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The Role of Code Proficiency in the Era of Generative AI

Gregorio Robles, Christoph Treude, Jesus M. Gonzalez-Barahona, Raula Gaikovina Kula

TL;DR

The paper examines how to keep AI-generated code transparent and controllable as generative AI integrates into software work. It argues for a white-box paradigm where developers can inspect, understand, and adapt AI-produced source code, facilitating safer evolution and maintenance. A proficiency-aware generation framework is proposed, alongside a research agenda addressing code quality, responsibility, security, legality, creativity, and social value. The work aims to guide both research and industry practices toward trusted, proficient AI-assisted software development by 2030 and beyond.

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.

The Role of Code Proficiency in the Era of Generative AI

TL;DR

The paper examines how to keep AI-generated code transparent and controllable as generative AI integrates into software work. It argues for a white-box paradigm where developers can inspect, understand, and adapt AI-produced source code, facilitating safer evolution and maintenance. A proficiency-aware generation framework is proposed, alongside a research agenda addressing code quality, responsibility, security, legality, creativity, and social value. The work aims to guide both research and industry practices toward trusted, proficient AI-assisted software development by 2030 and beyond.

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.
Paper Structure (8 sections, 1 figure, 1 table)

This paper contains 8 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Evolution and trust
  3. Advantages of the White-box paradigm
  4. Proficiency is key
  5. Research Agenda
  6. Code as a reflection of developer skills
  7. Leveraging Competency with AI
  8. Conclusion

Figures (1)

  • Figure 1: Overview of black-box vs. white-box when using AI agents. The upper part of it shows the black-box case: the software is directly produced by the LLM model, with no human intervention (except for prompting it, and interacting with the resulting software). The lower part of if shows how the white-box case includes humans in the loop, letting them inspect the produced source code.