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The vision is to leverage the capabilities of multiple GPT agents to contribute to SE tasks and to propose an initial road map for future work, arguing that multiple G PT agents can perform creative and demanding tasks far beyond coding and debugging.
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
A unified platform that utilizes multiple artificial intelligence agents to automate the process of transforming user requirements into well-organized deliverables, including user stories, prioritization, and UML sequence diagrams, along with the modular approach to APIs, unit tests, and end-to-end tests.
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Results corroborate the effectiveness of LLMs in improving and streamlining RE phases by analyzing the semantic similarity and API performance of different models, as well as their effectiveness and efficiency in requirements analysis.
A novel multi-agent AI model is introduced that aims to assess and compare the performance of various LLMs, and initial results indicate that the GPT-3.5 Turbo model's performance is comparatively better than the other models.
It is shown 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.
This work proposes and develops a multi-model unified platform to generate and execute code based on natural language prompts and presents practitioners feedback and insights into the use of LLMs in software development, including their strengths and weaknesses, key aspects overlooked by benchmarks and metrics.
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