CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866918510501822464 |
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| author | Rasheed, Zeeshan Waseem, Muhammad Kemell, Kai-Kristian Ahmad, Aakash Sami, Malik Abdul Saari, Mika Rasku, Jussi Abrahamsson, Pekka |
| author_facet | Rasheed, Zeeshan Waseem, Muhammad Kemell, Kai-Kristian Ahmad, Aakash Sami, Malik Abdul Saari, Mika Rasku, Jussi Abrahamsson, Pekka |
| contents | Context: LLM-based multi-agent systems enable automation and decision support in software development, yet existing studies rely on benchmark datasets offering only binary pass-or-fail results, limiting insight into real-world applicability. Objective: This study empirically investigates the potential and limitations of LLM-based agents in autonomous software development tasks. Method: A two-phase approach was employed: developing a multi-agent system, CodePori, for automated code generation, and conducting participant-based evaluation to assess practical performance. Results: Participant feedback reveals key strengths, challenges, and areas for improvement in LLM-based multi-agent systems, highlighting aspects missed by standard code-generation benchmarks. Conclusions: While LLM-based multi-agent systems show potential for large-scale software development, successful integration requires addressing challenges such as memory limitations, hallucinations, and code smells, alongside a practitioner-centric perspective. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01411 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology Rasheed, Zeeshan Waseem, Muhammad Kemell, Kai-Kristian Ahmad, Aakash Sami, Malik Abdul Saari, Mika Rasku, Jussi Abrahamsson, Pekka Software Engineering Context: LLM-based multi-agent systems enable automation and decision support in software development, yet existing studies rely on benchmark datasets offering only binary pass-or-fail results, limiting insight into real-world applicability. Objective: This study empirically investigates the potential and limitations of LLM-based agents in autonomous software development tasks. Method: A two-phase approach was employed: developing a multi-agent system, CodePori, for automated code generation, and conducting participant-based evaluation to assess practical performance. Results: Participant feedback reveals key strengths, challenges, and areas for improvement in LLM-based multi-agent systems, highlighting aspects missed by standard code-generation benchmarks. Conclusions: While LLM-based multi-agent systems show potential for large-scale software development, successful integration requires addressing challenges such as memory limitations, hallucinations, and code smells, alongside a practitioner-centric perspective. |
| title | CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2402.01411 |