Predicting Bug-Fixing Time with Machine Learning - A Collaborative Filtering Approach

Abstract

ABSTRACTPredicting bug-fixing time helps software managers and teams prioritizetasks, allocations and costs in software projects. In literature,machine learning (ML) models have been proposed to predict bugfixingtime. One of features highlighted by studies is the reporter(the person who open the bug) has positive influence in the timeto resolve a bug. In this way, this paper answers the following researchquestion: How does a collaborative filtering approach performin predicting bug-fixing time compared to the supervised machinelearning approaches? In order to answer this question we performedan experiment using collaborative filtering approach to recommendthe bugs that are fast to be resolved in two open software projects.We compare our proposed approach with the ML approach relatedto the literature. As a result, the collaborative filtering approachoutperforms the supervised ML achieving an F-measure of 74%while the supervised ML achieved 66%. The collaborative filteringapproach showed to be a new perspective to predict bug-fixing timein software projects focusing the prediction on the reporter.