Advancing GenAI Assisted Programming--A Comparative Study on Prompt Efficiency and Code Quality Between GPT-4 and GLM-4
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914686120755200 |
|---|---|
| author | Yang, Angus Li, Zehan Li, Jie |
| author_facet | Yang, Angus Li, Zehan Li, Jie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12782 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancing GenAI Assisted Programming--A Comparative Study on Prompt Efficiency and Code Quality Between GPT-4 and GLM-4 Yang, Angus Li, Zehan Li, Jie Software Engineering Artificial Intelligence D.2.3 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. |
| title | Advancing GenAI Assisted Programming--A Comparative Study on Prompt Efficiency and Code Quality Between GPT-4 and GLM-4 |
| topic | Software Engineering Artificial Intelligence D.2.3 |
| url | https://arxiv.org/abs/2402.12782 |