Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning
<|reference_start|>Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning: Large Language Models (LLMs) have become instrumental in advancing software engineering (SE) tasks, showcasing their efficacy in code understanding and beyond. Like traditional SE tools, open-source collaboration is key in realising the excellent products. However, with AI models, the essential need is in data. The collaboration of these AI-based SE models hinges on maximising the sources of high-quality data. However, data especially of high quality, often holds commercial or sensitive value, making it less accessible for open-source AI-based SE projects. This reality presents a significant barrier to the development and enhancement of AI-based SE tools within the software engineering community. Therefore, researchers need to find solutions for enabling open-source AI-based SE models to tap into resources by different organisations. Addressing this challenge, our position paper investigates one solution to facilitate access to diverse organizational resources for open-source AI models, ensuring privacy and commercial sensitivities are respected. We introduce a governance framework centered on federated learning (FL), designed to foster the joint development and maintenance of open-source AI code models while safeguarding data privacy and security. Additionally, we present guidelines for developers on AI-based SE tool collaboration, covering data requirements, model architecture, updating strategies, and version control. Given the significant influence of data characteristics on FL, our research examines the effect of code data heterogeneity on FL performance.<|reference_end|>
@article{lin2024open-source,
    title={Open-Source AI-based SE Tools: Opportunities and Challenges of
  Collaborative Software Learning},
    author={Zhihao Lin and Wei Ma and Tao Lin and Yaowen Zheng and Jingquan Ge and
  Jun Wang and Jacques Klein and Tegawende Bissyande and Yang Liu and Li Li},
    journal={arXiv preprint arXiv:2404.06201},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06201},
    primaryClass={cs.SE cs.AI}
}