The Role of Code Proficiency in the Era of Generative AI
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| author | Robles, Gregorio Treude, Christoph Gonzalez-Barahona, Jesus M. Kula, Raula Gaikovina |
| author_facet | Robles, Gregorio Treude, Christoph Gonzalez-Barahona, Jesus M. Kula, Raula Gaikovina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01565 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Role of Code Proficiency in the Era of Generative AI Robles, Gregorio Treude, Christoph Gonzalez-Barahona, Jesus M. Kula, Raula Gaikovina Software Engineering 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. |
| title | The Role of Code Proficiency in the Era of Generative AI |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2405.01565 |