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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711608
• 关键词: 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bug是软件维护和开发活动中不可分割的一部分。由于时间和预算的限制，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。延迟修复缺陷的短期利益与长期将缺陷保留在系统中的后果之间的权衡被解释为缺陷债务。通常，缺陷债被定义为在当前版本中发现但未修复的任何类型的缺陷、失败或错误。在这个提议中，我的目标是调查关于缺陷债的三个研究问题：缺陷债的本金是多少？缺陷债的利息金额是多少？为了使利益最小化，在有限的时间内解决bug的最佳顺序是什么？为了回答这些研究问题，我建议将bug分为债务倾向bug和常规bug。利用常规错误训练基于knn回归的预测模型，对债务本金进行估计。我建议使用图论分析来计算利息。最后，建议采用强化学习技术，根据漏洞的债务金额对其进行优先排序。为了验证我提出的模型的可行性，我将使用从Mozilla Firefox项目和IBM RTC项目收集的bug报告进行实证研究。