CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology

• URL: http://arxiv.org/abs/2402.01411v2
• Date: Tue, 17 Sep 2024 15:57:06 GMT
• Title: CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology
• Authors: Zeeshan Rasheed, Malik Abdul Sami, Kai-Kristian Kemell, Muhammad Waseem, Mika Saari, Kari Systä, Pekka Abrahamsson,
• Abstract summary: Large Language Models (LLMs) and Generative Pre-trained Transformers (GPTs) have transformed the field of Software Engineering. We introduce CodePori, a novel system designed to automate code generation for large and complex software projects. Results: CodePori is able to generate running code for large-scale projects, aligned with the typical software development process.
• Score: 4.2990995991059275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Context: Large Language Models (LLMs) and Generative Pre-trained Transformers (GPTs) have transformed the field of Software Engineering (SE). Existing LLM-based multi-agent models have successfully addressed basic dialogue tasks. However, the potential of LLMs for more challenging tasks, such as automated code generation for large and complex projects, has been investigated in only a few existing works. Objective: This paper aims to investigate the potential of LLM-based agents in the software industry, particularly in enhancing productivity and reducing time-to-market for complex software solutions. Our primary objective is to gain insights into how these agents can fundamentally transform the development of large-scale software. Methods: We introduce CodePori, a novel system designed to automate code generation for large and complex software projects based on functional and non-functional requirements defined by stakeholders. To assess the proposed system performance, we utilized the HumanEval benchmark and manually tested the CodePori model, providing 20 different project descriptions as input and then evaluated the code accuracy by manually executing the code. Results: CodePori is able to generate running code for large-scale projects, aligned with the typical software development process. The HumanEval benchmark results indicate that CodePori improves code accuracy by 89%. A manual assessment conducted by the first author shows that the CodePori system achieved an accuracy rate of 85%. Conclusion: Based on the results, our conclusion is that proposed system demonstrates the transformative potential of LLM-based agents in SE, highlighting their practical applications and opening new opportunities for broader adoption in both industry and academia. Our project is publicly available at https://github.com/GPT-Laboratory/CodePori.

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