document: Maximum profit mining and its application in software development 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 .
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