Master Thesis Defense: Aindrila Sarkar
Speaker: Aindrila Sarkar
Supervisor: Dr. P. Rigby
Drs. Y.-G. Gueheneuc, J. Rilling, A. Hanna (Chair)
Title: Bug Triaging with High Confidence Predictions
Date: Thursday, November 28, 2019
Place: EV 12.163
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. We also perform a case study on Eclipse bug reports. In this work, we replicate the research approaches that have been widely used in the literature including Fixer Cache. We apply them on over 10k bug reports for 9 large products at Ericsson and 2 large Eclipse products containing 21 components. We find that a logistic regression classifier including the simple textual and categorical attributes of the bug reports has the highest accuracy of 79.00% and 46% on Ericsson and Eclipse bug reports 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 accuracy of bug triaging in Ericsson’s context. Eclipse bug reports contain the stack traces that we add to the bug triaging model. Stack traces are only present in 8% of bug reports and do not improve the triage accuracy.
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% at Ericsson and 70% at Eclipse, but we can make predictions for 62% and 25% of the total Ericsson and Eclipse bug reports, respectively.