SHF: Small: Collaborative Research: Better Comprehension of Software Engineering Data

在今天的发展过程中产生了多少数据？美国的软件系统是惊人的。它包括源代码、开发人员电子邮件、bug信息、测试结果、分析数据、过程信息、需求等。这些信息的大小和复杂性使开发人员无法对其进行推理。数据挖掘技术是提取与开发人员和管理人员相关的信息的常用解决方案。这些软件项目的成功和质量取决于软件工程师。能够为特定的软件工程数据定制通用的数据挖掘算法。这个项目将产生工具和技术，这些工具和技术将允许软件开发人员和管理人员轻松地定制和应用数据挖掘技术来解决各种软件工程问题。这样的解决方案将变得更加实用，并将帮助许多现有的方法从研究实验室迁移到工业。代表性不足的学生类别将参与这项研究。这项计划将有助加强现有的软件工程课程，并有助日后的软件工程从业员和研究人员学习数据挖掘解决方案。具体地说，该项目将改进三个重要软件工程任务的技术解决方案的状态：软件中的概念定位、软件缺陷预测和开发工作估计。该项目将产生一个算法定制方法和一个框架，该框架将为数据挖掘算法x软件工程任务x软件系统数据的各种组合实例化。定制问题被定义为优化问题。结果定制代理将帮助软件工程用户有效地选择最佳配置，其中包括一组算法及其参数值，为特定任务和软件系统定制。所有的工具和方法都将在学术和工业环境中进行实证评估。

The amount of data generated during the development of today?s software systems is staggering. It includes the source code, developer e-mails, bug information, testing results, analysis data, process information, requirements, etc. The size and complexity of this information make it impossible for developers to reason about it. Data mining techniques are a common solution to extract what is relevant to developers and managers. The success and quality of these software projects depends on the software engineers? ability to customize generic data mining algorithms to specific software engineering data. This project will produce tools and techniques that will allow software developers and managers to easily customize and apply data mining techniques to a variety of software engineering problems. Such solution will become more practical and will help many existing approaches to migrate from the research lab into industry.Under represented categories of students will participate in this research. The project will enhance the existing software engineering curriculum and facilitate the inclusion of data mining solution in the repertoire of future software engineering practitioners and researchers.Specifically, the project will improve the state of the art solution to three important software engineering tasks: concept location in software, software defect prediction, and development effort estimation. The project will produce an algorithm customization methodology and a framework that will be instantiated for a variety of combinations of data mining algorithm x software engineering task x software system data. The customization problem is framed and addressed as an optimization problem. The resulting customization agent will assist the software engineering user in efficiently selecting the best configuration, which includes a set of algorithms and their parameter values, customized for a particular task and software system. All tools and methodologies will be empirically evaluated in academic and industrial settings.