Open-source software is crucial to modern development, but its complexity creates challenges in quality, security, and management. Current governance approaches excel at collaboration but struggle with decentralized management and security. With the rise of large language models (LLM)-based software engineering, the need for a finer-grained understanding of software composition is more urgent than ever. To address these challenges, inspired by the Human Genome Project, we treat the software source code as software DNA and propose the Software Genome Project (SGP), which is geared towards the secure monitoring and exploitation of open-source software. By identifying and labeling integrated and classified code features at a fine-grained level, and effectively identifying safeguards for functional implementations and non-functional requirements at different levels of granularity, the SGP could build a comprehensive set of software genome maps to help developers and managers gain a deeper understanding of software complexity and diversity. By dissecting and summarizing functional and undesirable genes, SGP could help facilitate targeted software optimization, provide valuable insight and understanding of the entire software ecosystem, and support critical development tasks such as open source governance. SGP could also serve as a comprehensive dataset with abundant semantic labeling to enhance the training of LLMs for code. Based on these, we expect SGP to drive the evolution of software development towards more efficient, reliable, and sustainable software solutions.CCS Concepts• Software and its engineering → Software design engineering.

@inproceedings{0001LXZZZ024,
  title = {The Software Genome Project: Unraveling Software Through Genetic Principles},
  author = {Yueming Wu and Chengwei Liu and Zhengzi Xu and Lyuye Zhang and Yiran Zhang and Zhiling Zhu and Yang Liu},
  booktitle = {ASE},
  year = {2024},
  doi = {10.1145/3691620.3695307}
}