动完成报告的错误的过程，从而使应用程序维护效率低下。 该项目将开发一个技术和工具系列，可以从错误报告中提取相关信息，以动态地搜索应用程序中的复制序列，以成功地重现报告的故障，并提高用于失败复制的信息质量。这些研究计划的产品将用于多种不同的软件工程应用程序，包括错误报告挖掘，错误报告复制，动态GUI探索和静态分析。该项目旨在改变开发人员从错误报告中调试，复制和理解软件错误的方式，从而导致更可靠的软件。该项目的总体目标是通过自动化从错误报告中复制，创建和生成测试的任务来改善解决移动应用失败的过程。该项目的分析组成部分涉及：（1）一种新颖的方法，用于准确提取繁殖及其上下文信息的步骤，（2）一种新型的GUI探索技术，可自动寻找复制事件序列，（3）一种新颖的静态分析，以帮助繁殖搜索避免本地范围，但要避免本地进行全球范围的搜索，并在全球范围内搜索更好，并获得了更好的整体范围，并获得了更高的整体范围。静态和动态分析，机器学习和自然语言处理的集成构成了一个新颖的复制框架，该框架有望不仅提供实际解决方案，而且还提供了软件挖掘领域的理论进步。该项目中开发的技术将通过对现实世界移动应用程序的大规模实验进行评估。该奖项反映了NSF的法定任务，并使用基金会的智力优点和更广泛的影响评估标准，认为值得通过评估来获得支持。