The Role of Code Proficiency in the Era of Generative AI

TL;DR

This paper emphasizes the importance of transparency in generative AI models for code, advocating a 'white box' approach to ensure AI-generated code is understandable, trustworthy, and aligns with developer skills amidst rapid AI integration.

Contribution

It proposes a research agenda focusing on white-box methodologies to improve transparency, responsibility, and trustworthiness of AI-generated code in software development.

Findings

Highlights the need for transparency in AI code generation

Identifies key research questions for white-box AI models

Emphasizes the importance of aligning AI output with developer skills

Abstract

At the current pace of technological advancements, Generative AI models, including both Large Language Models and Large Multi-modal Models, are becoming integral to the developer workspace. However, challenges emerge due to the 'black box' nature of many of these models, where the processes behind their outputs are not transparent. This position paper advocates for a 'white box' approach to these generative models, emphasizing the necessity of transparency and understanding in AI-generated code to match the proficiency levels of human developers and better enable software maintenance and evolution. We outline a research agenda aimed at investigating the alignment between AI-generated code and developer skills, highlighting the importance of responsibility, security, legal compliance, creativity, and social value in software development. The proposed research questions explore the potential of white-box methodologies to ensure that software remains an inspectable, adaptable, and trustworthy asset in the face of rapid AI integration, setting a course for research that could shape the role of code proficiency into 2030 and beyond.