AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 25 of 96 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
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.
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
A Multi-Vocal Literature Review is conducted, combining insights from both academia and industry, including peer-reviewed studies and grey literature to systematically synthesize and analyze existing knowledge on LLM-based multi-agent systems for code generation.
This study provides the first large-scale empirical comparison of agentic frameworks for reasoning-intensive software engineering tasks and shows that framework selection should prioritize orchestration quality, especially memory control, failure handling, and cost management.
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.
A web-based software tool is introduced that employs an LLM-based agent and prompt engineering to automate the generation of test case scenarios against user requirements and crafting test case scenarios based on these stories.
A web-based software tool utilizing AI agents and prompt engineering to automate task prioritization and apply diverse prioritization techniques, aimed at enhancing project management within the agile framework is introduced.
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.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.