TY - GEN
T1 - Predicting defect priority based on neural networks
AU - Yu, Lian
AU - Tsai, Wei Tek
AU - Zhao, Wei
AU - Wu, Fang
N1 - Funding Information:
This work is partially supported by the National Science Foundation of China (No.60973001), IBM China Research Lab (No.20090101), and U.S. Department of Education FIPSE project. The authors would thank Yang Cao, Jingtao Zhao and Jun Ying for working on data collection and the empirical study described in this paper.
Funding Information:
Acknowledgments. This work is partially supported by the National Science Foundation of China (No.60973001), IBM China Research Lab (No.20090101), and U.S. Department of Education FIPSE project. The authors would thank Yang Cao, Jingtao Zhao and Jun Ying for working on data collection and the empirical study described in this paper.
PY - 2010
Y1 - 2010
N2 - Existing defect management tools provide little information on how important/urgent for developers to fix defects reported. Manually prioritizing defects is time-consuming and inconsistent among different people. To improve the efficiency of troubleshooting, the paper proposes to employ neural network techniques to predict the priorities of defects, adopt evolutionary training process to solve error problems associated with new features, and reuse data sets from similar software systems to speed up the convergence of training. A framework is built up for the model evaluation, and a series of experiments on five different software products of an international healthcare company to demonstrate the feasibility and effectiveness.
AB - Existing defect management tools provide little information on how important/urgent for developers to fix defects reported. Manually prioritizing defects is time-consuming and inconsistent among different people. To improve the efficiency of troubleshooting, the paper proposes to employ neural network techniques to predict the priorities of defects, adopt evolutionary training process to solve error problems associated with new features, and reuse data sets from similar software systems to speed up the convergence of training. A framework is built up for the model evaluation, and a series of experiments on five different software products of an international healthcare company to demonstrate the feasibility and effectiveness.
KW - Defect priority
KW - artificial neural network
KW - attribute dependency
KW - convergence of training
KW - evolutionary training
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U2 - 10.1007/978-3-642-17313-4_35
DO - 10.1007/978-3-642-17313-4_35
M3 - Conference contribution
AN - SCOPUS:78650209732
SN - 3642173128
SN - 9783642173127
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 356
EP - 367
BT - Advanced Data Mining and Applications - 6th International Conference, ADMA 2010, Proceedings
T2 - 6th International Conference on Advanced Data Mining and Applications, ADMA 2010
Y2 - 19 November 2010 through 21 November 2010
ER -