EAGER: Bringing Design Thinking into Developers' Coding Activities through an Architectural Tactic Recommender System

EAGER：通过架构策略推荐系统将设计思维引入开发人员的编码活动

• 批准号: 1543176
• 负责人: Mehdi Mirakhorli
• 金额: $ 8万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1543176&HistoricalAwards=false
• 关键词: EAGER; Bringing; Design; Thinking; into

The success of any complex software-intensive system is highly dependent on the extent to which it addresses stakeholders' quality concerns such as reliability, availability, security and performance. Software architects utilize a rich set of proven and re-usable architectural solutions such as tactics and patterns to satisfy each specific quality attribute. The objective of this research project is to develop a novel tactic-recommender system that is trained through learning from the source code of thousands of open-source software systems. The project will include developing a set of novel big-data compatible architecture profilers that ?detect? and ?learn? architectural choices made by several developers across source codes of thousands of open-source systems. This knowledge is used to recommend architectural tactics fitting a new project. This represents a paradigm shift in utilizing automated techniques to bring design thinking into developers? daily coding activities. Unlike existing architecture design practices, which all involve a rigorous upfront analysis of the system's quality concerns, this research takes a bottom-up approach. It uses the latent domain topics in the source code and identifies not only architectural tactics/patterns missing in a given project but also recommends several high quality sample implementations of the tactics/patterns from open source software projects. The findings of this research will partially address the current gap between design and implementation. The broader impact of the work will be to improve the productivity of software development and to improve the quality and evolvability of software-intensive systems.

任何复杂的软件密集型系统的成功在很大程度上取决于它在多大程度上解决了利益攸关方的质量问题，如可靠性、可用性、安全性和性能。软件架构师利用一组丰富的、经过验证的、可重用的架构解决方案，如策略和模式，来满足每个特定的质量属性。 这个研究项目的目标是开发一个新的策略推荐系统，该系统通过从数千个开源软件系统的源代码中学习来训练。该项目将包括开发一套新颖的大数据兼容架构分析器，检测？然后呢？学习？几个开发人员在数千个开源系统的源代码中做出的架构选择。这些知识用于推荐适合新项目的架构策略。 这代表了利用自动化技术将设计思维带入开发人员的范式转变？日常编码活动。 与现有的体系结构设计实践不同，这些实践都涉及对系统质量问题的严格的前期分析，本研究采用自下而上的方法。它使用源代码中的潜在领域主题，不仅识别给定项目中缺少的架构策略/模式，还推荐了开源软件项目中策略/模式的几个高质量示例实现。 这项研究的结果将部分解决目前设计和执行之间的差距。这项工作的更广泛影响将是提高软件开发的生产力，并提高软件密集型系统的质量和可发展性。