Identifying and fixing of defects is part of software maintenance activities. However, due to tight budget and schedule, software development teams may not resolve all the existing bugs in the issue tracking systems. 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. Therefore, there is a necessity for software project managers to measure and monitor defect debts. 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. We proposed KNN-regression to predict the standard time for bug fixing 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 ( R  2 ) ranges between 74% to 85 %.
['Shirin Akbarinasaji', 'Ayse Basar Bener', 'Atakan Erdem']
Measuring the principal of defect debt