Improving Bug Triaging with High Confidence Predictions at Ericsson

Abstract

Correctly assigning bugs to the right developer or team, i.e. bug triaging, is a costly activity. A concerted effort at Ericsson has been done to adopt automated bug triaging to reduce development costs. In this work, we replicate the research approaches that have been widely used in the literature. We apply them on over 10k bug reports for 9 large products at Ericsson. We find that a logistic regression classifier including the simple textual and categorical attributes of the bug reports has the highest precision and recall of 78.09% and 79.00%, respectively. Ericsson's bug reports often contain logs that have crash dumps and alarms. We add this information to the bug triage models. We find that this information does not improve the precision and recall of bug triaging in Ericsson's context. Although our models perform as well as the best ones reported in the literature, a criticism of bug triaging at Ericsson is that the accuracy is not sufficient for regular use. We develop a novel approach where we only triage bugs when the model has high confidence in the triage prediction. We find that we improve the accuracy to 90%, but we can make predictions for 62% of the bug reports.