A Comparative Study of Supervised Learning Algorithms for Re-opened Bug Prediction

Bug fixing is a time-consuming and costly job which is performed in the whole life cycle of software development and maintenance. For many systems, bugs are managed in bug management systems such as Bugzilla. Generally, the status of a typical bug report in Bugzilla changes from new to assigned, ver...

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Main Authors: XIA, Xin, LO, David, WANG, Xinyu, YANG, Xiaohu, LI, Shanping, SUN, Jianling
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Language:English
Published: Institutional Knowledge at Singapore Management University 2013
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Online Access:https://ink.library.smu.edu.sg/sis_research/1688
http://doi.ieeecomputersociety.org/10.1109/CSMR.2013.43
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spelling sg-smu-ink.sis_research-26872015-11-23T02:31:04Z A Comparative Study of Supervised Learning Algorithms for Re-opened Bug Prediction XIA, Xin LO, David WANG, Xinyu YANG, Xiaohu LI, Shanping SUN, Jianling Bug fixing is a time-consuming and costly job which is performed in the whole life cycle of software development and maintenance. For many systems, bugs are managed in bug management systems such as Bugzilla. Generally, the status of a typical bug report in Bugzilla changes from new to assigned, verified and closed. However, some bugs have to be reopened. Reopened bugs increase the software development and maintenance cost, increase the workload of bug fixers, and might even delay the future delivery of a software. Only a few studies investigate the phenomenon of reopened bug reports. In this paper, we evaluate the effectiveness of various supervised learning algorithms to predict if a bug report would be reopened. We choose 7 state-of-the-art classical supervised learning algorithm in machine learning literature, i.e., kNN, SVM, Simple Logistic, Bayesian Network, Decision Table, CART and LWL, and 3 ensemble learning algorithms, i.e., AdaBoost, Bagging and Random Forest, and evaluate their performance in predicting reopened bug reports. The experiment results show that among the 10 algorithms, Bagging and Decision Table (IDTM) achieve the best performance. They achieve accuracy scores of 92.91% and 92.80%, respectively, and reopened bug reports F-Measure scores of 0.735 and 0.732, respectively. These results improve the reopened bug reports F-Measure of the state-of-the-art approaches proposed by Shihab et al. by up to 23.53%. 2013-03-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/1688 info:doi/10.1109/CSMR.2013.43 http://doi.ieeecomputersociety.org/10.1109/CSMR.2013.43 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Software Engineering
spellingShingle Software Engineering
XIA, Xin
LO, David
WANG, Xinyu
YANG, Xiaohu
LI, Shanping
SUN, Jianling
A Comparative Study of Supervised Learning Algorithms for Re-opened Bug Prediction
description Bug fixing is a time-consuming and costly job which is performed in the whole life cycle of software development and maintenance. For many systems, bugs are managed in bug management systems such as Bugzilla. Generally, the status of a typical bug report in Bugzilla changes from new to assigned, verified and closed. However, some bugs have to be reopened. Reopened bugs increase the software development and maintenance cost, increase the workload of bug fixers, and might even delay the future delivery of a software. Only a few studies investigate the phenomenon of reopened bug reports. In this paper, we evaluate the effectiveness of various supervised learning algorithms to predict if a bug report would be reopened. We choose 7 state-of-the-art classical supervised learning algorithm in machine learning literature, i.e., kNN, SVM, Simple Logistic, Bayesian Network, Decision Table, CART and LWL, and 3 ensemble learning algorithms, i.e., AdaBoost, Bagging and Random Forest, and evaluate their performance in predicting reopened bug reports. The experiment results show that among the 10 algorithms, Bagging and Decision Table (IDTM) achieve the best performance. They achieve accuracy scores of 92.91% and 92.80%, respectively, and reopened bug reports F-Measure scores of 0.735 and 0.732, respectively. These results improve the reopened bug reports F-Measure of the state-of-the-art approaches proposed by Shihab et al. by up to 23.53%.
format text
author XIA, Xin
LO, David
WANG, Xinyu
YANG, Xiaohu
LI, Shanping
SUN, Jianling
author_facet XIA, Xin
LO, David
WANG, Xinyu
YANG, Xiaohu
LI, Shanping
SUN, Jianling
author_sort XIA, Xin
title A Comparative Study of Supervised Learning Algorithms for Re-opened Bug Prediction
title_short A Comparative Study of Supervised Learning Algorithms for Re-opened Bug Prediction
title_full A Comparative Study of Supervised Learning Algorithms for Re-opened Bug Prediction
title_fullStr A Comparative Study of Supervised Learning Algorithms for Re-opened Bug Prediction
title_full_unstemmed A Comparative Study of Supervised Learning Algorithms for Re-opened Bug Prediction
title_sort comparative study of supervised learning algorithms for re-opened bug prediction
publisher Institutional Knowledge at Singapore Management University
publishDate 2013
url https://ink.library.smu.edu.sg/sis_research/1688
http://doi.ieeecomputersociety.org/10.1109/CSMR.2013.43
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