TY - GEN
T1 - Maximum profit mining and its application in software development
AU - Ling, Charles X.
AU - Sheng, Victor S.
AU - Bruckhaus, Tilmann
AU - Madhavji, Nazim H.
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
KW - Cost-sensitive learning
KW - Data mining
KW - Escalation prediction
UR - http://www.scopus.com/inward/record.url?scp=33749562833&partnerID=8YFLogxK
U2 - 10.1145/1150402.1150530
DO - 10.1145/1150402.1150530
M3 - Conference contribution
AN - SCOPUS:33749562833
SN - 1595933395
SN - 9781595933393
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 929
EP - 934
BT - KDD 2006
PB - Association for Computing Machinery
T2 - KDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Y2 - 20 August 2006 through 23 August 2006
ER -