Software R&D efficiency serves as the fundamental cornerstone of enterprise digital transformation and technological product innovation. Accurate assessment and intelligent governance of R&D efficiency are crucial for ensuring software quality, improving team collaboration efficiency, and building a solid foundation for technological innovation. The deep application of artificial intelligence and large language model technologies has brought new opportunities for enhancing R&D efficiency. However, existing R&D efficiency systems commonly suffer from prominent technical and managerial challenges, including imbalanced metric design, distorted assessments, susceptibility to manipulation, as well as code duplication and plagiarism, misuse of open-source components, and the lack of effective governance over AI-generated code.

This project addresses the industrial demands for efficient governance and intelligent upgrading of enterprise software development. It innovatively proposes an AI-empowered R&D efficiency governance technology system, breaking through key technologies such as multi-dimensional efficiency metric optimization, intelligent detection of anomalous code behaviors, precise identification of AI-generated code, and fine-grained traceability of open-source software components. A high-quality dataset covering the entire development lifecycle and a panoramic efficiency interpretation platform are constructed, leading to the engineering realization of an intelligent R&D efficiency governance system. The project outcomes have been deployed across multiple enterprise development scenarios, significantly improving code quality, resource utilization, and contribution assessment accuracy. The results have produced a number of high-level academic papers, invention patents, and software copyrights, providing core technical support for improving software R&D efficiency and fostering high-quality development, with notable technical benefits and industrial value.