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

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(App)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APP用户在正常运行期间遇到故障是很常见的。开发人员依赖报告问题跟踪系统中的这些错误的用户来了解和解决故障。然而，在目前的实践中，复制报告的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，可以从错误报告中提取用于重现步骤的相关信息，动态搜索应用程序中的重现序列以成功重现报告的故障，并提高用于故障重现的信息质量。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，包括错误报告挖掘、错误报告复制、动态图形用户界面探索和静态分析。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，从而产生更可靠的软件。该项目的总体目标是通过自动化复制、创建和从错误报告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)精确提取复制步骤及其上下文信息的新方法；(2)自动搜索复制事件序列的新的图形用户界面探索技术；(3)帮助复制搜索避免局部最优但全局次优搜索并导致更好的整体和更成功的复制的新的静态分析。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复制框架，它不仅有望提供实用的解决方案，而且还将在软件挖掘领域提供理论上的进步。在这个项目中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。

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.