In the dynamic landscape of modern software development, open-source software plays a crucial role, driving advancements in computer science across industries. However, the inherent complexity and diversity of open-source software reuse pose challenges in quality, security, management, and sustainability. Existing open-source governance approaches, while excelling in community building and collaboration, still face shortcomings in decentralized management, security, and maintenance. Specifically, under the circumstance of generative software engineering based on large language models (LLMs), it is even more fragmented and complex in the management of open-source reuse, leading to the urgent requirements for a finer-grained understanding of software compositions. To address these challenges, inspired by the Human Genome Project, we treat the software source code as software DNA and propose the \textbf{Software Genome Project (SGP)}, which is geared towards the secure monitoring and exploitation of open-source software. By identifying and labelling 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 technology selection and open source governance. SGP could also serve as a comprehensive dataset with abundant semantic labelling 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.