I-Corps: Intelligent Software Quality Monitoring and Improvement Technology

I-Corps：智能软件质量监控和改进技术

• 批准号: 2216594
• 负责人: Marouane Kessentini
• 金额: $ 5万
• 依托单位: Oakland University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2216594&HistoricalAwards=false
• 关键词: Corps; Intelligent; Software; Quality; Monitoring

The broader impact/commercial potential of this I-Corps project is the development of an intelligent software maintenance technology that focuses on improvement of the software and systems that underpin our national infrastructure and drive our economy. It is estimated corporations are spending over $300 billion per year globally, paying down “technical debt” on addressing issues related to maintaining legacy systems or dealing with bad software. By adopting the proposed technology, software development teams will avoid critical bugs that, in turn, will avoid financial loss. The proposed technology will help chief technology officers (CTOs) and chief information officers (CIOs) - not only developers - carry out several critical activities including: gaining actionable insight into the state of their code; accurately estimating the scope and cost of code cleanup; and assessing code quality trends and how to repair these code quality issues. By carrying out these activities, the proposed technology will decrease the time, cost, and risk of code cleanup while improving team performance.This I-Corps project is based on the development of an intelligent refactoring bot that may be easily integrated into any project repository. The bot may be customized to monitor the quality in the repository after a number of pull-requests. The bot analyzes the changed files to identify refactoring opportunities using a set of quality attributes. It will then find the best sequence of refactorings to fix the quality issues, if any. The developer is able to review the recommendations in the generated pull-request. The initial proof-of-concept of the proposed interactive refactoring software was tested on 12 large-scale, open-source, and industrial systems with a total of 37 developers from industry. The technology reduced the amount of time that developers spent to understand existing large-scale source code by an average of 47%. Existing refactoring products, when tested on the same systems, were able to reduce that time by less than 24% on average. In addition, the technology was able to detect and fix an average of 92% of existing design defects in these systems. In contrast, existing refactoring products were able to only detect an average of less than 76% of these defects.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.

该 I-Corps 项目更广泛的影响/商业潜力是开发智能软件维护技术，该技术专注于改进支持我们国家基础设施并推动我们经济的软件和系统。据估计，每个企业的支出超过 3000 亿美元。通过采用拟议的技术，软件开发团队将在全球范围内偿还与维护遗留系统或处理不良软件相关的“技术债务”，从而避免财务损失。帮助首席技术官(CTO) 和首席信息官 (CIO)（不仅仅是开发人员）开展多项关键活动，包括：深入了解代码状态；准确估计代码清理的范围和成本；评估代码质量趋势以及如何进行清理；通过执行这些活动，所提出的技术将减少代码清理的时间、成本和风险，同时提高团队绩效。这个 I-Corps 项目基于智能重构机器人的开发。轻松集成到任何项目中机器人可以定制为在多次拉取请求后监视存储库中的质量，机器人将使用一组质量属性来识别重构机会，然后找到要修复的最佳重构序列。质量问题（如果有） 开发人员能够查看生成的拉取请求中的建议 所提议的交互式重构软件的初始概念验证已在 12 个大型开源和工业平台上进行了测试。在共有 37 名来自业界的开发人员的系统中，该技术将开发人员花在理解现有大规模源代码上的时间平均减少了 47%。平均而言，该技术能够检测并修复这些系统中 92% 的现有设计缺陷，相比之下，现有的重构产品只能检测到平均不到 24% 的缺陷。 76%该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。