Exploring the Potential of Llama Models in Automated Code Refinement: A Replication Study
Sábháilte in:
| Foilsithe in: | arXiv.org (Dec 3, 2024), p. n/a |
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| Príomhchruthaitheoir: | |
| Rannpháirtithe: | , , , , |
| Foilsithe / Cruthaithe: |
Cornell University Library, arXiv.org
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| Ábhair: | |
| Rochtain ar líne: | Citation/Abstract Full text outside of ProQuest |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3141257003 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3141257003 | ||
| 045 | 0 | |b d20241203 | |
| 100 | 1 | |a Caumartin, Genevieve | |
| 245 | 1 | |a Exploring the Potential of Llama Models in Automated Code Refinement: A Replication Study | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 3, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Code reviews are an integral part of software development and have been recognized as a crucial practice for minimizing bugs and favouring higher code quality. They serve as an important checkpoint before committing code and play an essential role in knowledge transfer between developers. However, code reviews can be time-consuming and can stale the development of large software projects. In a recent study, Guo et al. assessed how ChatGPT3.5 can help the code review process. They evaluated the effectiveness of ChatGPT in automating the code refinement tasks, where developers recommend small changes in the submitted code. While Guo et al. 's study showed promising results, proprietary models like ChatGPT pose risks to data privacy and incur extra costs for software projects. In this study, we explore alternatives to ChatGPT in code refinement tasks by including two open-source, smaller-scale large language models: CodeLlama and Llama 2 (7B parameters). Our results show that, if properly tuned, the Llama models, particularly CodeLlama, can achieve reasonable performance, often comparable to ChatGPT in automated code refinement. However, not all code refinement tasks are equally successful: tasks that require changing existing code (e.g., refactoring) are more manageable for models to automate than tasks that demand new code. Our study highlights the potential of open-source models for code refinement, offering cost-effective, privacy-conscious solutions for real-world software development. | |
| 653 | |a Debugging | ||
| 653 | |a Knowledge management | ||
| 653 | |a Source code | ||
| 653 | |a Software development | ||
| 653 | |a Large language models | ||
| 653 | |a Automation | ||
| 653 | |a Chatbots | ||
| 653 | |a Privacy | ||
| 653 | |a Effectiveness | ||
| 700 | 1 | |a Qin, Qiaolin | |
| 700 | 1 | |a Chatragadda, Sharon | |
| 700 | 1 | |a Panjrolia, Janmitsinh | |
| 700 | 1 | |a Li, Heng | |
| 700 | 1 | |a Diego Elias Costa | |
| 773 | 0 | |t arXiv.org |g (Dec 3, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3141257003/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.02789 |