Harnessing Deep Learning for Efficient and Responsible AI Code Assistants: A Comprehensive Study of Methods, Evaluation, and Human Interaction

Abstract

The increasing complexity of software development has led to a growing need for intelligent code assistance tools that can aid developers in various tasks, such as code generation, translation, and repair. Deep learning has emerged as a promising approach to addressing this need. This paper presents a comprehensive study of deep learning methods, evaluation metrics, and human interaction techniques for AI code assistants, aiming to provide a solid foundation for researchers and practitioners in the field. We discuss the importance of responsible AI in the context of code assistance, focusing on fairness, security, robustness, and privacy. Furthermore, we highlight the significance of open science practices in promoting reproducibility and transparency in the development of deep learning models for code. Finally, we outline potential future research directions in the area of deep learning for code assistance.

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