CRII: SHF: RUI: Leroid: Bug Oracle and Environment Configuration Automation for Android Bug Report Reproduction

移动应用程序的开发人员严重依赖问题跟踪系统中的错误报告来重现故障。然而，故障重现的过程通常是由开发人员手动完成的，这使得错误的解决效率低下且成本高昂，特别是因为错误报告通常是用自然语言编写的。例如，bug报告可能会遗漏重现信息的必要步骤，或者包含不明确的讨论。然而，现有的方法远远不能解决bug报告再现中的所有问题。第一个开放的问题是bug oracle生成问题。当前的方法只能支持重现崩溃bug报告，而其他类型的bug仍然需要自动生成的oracle来确定bug是否被重现。另一个开放的问题是，重现环境配置（例如，Android SDK版本）仍然需要人工从bug报告中手动收集信息并配置它们。这个项目将开发一种新颖的方法，可以通过自动生成oracle和配置环境来支持复制更多类型的Android错误报告。这项研究允许软件开发人员更快地重现错误报告并修复错误，这将提高软件质量。反过来，这将有利于我们的现代社会，这在很大程度上依赖于软件。此外，该项目通过整合课程，培训来自代表性不足的少数群体的学生，以及组织旨在提高K-12女孩对stem相关职业兴趣的暑期讲习班，为多样化的劳动力做出了贡献。为了通过支持oracle生成和环境配置来大幅改进自动错误报告再现，将开发新的技术和工具来解决以下三个重要挑战：(1)在错误报告中提取观察/期望和环境信息句子，(2)生成可以确定目标错误是否已被触发的oracle断言代码，(3)配置再现环境。在这个项目中，基于技术组合的技术和工具包括静态/动态程序分析、深度学习、自然语言处理和网络爬行。该方法将嵌入到用户界面中，并通过公共错误报告和行业协作进行广泛评估。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。

Developers of mobile apps rely heavily on bug reports in issue-tracking systems to reproduce failures. However, the process of failure reproduction is often manually done by developers, making the resolution of bugs inefficient and costly, especially since bug reports are often written in natural language. For example, bug reports may miss necessary steps-to-reproduce information or include unclear discussions. However, existing approaches are far from solving all problems in bug report reproduction. The first open problem is the bug oracle generation problem. Current approaches can only support reproducing crash bug reports while other types of bugs still need automatic-generated oracles to determine whether the bug has been reproduced or not. Another open problem is that reproducing environment configurations (e.g., Android SDK version) still requires human effort to manually collect information from bug reports and configure them. This project will develop a novel approach that can support reproducing more bug types of Android bug reports by automatically generating oracles and configuring environments. This research allows software developers to more quickly reproduce bug reports and to fix bugs, which will increase software quality. This, in turn, will benefit our modern society, which greatly relies on software. Additionally, this project contributes toward a diverse workforce, through integration with course curricula, training of students from underrepresented minority groups, and organizing summer workshops aimed at increasing K-12 girls’ interest in STEM-related careers.To substantially improve automated bug report reproduction by supporting oracle generation and environment configuration, novel techniques and tools will be developed that address three important challenges: (1) extract observation/expectation and environment information sentences in a bug report, (2) generate oracle assertion codes that can determine whether the targeted bug has been triggered, (3) configure reproducing environment. In this project, techniques and tools based on a combination of techniques include static/dynamic program analyses, deep learning, natural language processing, and web crawling. The approach will be embedded in a user interface and evaluated extensively on public bug reports and through industrial collaborations.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.