Toward Measuring Defect Debt and Developing a Recommender system for their prioritization

Software development managers make a release decision without fully resolving the defects from current and previous releases due to tight deadlines. Deferring the defects would accumulate a tremendous amount of technical debt in the system. Typically, the defect debts are defined as the type of defect that should be fixed. However, due to competing priorities and the limited amount of time and resources, they would be postponed to the next release. In order to aid practitioners ,who make release decisions, to observe the amount of debt, there is a need for quantifying the defect debt. Software bug repositories roughly provide us with information about the amount of time the defect debt exist in the system, the time the defects are resolved and the severity of the defect. We suggest categorizing the defect into the regular defect and debt prone defect by analyzing this information. Afterwards, we compare the regular defect and debt-prone defect to determine the principal, interest and interest probability of defect debt. We also propose the reinforcement learning for scheduling which defect debt needs to be paid and when they need to be paid.