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

在今天的发展过程中产生的数据量？的软件系统是惊人的。 它包括源代码、开发人员电子邮件、错误信息、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。数据挖掘技术是一种常见的解决方案，可以提取与开发人员和管理人员相关的信息。 这些软件项目的成功和质量取决于软件工程师？定制通用数据挖掘算法到特定软件工程数据的能力。 该项目将产生工具和技术，使软件开发人员和管理人员能够轻松地定制和应用数据挖掘技术的各种软件工程问题。 这样的解决方案将变得更加实用，并将有助于许多现有的方法从研究实验室迁移到工业中。 该计划将加强现有的软件工程课程，并促进未来的软件工程从业员和研究人员将数据挖掘解决方案纳入他们的课程中。具体来说，该计划将改善三个重要软件工程任务的最先进解决方案：软件中的概念定位，软件缺陷预测和开发工作估计。 该项目将产生一种算法定制方法和一个框架，该框架将被实例化为数据挖掘算法x软件工程任务x软件系统数据的各种组合。 定制问题的框架和解决作为一个优化问题。 由此产生的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，定制为特定的任务和软件系统。 所有的工具和方法将在学术和工业环境中进行经验评估。

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.