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

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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.