Towards Measuring Defect Debt and Developing a Recommender System for Their Prioritization

衡量缺陷债务并开发优先级推荐系统

• 批准号: RGPIN-2017-05312
• 负责人: Bener, Ayse
• 金额: $ 1.89万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=681723
• 关键词: Towards; Measuring; Defect; Debt; Developing; Towards; Measuring; Defect; Debt; Developing

Software bugs are an inextricable part of software maintenance and development activities. Due to limited time and tight budget constraints, the software development team is not able to fully resolve all the bugs that exist in the issue tracking system. The trade-off between short-term benefit of postponing the fixing of defect and the consequence of keeping the bug in the system in the long-term is interpreted as a defect debt. Typically, defect debt is defined as any kind of defect, failure or bug that is found but not fixed in the current release. In this proposal, I aim to investigate three research questions regarding the defect debt: What is the principal amount of defect debt? What is the interest amount for defect debt? What is the optimal sequence for resolving the bugs in a limited time in order to minimize the interest? In order to answer these research questions, I propose to categorize the bugs into debt prone bugs and regular bugs. The regular bugs are used to train the prediction model based on KNN-regression for estimating the principal of the debt. I propose to use graph theory analysis in order to calculate the interest. Eventually, reinforcement learning technique is recommended to prioritize the bugs based on their debt amount. In order to validate the feasibility of my proposed model, I will perform an empirical study using the bug reports collected from Mozilla Firefox project and IBM RTC project.

软件错误是软件维护和开发活动的不可忽视的一部分。由于时间有限和预算限制，软件开发团队无法完全解决问题跟踪系统中存在的所有错误。推迟缺陷的固定和长期将错误保留在系统中的后果之间的权衡是将缺陷债务解释为缺陷债务。通常，缺陷债务定义为当前版本中发现但未固定的任何形式的缺陷，故障或错误。在此提案中，我的目标是调查有关缺陷债务的三个研究问题：缺陷债务的主要数量是多少？缺陷债务的利息金额是多少？为了最大程度地降低利息，在有限时间内解决错误的最佳序列是什么？为了回答这些研究问题，我建议将这些错误分类为容易发生错误和常规错误。常规错误用于基于KNN回归培训预测模型，以估计债务本金。我建议使用图理论分析来计算兴趣。最终，建议使用强化学习技术根据其债务金额优先考虑这些错误。为了验证我提出的模型的可行性，我将使用Mozilla Firefox项目和IBM RTC项目收集的错误报告进行实证研究。