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中遇到失败是很常见的。开发人员依靠用户在问题跟踪系统中报告这些错误来理解和解决故障。然而，在目前的实践中，重现报告错误的过程必须由开发人员手动完成，这使得应用程序维护效率低下。该项目将开发一系列技术和工具，可以从bug报告中提取相关信息，用于复制步骤，动态搜索应用程序中的复制序列，以成功复制报告的故障，并提高用于故障复制的信息质量。这些研究计划的产品将用于几个不同的软件工程应用程序，包括bug报告挖掘、bug报告再现、动态GUI探索和静态分析。该项目旨在改变开发人员调试、复制和从错误报告中理解软件错误的方式，从而产生更可靠的软件。该项目的总体目标是通过自动化从bug报告中重现、创建和生成测试的任务，来改进解决移动应用程序故障的过程。该项目的分析部分包括：(1)一种准确提取复制步骤及其上下文信息的新方法，(2)一种新的GUI探索技术，用于自动搜索复制事件序列，(3)一种新的静态分析，有助于复制搜索避免局部最优但全局次优搜索，从而实现更好的整体和更成功的复制。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的再现框架，它不仅有望提供实用的解决方案，而且还有望在软件挖掘领域取得理论进展。本项目开发的技术将通过在真实世界的移动应用程序上进行大规模实验来评估其有效性。该奖项反映了美国国家科学基金会的法定使命，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。