CodeQUEST, a GPT-4o-based evaluator-optimizer loop, reports a 52.6% mean relative improvement in code quality, but the improvement is measured by the same model that enforces monotonic score increases.
Advancing GenAI Assisted Programming--A Comparative Study on Prompt Efficiency and Code Quality Between GPT-4 and GLM-4
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abstract
This study aims to explore the best practices for utilizing GenAI as a programming tool, through a comparative analysis between GPT-4 and GLM-4. By evaluating prompting strategies at different levels of complexity, we identify that simplest and straightforward prompting strategy yields best code generation results. Additionally, adding a CoT-like preliminary confirmation step would further increase the success rate. Our results reveal that while GPT-4 marginally outperforms GLM-4, the difference is minimal for average users. In our simplified evaluation model, we see a remarkable 30 to 100-fold increase in code generation efficiency over traditional coding norms. Our GenAI Coding Workshop highlights the effectiveness and accessibility of the prompting methodology developed in this study. We observe that GenAI-assisted coding would trigger a paradigm shift in programming landscape, which necessitates developers to take on new roles revolving around supervising and guiding GenAI, and to focus more on setting high-level objectives and engaging more towards innovation.
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On Iterative Evaluation and Enhancement of Code Quality Using GPT-4o
CodeQUEST, a GPT-4o-based evaluator-optimizer loop, reports a 52.6% mean relative improvement in code quality, but the improvement is measured by the same model that enforces monotonic score increases.