论文标题
ADPTRIAGE：BUG Triage的近似动态编程
ADPTriage: Approximate Dynamic Programming for Bug Triage
论文作者
论文摘要
在任何软件开发项目中，Bug Tiaring是一项至关重要的任务。它需要三名列表浏览一个开放错误的列表，确定是否需要解决每个错误，如果是，则该开发人员应将其修复。但是，问题跟踪系统（ITS）中的手动错误分配仅提供有限的解决方案，并且在三名三名必须处理大量错误报告时很容易失败。在自动化任务期间，ITS中有多种不确定性来源，应精心解决。在这项研究中，我们为在线错误分类任务开发了马尔可夫决策过程（MDP）模型。除了基于优化的近视技术外，我们还提供了一个基于ADP的Bug Triage解决方案，称为ADPtriage，该解决方案具有反映错误到达和开发人员时间表的下游不确定性的能力。具体而言，在不考虑基本的随机过程上，该技术可以实时对错误分配的决策，同时考虑开发人员的专业知识，错误类型和错误修复时间。我们的结果在分配准确性和固定时间方面表明了对近视方法的显着改善。我们还证明了模型的经验收敛，并使用各种模型参数进行灵敏度分析。因此，这项工作构成了解决Bug Triage解决方案的不确定性的重要一步
Bug triaging is a critical task in any software development project. It entails triagers going over a list of open bugs, deciding whether each is required to be addressed, and, if so, which developer should fix it. However, the manual bug assignment in issue tracking systems (ITS) offers only a limited solution and might easily fail when triagers must handle a large number of bug reports. During the automated assignment, there are multiple sources of uncertainties in the ITS, which should be addressed meticulously. In this study, we develop a Markov decision process (MDP) model for an online bug triage task. In addition to an optimization-based myopic technique, we provide an ADP-based bug triage solution, called ADPTriage, which has the ability to reflect the downstream uncertainty in the bug arrivals and developers' timetables. Specifically, without placing any limits on the underlying stochastic process, this technique enables real-time decision-making on bug assignments while taking into consideration developers' expertise, bug type, and bug fixing time. Our result shows a significant improvement over the myopic approach in terms of assignment accuracy and fixing time. We also demonstrate the empirical convergence of the model and conduct sensitivity analysis with various model parameters. Accordingly, this work constitutes a significant step forward in addressing the uncertainty in bug triage solutions
