CodePori: Large Scale Model for Autonomous Software Development by Using Multi-Agents

• URL: http://arxiv.org/abs/2402.01411v1
• Date: Fri, 2 Feb 2024 13:42:50 GMT
• Title: CodePori: Large Scale Model for Autonomous Software Development by Using Multi-Agents
• Authors: Zeeshan Rasheed, Muhammad Waseem, Mika Saari, Kari Syst\"a, Pekka Abrahamsson
• Abstract summary: Large Language Models (LLMs) and Generative Pre-trained Transformers (GPTs) are reshaping the field of Software Engineering (SE) This paper introduces CodePori, a novel model designed to automate code generation for extensive and complex software projects based on natural language prompts. We show in the paper that CodePori is able to generate running code for large-scale projects, completing the entire software development process in minutes rather than hours, and at a cost of a few dollars.
• Score: 3.8066447473175304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) and Generative Pre-trained Transformers (GPTs) are reshaping the field of Software Engineering (SE). Existing LLM-based multi-agent systems have successfully resolved simple dialogue tasks. However, the potential of LLMs for more complex tasks, such as automated code generation for large and complex projects, have been explored in only a few existing works. This paper introduces CodePori, a novel model designed to automate code generation for extensive and complex software projects based on natural language prompts. We employ LLM-based multi-AI agents to handle creative and challenging tasks in autonomous software development. Each agent engages with a specific task, including system design, code development, code review, code verification, and test engineering. We show in the paper that CodePori is able to generate running code for large-scale projects, completing the entire software development process in minutes rather than hours, and at a cost of a few dollars. It identifies and mitigates potential security vulnerabilities and corrects errors while maintaining a solid code performance level. We also conducted an evaluation of CodePori against existing solutions using HumanEval and the Massively Multitask Benchmark for Python (MBPP) benchmark. The results indicate that CodePori improves upon the benchmarks in terms of code accuracy, efficiency, and overall performance. For example, CodePori improves the pass@1 metric on HumanEval to 87.5% and on MBPP to 86.5%, representing a clear improvement over the existing models. We also assessed CodePori's performance through practitioner evaluations, with 91% expressing satisfaction with the model's performance.

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