Bug assignment is a burden for projects receiving many bug reports. To automate the process of assigning bug reports to the appropriate developers, several studies have relied on combining natural language processing and information retrieval techniques to extract two categories of features. One of these categories targets developers who have fixed similar bugs before, and the other determines developers working on source files similar to the description of the bug. Commit messages represent another rich source for profiling developer expertise as the language used in commit messages is closer to that used in bug reports.

In this work, we propose a more enhanced profiling of developers through their commits, which are captured in a new set of features that we combine with features used in previous studies. More precisely, we propose an adaptive ranking approach that takes as input a given bug report and ranks the top developers who are most suitable to fix it. This approach learns from the history of previously fixed bugs to profile developers in terms of their expertise. With respect to a given bug report, the ranking score of each developer is computed as a weighted combination of an array of features encoding domain knowledge, where the weights are trained automatically on previously solved bug reports using a learning-to-rank technique. Our model was evaluated using around 22,000 bug reports, exported from four large scale open-source Java projects. Results show that our model significantly outperformed two recent state-of-the-art methods in recommending the suitable developer to handle a certain bug report. Specifically, the percentage of recommending a developer within the top 5 ranked developers correctly was over 80% for both the Eclipse UI Platform and Birt projects.

错误分配是项目接收许多错误报告的负担。为了自动将错误报告分配给适当的开发人员，一些研究依靠结合自然语言处理和信息检索技术来提取两类特征。这些类别中的一个针对的是以前已修复类似错误的开发人员，另一类确定了开发人员在处理与该错误的描述类似的源文件。提交消息代表了用于分析开发人员专业知识的另一个丰富资源，因为提交消息中使用的语言更接近于错误报告中使用的语言。

在这项工作中，我们建议通过开发人员的提交对开发人员进行更全面的剖析，将其捕获为一组新的功能，并将它们与以前的研究中使用的功能相结合。更准确地说，我们提出了一种自适应排名方法，该方法将给定的错误报告作为输入，并对最适合修复该错误的顶级开发人员进行排名。这种方法从以前已修复的错误的历史中学习，从而根据开发人员的专业知识来对其进行概要分析。对于给定的错误报告，每个开发人员的排名得分是作为对领域知识进行编码的一组特征的加权组合而计算的，其中权重是使用学习到排名的技术在先前解决的错误报告中自动进行训练的。我们的模型是使用从2个大型开源Java项目中导出的大约22,000个错误报告进行评估的。结果表明，我们的模型在推荐合适的开发人员处理某些错误报告方面明显优于两种最新的最新方法。具体而言，对于Eclipse UI平台和Birt项目，在排名前5位的开发人员中正确推荐开发人员的百分比均超过80％。