SHF: Small: Collaborative Research: Automatically Enhancing Quality of Social Communication Channels to Support Software Developers and Improve Tool Reliability

SHF：小型：协作研究：自动增强社交沟通渠道的质量以支持软件开发人员并提高工具可靠性

• 批准号: 1813253
• 负责人: Lori Pollock
• 金额: $ 26.59万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813253&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Automatically

Social communication channels (e.g., Stack Overflow, Slack, and GitHub) play an increasingly important role in software developer productivity, as developers use these channels to collaborate and coordinate activities. Researchers have shown additional value in mining information from these sources to develop and improve recommendation systems, software integrated development environments, and other tools. Users of these systems complain about the varying and often poor quality of the information shared on these channels despite the intrinsic mechanisms for maintaining quality (e.g., voting, accepted answers). This project aims to address the need for automation in curating for increased quality of the information shared in developer communication channels. The envisioned system uses multiple software developers' social communication channels as parallel sources of evidence to establish quality of information in each channel.This project will contribute to the state of the art by tackling three major challenges to bring automated curation of developer communication into practical use. The first aim is to develop analyses for automatically determining and improving the quality of developers' interactive communications on social media sites with regard to quality concerns including poor software properties, obsolescence, contradictions, unreliable sources, and duplication. The second aim is to explore different feedback mechanisms to indicate quality findings back to the communities. Third, the project will develop techniques to customize quality measurement and feedback to a particular developer's context. The resulting tools, data sets, and experimental infrastructure developed as part of the project will be released, enabling other researchers and practitioners to build on the project's results and ultimately advance the quality of the modern software development ecosystem.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

社交沟通渠道（例如，堆栈溢出，Slack和Github）在软件开发人员的生产力中起着越来越重要的作用，因为开发人员使用这些渠道来协作和协调活动。研究人员已经显示了来自这些来源的采矿信息的其他价值，以开发和改善建议系统，软件集成开发环境以及其他工具。这些系统的用户抱怨尽管具有维持质量的内在机制（例如，投票，接受的答案），这些信息在这些渠道上共享的信息质量的变化和差异通常很差。该项目旨在满足自动化的需求，以提高开发人员通信渠道中共享的信息的质量。设想的系统使用多个软件开发人员的社交沟通渠道作为平行的证据来源，以在每个渠道中建立信息质量。该项目将通过应对三个主要挑战以将开发人员沟通的自动化策划带入实际使用中，从而为最新技术做出贡献。第一个目的是开发分析，以自动确定和提高开发人员在社交媒体网站上的交互式通信的质量，包括质量问题，包括软件属性差，过时，矛盾，不可靠的来源和重复。第二个目的是探索不同的反馈机制，以向社区表明质量发现。第三，该项目将开发技术来自定义特定开发人员上下文的质量测量和反馈。将发布作为项目一部分开发的生成工具，数据集和实验基础架构，使其他研究人员和从业人员能够在项目的结果上构建基于项目的结果，并最终提高现代软件开发生态系统的质量。该奖项反映了NSF的法规使命，并认为通过基金会的知识优点和广泛的critia crietia critia crietia critia crietia crietia crietia critia crietia crietia crietia cristia critia crietia criteia crietia criteia cristia crigia cristia cristia均值得一提。

项目成果

Investigating user perceptions of conversational agents for software-related exploratory web search

调查用户对软件相关探索性网络搜索的对话代理的看法

• DOI: 10.1145/3510455.3512778
• 发表时间: 2022
• 期刊: Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results
• 作者: Frazier, Matthew;Kumar, Shaayal;Damevski, Kostadin;Pollock, Lori
• 通讯作者: Pollock, Lori

Exploratory study of slack q&a chats as a mining source for software engineering tools

• DOI: 10.1109/msr.2019.00075
• 发表时间: 2019-01-01
• 期刊: IEEE WORK CONF MIN S
• 作者: Chatterjee, P.;Damevski, K.;Pollock, L.
• 通讯作者: Pollock, L.

Extracting Archival-Quality Information from Software-Related Chats

从软件相关的聊天中提取档案质量信息

• DOI: 10.1145/3377812.3381391
• 发表时间: 2020
• 期刊: ICSE Doctoral Symposium
• 作者: Chatterjee, Preetha

Automatically Selecting Follow-up Questions for Deficient Bug Reports

• DOI: 10.1109/msr52588.2021.00029
• 发表时间: 2021-05
• 期刊: 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR)
• 作者: M. M. Imran-M.;Agnieszka Ciborowska;Kostadin Damevski

Automatic Extraction of Opinion-based Q&A from Online Developer Chats

• DOI: 10.1109/icse43902.2021.00115
• 发表时间: 2021-01-01
• 期刊: 2021 IEEE/ACM 43RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2021)
• 作者: Chatterjee, Preetha;Damevski, Kostadin;Pollock, Lori
• 通讯作者: Pollock, Lori