Beyond Autocomplete: Investigating the Impact of AI Code Generation on Code Quality and Technical Debt
DOI:
https://doi.org/10.63282/3050-9246/ICRTCSIT-115Keywords:
Human-AI collaboration, Software quality metrics, Programming automation, Code correctness, Refactoring and AI, Long-term software maintenance, Code smells, Impact of AI on software lifecycle, Software development tools, GPT-based code generationAbstract
The focus of the study has been on understanding the impact of AI code generation on code quality and technical debt. It has been observed in most cases that the speed of generating codes with the help of artificial intelligence has increased, and it is mostly effective for short works. In larger and complex analyses, there can be a chance of higher redundancy and overhead hosts because of the constant supervision and proper checks that are required. In different areas, the application of AI in generating codes can increase the technical test, the quality of the model, and the architecture is not properly aligned. However, it has also been identified that the proper implementation of AI models and techniques can eventually help in reducing technical debt
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