DREX: Developer Recommendation with K-Nearest-Neighbor Search and Expertise Ranking
DREX: Developer Recommendation with K-Nearest-Neighbor Search and Expertise Ranking
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DOI：
10.1109/apsec.2011.15
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发表时间：
2011-12
期刊：
影响因子：
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通讯作者：
Wenjin Wu;Wen Zhang;Ye Yang;Qing Wang
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文献类型：
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作者：
Wenjin Wu;Wen Zhang;Ye Yang;Qing Wang
本文提出了一种基于K-Nearest-Neighbor搜索的开发者推荐方法DREX（Developer Recommendation with k-nearest-Neighbor search and Expertise ranking），该方法基于K-Nearest-Neighbor搜索和漏洞相似度以及基于各种度量（包括简单频率和社交网络度量）的专家排名来推荐开发者解决漏洞。我们收集了Mozilla Firefox开放的bug库作为实验数据集，并比较了不同的排名指标对推荐有能力的开发人员的bug的性能。我们的实验结果表明，当推荐10个开发人员的250个测试错误中的每一个，DREX产生了更好的性能比传统的方法与多标签的文本分类。在平均召回率为0.6的情况下，通过两个度量（Out-Degree和Frequency）获得的最佳性能。此外，其他社交网络指标，如度和页面排名，在开发人员推荐方面的表现与用于开发人员专业知识排名的频率相当。
This paper proposes a new approach called DREX (Developer Recommendation with k-nearest-neighbor search and Expertise ranking) to developer recommendation for bug resolution based on K-Nearest-Neighbor search with bug similarity and expertise ranking with various metrics, including simple frequency and social network metrics. We collect Mozilla Fire fox open bug repository as the experimental data set and compare different ranking metrics on the performance of recommending capable developers for bugs. Our experimental results demonstrate that, when recommending 10 developers for each one of the 250 testing bugs, DREX has produced better performance than traditional methods with multi-labeled text categorization. The best performance obtained by two metrics as Out-Degree and Frequency, is with recall as 0.6 on average. Moreover, other social network metrics such as Degree and Page Rank have produced comparable performance on developer recommendation as Frequency when used for developer expertise ranking.