PFI–TT: Intelligent Software Refactoring Bot for Continuous Integration

创新伙伴关系-技术转化（PFI-TT）项目的更广泛影响/商业潜力侧重于经济影响问题：改进支撑我们国家基础设施的软件和系统。通过采用建议的技术，软件开发团队可以避免关键的质量和安全问题。这种智能重构技术使组织能够更好地维护他们的软件，并更好地将他们的维护工作与他们的优先级结合起来。这一创新旨在为学生提供持续的技术创新和创业培训机会。该项目专注于开发可伸缩的方法，以确定何时以及如何将开发人员的反馈集成到持续集成环境的半自动化代码重构中，同时遵守行业标准，使工作与其商业化目标保持一致。软件重构被认为是通过重构现有代码和减少技术债务来维护高质量软件的关键组件。重构要求程序员审查、检测和修复质量问题，以提高软件性能。然而，重构很难实现，而且常常被忽视，这不仅是因为要满足发布期限的压力，还因为人工重构所施加的限制，以及在重构复杂系统时缺乏技术技能。传统的根管重构过程并不实用，因为它既耗时又难以集成到开发流程中。因此，新的重构工具必须为代码修复提供及时的支持。这项技术的目标是通过基于人工智能的机器人，清楚地展示将交互式、半自动化、重构技术与持续集成相结合的可行性，并大规模展示实现的概念。这项工作还将支持多种编程语言，包括定量（如准确性、相关性和性能）和定性（如程序员的注释）方面。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 Partnerships for Innovation - Technology Translation (PFI-TT) project focuses on issues of economic impact: improvement of the software and systems that underpin our national infrastructure. By adopting the proposed technology, software development teams may avoid critical quality and security issues. This intelligent refactoring technology enables organizations to better maintain their software as it ages and better align their maintenance efforts with their priorities. This innovation seeks to deliver continuous will also provide training opportunities for students in technological innovation and entrepreneurship. This project focuses on developing scalable methods to determine when and how to integrate developer feedback to semi-automate code refactoring for continuous integration environments while adhering to industry standards to align the effort with their commercialization objectives. Software refactoring is recognized as the key component for maintaining high quality software by restructuring existing code and reducing technical debt. Refactoring requires programmers to review, detect, and fix quality issues to improve software performance. However, refactoring is difficult to achieve and often neglected not only due to a pressure to meet release deadlines, but also due to the constraints imposed by manual refactoring as well as lack of technical skill in restructuring complex systems. The traditional root-canal refactoring process is not practical since it is time consuming and hard to integrate in the development pipelines. Hence, new refactoring tool must deliver timely support for code repair. The goal of this technology is to clearly exhibit the feasibility of combining interactive, semi-automated, refactoring technology with continuous integration via an artificial intelligence-based bot and demonstrate the implemented concept at large-scale. The effort will also support multiple programming languages including quantitative (such as accuracy, relevance, and performance) and qualitative (such as programmers' comments) aspects.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.