课题基金 / 基金详情

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

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:
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
项目状态:
已结题
起止时间:
2016-07-01 至 2020-05-31

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中文摘要
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英文摘要
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.
期刊论文(1)
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会议论文
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
影响因子: --
作者: [Lin, Jinfeng, Liu, Yalin, Cleland-Huang, Jane]
通讯作者: Cleland-Huang, Jane
Unveiling diverse planet formation environments with millimeter imaging
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    2307916
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    Standard Grant
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    2023
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    2122689
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    2021
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  • 资助金额:
    $101.9万
  • 财政年份:
    2019
  • 负责人:
    Jane Huang
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