Leveraging software analytics to maximize developer productivity during software maintenance.

软件从业者根据他们对软件系统的理解做出技术和业务决策。这种理解是基于他们自己的经验，但可以通过研究各种开发构件来增强，包括源代码、错误报告、版本控制元数据、测试用例、使用日志等。不幸的是，这些构件中包含的信息通常不是以对开发人员的日常决策需求立即有用的方式组织的。为了处理大量的数据，许多从业者和研究人员转向了分析。*本研究计划旨在探索如何系统地发现软件分析在支持软件开发人员的日常决策以及提高他们在构建和维护软件系统时的意识和效率方面的好处。该程序将研究从各种开发构件(错误报告、提交历史记录、源代码、维护任务、测试套件、开发人员讨论等)收集、预处理、转换和建模数据的技术。目的是突出有用的信息并从中得出结论。*这个项目是一个长期研究议程的一部分，目的是提高对开发人员如何创建、发展和维护软件系统的理解，并调查如何改进软件维护的实践。*软件是由人制造的；因此，我们相信，通过更好地了解人们如何创建、使用和推理软件系统，我们可以提高他们开发软件的效率。通过使工具和开发环境更加基于分析，我们可以提高个人绩效和生产力。*

Software practitioners make technical and business decisions based on the understanding they have of their software systems. This understanding is grounded in their own experiences, but can be augmented by studying various kinds of development artifacts, including source code, bug reports, version control meta-data, test cases, usage logs, etc. Unfortunately, the information contained in these artifacts is typically not organized in the way that is immediately useful to developers' everyday decision making needs. To handle the large volumes of data, many practitioners and researchers have turned to analytics.***This research program aims to explore how we can systematically uncover the benefits of software analytics in supporting software developers' daily decision-making and improving their awareness and effectiveness when they build and maintain software systems. This program will investigate techniques for gathering, preprocessing, transforming and modelling the data from various development artifacts (bug reports, commit history, source code, maintenance tasks, test suits, developer discussions, etc.) with the purpose of highlighting useful information and drawing conclusions from it.***This project is a part of of a long-term research agenda with the goal of improving the understanding of how developers create, evolve and maintain software systems, and investigating how the practice of software maintenance can be improved.***Software is made by people; and thus, we believe that by better understanding how people create, use and reason about software systems, we can enhance their effectiveness as they develop software. By making tools and development environments more analytics-based, we can improve individual performance and productivity. *** **