The paper discusses the opportunities and challenges of developing open-source AI-based software engineering tools. It highlights the current limitations of open-source code model development, such as limited access to high-quality data, lack of strong community support, and inefficient resource utilization.
To address these challenges, the paper proposes a decentralized governance framework for open-source code models based on federated learning (FL). This framework allows multiple entities, including research labs, industry organizations, and companies, to collaboratively train and maintain code models while preserving data privacy.
The key aspects of the proposed framework include:
The paper also presents a comprehensive experimental evaluation to assess the impact of data heterogeneity on the performance of federated learning models across various code-related tasks, such as clone detection, defect detection, code search, code-to-text, and code completion. The results demonstrate the potential of federated learning to achieve performance comparable to centralized training while preserving data privacy.
The paper concludes by discussing the challenges and opportunities in implementing this decentralized governance framework for open-source AI-based software engineering tools, including code privacy protection, reward mechanisms, collaborative interaction protocols, copyright issues, and security concerns.
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by Zhihao Lin,W... lúc arxiv.org 04-10-2024
https://arxiv.org/pdf/2404.06201.pdfYêu cầu sâu hơn