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There are 10ts of change request reports without being closed for a 10ng time in the Issue tracking system, which increases the likelihood of developers to click and read the reports again and agai凡 It seriously affects the implementation of management task of software requirements and the feedback experiences of users. The accurate and instant prediction of closed-probability or importance of these reports can improve the quality of the task of software mainte nance. Severa1 metrics were defined to measure the feature of change request , and Logistic regression prediction mode1 was built by using these best predictive metrics on datasets for training. Then experiments which app1ied proposed method were performed on datasets of testing which contain 20 SourceForge projects , and achieved a result that average recall of 95% and average FPR (False Positive Rate) of 14%. Ana1ysis of experimenta1 result shows that the proposed method can achieve a good prediction performance on datasets of testing , and closed-percentage or size of change re quests report doesn' t affect the performance of the mode1 , and some features of change request report can be used to predict its closed-probabi1ity in the next version.

面向Issue跟踪系统的变更请求报告关闭可能性预测 (Prediction on Closed-probability of Change Request Report for Issue Tracking System).