Collaborative Research: SHF: Medium: Bug Report Management 2.0

软件系统经常存在导致意外结果的缺陷。最终用户通过问题报告系统报告这些意外结果，以便软件工程师可以识别和修复相关缺陷，以提高系统质量。在报告时，用户可以使用自然语言或截图、视频等图形信息来描述软件问题。不幸的是，日常终端用户很少(如果有的话)接受过报告软件问题的培训。因此，他们经常提交不完整或难以理解的报告，导致花费过多的精力来解决问题，甚至无法识别和修复潜在的缺陷。此外，现有的问题报告制度无法执行报告的质量标准，当记者提交不合格的信息时，也无法向他们提供反馈。该项目将开发一个新的问题报告系统，使用户能够通过与自动化软件代理的对话以交互方式描述软件问题，而不是被动地编写报告，没有反馈和质量评估。软件代理将自动将对话转换为高质量的问题报告，并将其传输给软件工程师。拟议的系统将允许软件工程师更快地管理和修复缺陷，从而产生更高质量的软件系统。该项目还将制作和传播关于报告软件问题的最佳做法的教育材料。这些材料旨在纳入各级教育中现有的计算机扫盲课程。此外，该项目将侧重于从传统上代表性较低的类别招聘和留住计算机科学专业的学生。该项目围绕三个具体目标展开。首先，它将开发用于缺陷报告的自动分析和质量评估的新技术。这一构成部分将采用并建立自动化话语分析、动态程序分析和计算机视觉技术。第二，通过互动机制提高问题报告的质量。这一积极主动的报告解决方案将通过对经验软件工程、人机交互、自动文本分析和高级机器学习的交叉研究来开发。这种以对话为基础的新报告有望成为报告各种软件问题的标准方法。最后，该项目将利用通过交互式报告系统创建的高质量报告，开发更高效和有效的自动缺陷复制和重复检测技术。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。

Software systems often suffer from defects that lead to unexpected results. End users report these unexpected results via issue-reporting systems so that software engineers can identify and fix the related defects to improve the quality of the system. When reporting, users can describe the software problems using natural language or graphical information such as screenshots and videos. Unfortunately, everyday end users are rarely, if ever, trained in reporting software issues. In consequence, they often submit reports that are incomplete or hard to understand, resulting in excessive effort spent addressing the problems, or even the inability for the underlying defects to be identified and fixed. In addition, existing issue-reporting systems are unable to enforce quality standards for reports and fail to provide feedback to the reporters when they submit substandard information. This project will develop a novel-issue reporting system that will allow users to describe software problems interactively, through a dialogue with an automated software agent, rather than writing reports passively, with no feedback and quality assessment. The software agent will automatically convert the conversations into high-quality issue reports, which will be transmitted to the software engineers. The proposed system will allow software engineers to manage and fix defects faster, leading to higher-quality software systems. The project will also produce and disseminate educational material on best practices in reporting software problems. These materials are intended to be integrated into existing computer-literacy courses at all levels of education. In addition, the project will focus on recruiting and retaining computer science students from traditionally underrepresented categories. The project is centered on three specific goals. First, it will develop novel techniques for the automated analysis and quality assessment of defect reports. This component will adapt and build upon techniques for automated discourse analysis, dynamic program analysis, and computer vision. Second, it will improve the quality of issue reports through interactive mechanisms. This proactive reporting solution will be developed through cross-cutting research on empirical software engineering, human-computer interaction, automated text analysis, and advanced machine learning. This new dialogue-based reporting is expected to become the standard method by which many kinds of software issues will be reported. Finally, the project will develop more efficient and effective techniques for automated defect reproduction and duplicate detection, leveraging the high-quality reports created via the interactive reporting system.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.