I-Corps: Intelligent Software Quality Monitoring and Improvement Technology

I-Corps项目更广泛的影响/商业潜力是开发一种智能软件维护技术，重点是改进支撑我们国家基础设施和推动我们经济的软件和系统。据估计，全球企业每年花费超过3000亿美元来偿还与维护遗留系统或处理不良软件相关的“技术债务”。通过采用建议的技术，软件开发团队将避免严重的错误，从而避免经济损失。拟议的技术将帮助首席技术官（cto）和首席信息官（cio）——不仅仅是开发人员——开展几项关键活动，包括：获得对代码状态的可操作的洞察；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修复这些代码质量问题。通过执行这些活动，所建议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项目基于一个智能重构机器人的开发，它可以很容易地集成到任何项目存储库中。可以对bot进行定制，以便在多次拉取请求之后监控存储库中的质量。bot分析更改的文件，使用一组质量属性来识别重构机会。然后，它将找到最佳的重构序列来修复质量问题（如果有的话）。开发人员可以查看生成的拉取请求中的建议。提议的交互式重构软件的最初概念验证在12个大规模、开源和工业系统上进行了测试，总共有37名来自行业的开发人员。该技术将开发人员用于理解现有大规模源代码的时间平均减少了47%。现有的重构产品，当在相同的系统上进行测试时，能够平均减少不到24%的时间。此外，该技术能够检测并修复这些系统中平均92%的现有设计缺陷。相比之下，现有的重构产品平均只能检测不到76%的缺陷。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.