SHF: Small: Asked and Answered: Intelligent Data Science for Software Projects

SHF：小型：询问和回答：软件项目的智能数据科学

• 批准号: 1649448
• 负责人: Jane Huang
• 金额: $ 51.59万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1649448&HistoricalAwards=false
• 关键词: SHF; Small; Asked; Answered; Intelligent

Software and systems engineering projects accrue large amounts of development data including requirements, design, code, test cases, and fault logs. When combined with the power of software analytics this data could be used to provide actionable intelligence to project stakeholders. For example, a developer might ask to view all "safety-related code which is likely to exhibit runtime faults." The proposed work will deliver a solution named Asked and Answered for Software Intensive Projects (AA) and will support a broad range of analytic queries. To foster the transition of AA to practice, the researchers will partner with industry collaborators throughout the project and develop an open-source framework facilitating the deployment of AA technology into an industrial environment. A series of natural language Query Challenges will be designed and disseminated and used to train Software Engineering students in a broad spectrum of software analytics. Delivering the AA solution requires several non-trivial challenges to be addressed. First, a natural language (NL) query interface will be developed allowing stakeholders to issue queries in their own words and from their own perspective on the project. These queries will then be transformed into a structured, executable format. Heuristics and statistical inferencing techniques will be adopted and interactive mechanisms will be designed to seek clarification from the user when the query cannot be disambiguated automatically. Software analytics will be integrated into the query mechanism so that AA can respond to a wide range of analytical questions. AA will support the dynamic composition of primitive functions into execution flows in order to service a wide range of analytical queries. Finally, AA will deliver a query engine capable of generating optimized query execution plans which take into account the nuances of the domain - namely its heterogeneous data formats, distributed tools, and the dynamic runtime requirements of analytic functions.

软件和系统工程项目具有大量的开发数据，包括需求，设计，代码，测试用例和故障日志。当与软件分析的功能结合使用时，该数据可用于为项目利益相关者提供可行的情报。例如，开发人员可能会要求查看所有“与安全相关的代码，可能会出现运行时故障”。拟议的工作将提供一个名为“询问”的解决方案，并为软件密集型项目（AA）回答，并将支持广泛的分析查询。 为了促进AA向实践的过渡，研究人员将与整个项目中的行业合作者合作，并开发一个开源框架，以促进AA技术在工业环境中的部署。一系列自然语言查询挑战将被设计和传播，并用于培训软件工程专业的学生，​​以各种软件分析。提供AA解决方案需要一些非平凡的挑战。首先，将开发一种自然语言（NL）查询界面，使利益相关者能够用自己的文字和对项目的角度发表查询。然后，这些查询将转换为结构化的可执行格式。将采用启发式方法和统计推导技术，并将设计互动机制，以寻求用户的澄清，当该查询无法自动消除。软件分析将集成到查询机制中，以便AA可以回答广泛的分析问题。 AA将支持原始函数的动态组成为执行流，以服务广泛的分析查询。最后，AA将提供能够生成优化查询执行计划的查询引擎，该计划考虑了域的细微差别 - 即其异质数据格式，分布式工具以及分析功能的动态运行时要求。

项目成果

Supporting Program Comprehension through Fast Query response in Large-Scale Systems

通过大型系统中的快速查询响应支持程序理解

• DOI: 10.1145/3387904.338926
• 发表时间: 2020
• 期刊: 2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC
• 作者: Lin, Jinfeng;Liu, Yalin;Cleland-Huang, Jane
• 通讯作者: Cleland-Huang, Jane