Reference number : GC-2017-Q1-02575, amendment number : , agreement type : G, recipient legal name : Bener, Ayse (Ryerson University), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : 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., program purpose french : 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., agreement title english : Towards Measuring Defect Debt and Developing a Recommender System for Their Prioritization, agreement title french : Towards Measuring Defect Debt and Developing a Recommender System for Their Prioritization, agreement number : RGPIN, agreement value : 130000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada