Automatic, high accuracy prediction of reopened bugs
Automatic, high accuracy prediction of reopened bugs
复制标题
自动、高精度地预测重新打开的错误
DOI：
10.1007/s10515-014-0162-2
复制
发表时间：
2014-09
影响因子：
3.4
通讯作者：
Bo Zhou
中科院分区：
文献类型：
--
作者：
Xin Xia;David Lo;Emad Shihab;Xinyu Wang;Bo Zhou
修复错误是软件开发生命周期中最耗时，最昂贵的活动之一。通常，在由Triage团队验证的错误跟踪系统中报告了错误，并分配给某人进行修复，并最终进行了验证和关闭。何
Bug fixing is one of the most time-consuming and costly activities of the software development life cycle. In general, bugs are reported in a bug tracking system, validated by a triage team, assigned for someone to fix, and finally verified and closed. However, in some cases bugs have to be reopened. Reopened bugs increase software maintenance cost, cause rework for already busy developers and in some cases even delay the future delivery of a software release. Therefore, a few recent studies focused on studying reopened bugs. However, these prior studies did not achieve high performance (in terms of precision and recall), required manual intervention, and used very simplistic techniques when dealing with this textual data, which leads us to believe that further improvements are possible. In this paper, we proposeReopenPredictor, which is an automatic, high accuracy predictor of reopened bugs.ReopenPredictoruses a number of features, including textual features, to achieve high accuracy prediction of reopened bugs. As part ofReopenPredictor, we propose two algorithms that are used to automatically estimate various thresholds to maximize the prediction performance. To examine the benefits ofReopenPredictor, we perform experiments on three large open source projects—namely Eclipse, Apache HTTP and OpenOffice. Our results show thatReopenPredictoroutperforms prior work, achieving a reopened F-measure of 0.744, 0.770, and 0.860 for Eclipse, Apache HTTP and OpenOffice, respectively. These results correspond to an improvement in the reopened F-measure of the method proposed in the prior work by Shihab et al. by 33.33, 12.57 and 3.12 % for Eclipse, Apache HTTP and OpenOffice, respectively.
登录
查看更多内容
影响因子：
7.4
作者：
Thomas Zimmermann;Rahul Premraj;Nicolas Bettenburg;Sascha Just;Adrian Schröter;Cathrin Weiss
通讯作者：
Thomas Zimmermann;Rahul Premraj;Nicolas Bettenburg;Sascha Just;Adrian Schröter;Cathrin Weiss
DOI：
10.1109/msr.2010.5463284
发表时间：
2010-05
期刊：
2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)
影响因子：
--
作者：
Ahmed Lamkanfi;S. Demeyer;E. Giger;Bart Goethals
通讯作者：
Ahmed Lamkanfi;S. Demeyer;E. Giger;Bart Goethals
DOI：
10.1109/icse.2007.32
发表时间：
2007-05
期刊：
29th International Conference on Software Engineering (ICSE'07)
影响因子：
--
作者：
P. Runeson;Magnus Alexandersson;Oskar Nyholm
通讯作者：
P. Runeson;Magnus Alexandersson;Oskar Nyholm
影响因子：
4.1
作者：
Emad Shihab;Akinori Ihara;Yasutaka Kamei;Walid M. Ibrahim;M. Ohira;Bram Adams;A. Hassan;Ken-ichi Matsumoto
通讯作者：
Emad Shihab;Akinori Ihara;Yasutaka Kamei;Walid M. Ibrahim;M. Ohira;Bram Adams;A. Hassan;Ken-ichi Matsumoto
DOI：
10.1109/csmr-wcre.2014.6747166
发表时间：
2014-02
期刊：
2014 Software Evolution Week - IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE)
影响因子：
--
作者：
Annibale Panichella;Rocco Oliveto;A. D. Lucia
通讯作者：
Annibale Panichella;Rocco Oliveto;A. D. Lucia