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

The Problem

The tight schedule of the software projects make the software managers to deliver the software while addressing the defects form current and previous release. Deferring the defects would accumulate 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 limited amount of time and resource, they will be postponed to the next release. In order to aid practitioners who make release decisions to observe amount of debt , there is a need for quantifying the defect debt. Software bug repositories provides us with roughly information about amount of the time the defect debt exists in the system, the time defects are resolved and the severity of defect. Our proposed strategy is to categorize the defect into regular defect and deb prone defect. Then, we compare the regular defect and debt-prone defect to determine the principal, interest and interest probability of defect debt.

Our Recent Paper on ‘Measuring the Principal of Defect Debt’:

The trade-off between the short-term benefit of postponing bug fixing activities and long-term consequence of delaying those activities is interpreted as defect debt. The accumulation of defect debt in the issue tracking system might cause system bankruptcy. In this study, we categorized the bugs into regular bugs and debt prone bugs and employed the historical data from regular bugs to train a prediction model for estimating the principal for debt prone bugs. The principal for the regular bug is equivalent to a standard amount of time to fix them. There are studies in
the literature that predict bug fixing time as a classification problem. We proposed KNN-regression to predict the standard time for bug xing time (principal). We performed an empirical study on both commercial and open source projects to investigate the feasibility of our model. The results showed that KNN-regression outperformed the simple linear regression with the predictive power (R2) ranges between 74% to 85 %.