Predicting Defect Priority Based on Neural Networks
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.
Defect priority evolutionary trainingartificial neural network attribute dependency convergence of training
Lian Yu Wei-Tek Tsai Wei Zhao Fang Wu
School of Software and Microelectronics, Peking University, Beijing, 102600, PRC Department of Computer Science and Engineering, Arizona State University, USA Department of Computer IBM China Research Lab, Beijing, 100193, PRC
国际会议
6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)
重庆
英文
356-367
2010-11-19（万方平台首次上网日期，不代表论文的发表时间）