George Kour¹, Shaul Strachan² and Raz Regev², ¹Hewlett Packard Labs, Israel and ²Hewlett Packard Enterprise, Israel

The problem of accurately predicting handling time for software defects is of great practical importance. However, it is difficult to suggest a practical generic algorithm for such estimates, due in part to the limited information available when opening a defect and the lack of a uniform standard for defect structure. We suggest an algorithm to address these challenges that is implementable over different defect management tools. Our algorithm uses machine learning regression techniques to predict the handling time of defects based on past behaviour of similar defects. The algorithm relies only on a minimal set of assumptions about the structure of the input data. We show how an implementation of this algorithm predicts defect handling time with promising accuracy results.

Defects, Bug-fixing time, Effort estimation, Software maintenance, Defect prediction, Data mining