Improving automatic bug assignment using time-metadata in term-weighting
Assigning newly reported bugs to project developers is a time-consuming and tedious task for triagers using the traditional manual bug triage process. Previous efforts for creating automatic bug assignment systems use machine learning and information-retrieval techniques. These approaches commonly u...
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| Main Authors: | , , , |
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| 格式: | Article |
| 出版: |
The Institution of Engineering and Technology
2014
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| 主題: | |
| 在線閱讀: | http://eprints.um.edu.my/15553/ |
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| 機構: | Universiti Malaya |
| 總結: | Assigning newly reported bugs to project developers is a time-consuming and tedious task for triagers using the traditional manual bug triage process. Previous efforts for creating automatic bug assignment systems use machine learning and information-retrieval techniques. These approaches commonly use tf-idf, a statistical computation technique for weighting terms based on term frequency. However, tf-idf does not consider the metadata, such as the time frame at which a term was used, when calculating the weight of the terms. This study proposes an alternate term-weighting technique to improve the accuracy of automatic bug assignment approaches that use a term-weighting technique. This technique includes the use of metadata in addition to the statistical computation to calculate the term weights. Moreover, it restricts the set of terms used to only nouns. It was found that when using only nouns and the proposed term-weighting technique, the accuracy of an automatic bug assignment approach improves from 12 to 49% over tf-idf for three open-source projects. |
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