作为数据挖掘与软件工程的交叉领域，bug仓库挖掘是国际学术界的前沿研究领域。它通过将软件任务转化为数据挖掘问题，为提高软件质量提供了新的途径。已有的研究成果通常将bug仓库的主体（bug或开发者）简单映射为数据实例或者标签，而未考虑开发者的经验程度或bug的重要程度差异，严重影响了相关的软件任务的求解效果。本项目围绕如何挖掘、分析及应用bug仓库中的开发者的经验程度和bug的重要程度(报告中，经验程度或者重要程度被泛称为优先级)，凝练出一系列科学问题：1）模型建立层面，如何建立适应大规模数据和抗噪音的模型，以挖掘开发者或bug的优先级；2）模型分析层面，如何分析优先级随时间，软件部件粒度演化的性质；3）模型应用层面，如何利用优先级解决与bug处理相关的各种典型任务。结合已有的研究基础，项目组拟定了切实可行的研究方案。该项目有望显著拓展bug仓库挖掘的研究范畴，改进基于bug仓库的软件任务。

As the interdiscipline of data mining and software engineering, mining bug repositories is a leading field in the current research of computer science. In mining bug repositories, software tasks are converted into the problem of data mining to improve software quality. However, existing work on mining bug repositories simply maps bugs or developers into instances or labels. Such mapping has omitted the inherent difference of experience between developers or the differences of importance between bugs, therefore significantly restricted the solutions of software tasks. In this project, we issue a series of research problems on the priority mining in bug repositories, which aims to mine, analyze, and apply the experience of developers or the importance of bugs (in this project, priority refers to the experience of developers or the importance of bugs). These research problems can be divided into three levels, namely 1) the level of model building - how to build algorithms models for mining developer priority or bug priority to adapt to the large-scale data processing and the noise tolerance, 2) the level of model analysis - whether the developer priority or bug priority can keep stable with the time evolution and the changes of software granularity, 3) the level of model application - how to apply developer priority or bug priority to solve various typical tasks, which are related to bug handling. The project can effectively enhance the current work on mining bug repositories and improve the software tasks in bug repositories.