While most software defects (i.e., bugs) are corrected and tested as part of the lengthy software development cycle, enterprise software vendors often have to release software products before all reported defects are corrected, due to deadlines and limited resources. A small number of these defects will be escalated by customers and they must be resolved immediately by the software vendors at a very high cost. In this paper, we develop an Escalation Prediction (EP) system that mines historic defect report data and predict the escalation risk of the defects for maximum net profit. More specifically, we first describe a simple and general framework to convert the maximum net profit problem to cost-sensitive learning. We then apply and compare several well-known cost-sensitive learning approaches for EP. Our experiments suggest that the cost-sensitive decision tree is the best method for producing the highest positive net profit and comprehensible results. The EP system has been deployed successfully in the product group of an enterprise software vendor.

Enterprise software vendors often have to release software products before all reported defects are corrected, and a small number of these reported defects will be escalated by customers whose businesses are seriously impacted. Escalated defects must be quickly resolved at a high cost by the software vendors. The total costs can be even greater, including loss of reputation, satisfaction, loyalty, and repeat revenue. In this paper, we develop an Escalation Prediction (EP) system to mine historic defect report data and predict the escalation risk of current defect reports for maximum ROI (Return On Investment). More specifically, we first describe a simple and general framework to convert the maximum ROI problem to cost-sensitive learning. We then apply and compare several best-known cost-sensitive learning approaches for EP. The EP system has produced promising results, and has been deployed in the product group of an enterprise software vendor. Conclusions drawn from this study also provide guidelines for mining imbalanced datasets and cost-sensitive learning.