Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction
Bugs are prevalent in software systems. Some bugs are critical and need to be fixed right away, whereas others are minor and their fixes could be postponed until resources are available. In this work, we propose a new approach leveraging information retrieval, in particular BM25-based document simil...
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sg-smu-ink.sis_research-25852020-01-08T05:49:09Z Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction TIAN, Yuan LO, David SUN, Chengnian Bugs are prevalent in software systems. Some bugs are critical and need to be fixed right away, whereas others are minor and their fixes could be postponed until resources are available. In this work, we propose a new approach leveraging information retrieval, in particular BM25-based document similarity function, to automatically predict the severity of bug reports. Our approach automatically analyzes bug reports reported in the past along with their assigned severity labels, and recommends severity labels to newly reported bug reports. Duplicate bug reports are utilized to determine what bug report features, be it textual, ordinal, or categorical, are important. We focus on predicting fine-grained severity labels, namely the different severity labels of Bugzilla including: blocker, critical, major, minor, and trivial. Compared to the existing state-of-the-art study on fine-grained severity prediction, namely the work by Menzies and Marcus, our approach brings significant improvement. 2012-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1586 info:doi/10.1109/WCRE.2012.31 https://ink.library.smu.edu.sg/context/sis_research/article/2585/viewcontent/IR_NNC_severity_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Severity Prediction Software Defects Software Engineering |
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Severity Prediction Software Defects Software Engineering TIAN, Yuan LO, David SUN, Chengnian Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction |
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Bugs are prevalent in software systems. Some bugs are critical and need to be fixed right away, whereas others are minor and their fixes could be postponed until resources are available. In this work, we propose a new approach leveraging information retrieval, in particular BM25-based document similarity function, to automatically predict the severity of bug reports. Our approach automatically analyzes bug reports reported in the past along with their assigned severity labels, and recommends severity labels to newly reported bug reports. Duplicate bug reports are utilized to determine what bug report features, be it textual, ordinal, or categorical, are important. We focus on predicting fine-grained severity labels, namely the different severity labels of Bugzilla including: blocker, critical, major, minor, and trivial. Compared to the existing state-of-the-art study on fine-grained severity prediction, namely the work by Menzies and Marcus, our approach brings significant improvement. |
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TIAN, Yuan LO, David SUN, Chengnian |
author_facet |
TIAN, Yuan LO, David SUN, Chengnian |
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TIAN, Yuan |
title |
Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction |
title_short |
Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction |
title_full |
Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction |
title_fullStr |
Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction |
title_full_unstemmed |
Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction |
title_sort |
information retrieval based nearest neighbor classification for fine-grained bug severity prediction |
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Institutional Knowledge at Singapore Management University |
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2012 |
url |
https://ink.library.smu.edu.sg/sis_research/1586 https://ink.library.smu.edu.sg/context/sis_research/article/2585/viewcontent/IR_NNC_severity_av.pdf |
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