A Multi-agent Onboarding Assistant based on Large Language Models, Retrieval Augmented Generation, and Chain-of-Thought
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915218817286144 |
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| author | Ionescu, Andrei Cristian Titov, Sergey Izadi, Maliheh |
| author_facet | Ionescu, Andrei Cristian Titov, Sergey Izadi, Maliheh |
| contents | Effective onboarding in software engineering is crucial but difficult due to the fast-paced evolution of technologies. Traditional methods, like exploration and workshops, are costly, time-consuming, and quickly outdated in large projects. We propose the Onboarding Buddy system, which leverages large language models, retrieval augmented generation, and an automated chain-of-thought approach to improve onboarding. It integrates dynamic, context-specific support within the development environment, offering natural language explanations, code insights, and project guidance. Our solution is agent-based and provides customized assistance with minimal human intervention. Our study results among the eight participants show an average helpfulness rating of (M=3.26, SD=0.86) and ease of onboarding at (M=3.0, SD=0.96) out of four. While similar to tools like GitHub Copilot, Onboarding Buddy uniquely integrates a chain-of-thought reasoning mechanism with retrieval-augmented generation, tailored specifically for dynamic onboarding contexts. While our initial evaluation is based on eight participants within one project, we will explore larger teams and multiple real-world codebases in the company to demonstrate broader applicability. Overall, Onboarding Buddy holds great potential for enhancing developer productivity and satisfaction. Our tool, source code, and demonstration video are publicly available |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23421 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multi-agent Onboarding Assistant based on Large Language Models, Retrieval Augmented Generation, and Chain-of-Thought Ionescu, Andrei Cristian Titov, Sergey Izadi, Maliheh Software Engineering Effective onboarding in software engineering is crucial but difficult due to the fast-paced evolution of technologies. Traditional methods, like exploration and workshops, are costly, time-consuming, and quickly outdated in large projects. We propose the Onboarding Buddy system, which leverages large language models, retrieval augmented generation, and an automated chain-of-thought approach to improve onboarding. It integrates dynamic, context-specific support within the development environment, offering natural language explanations, code insights, and project guidance. Our solution is agent-based and provides customized assistance with minimal human intervention. Our study results among the eight participants show an average helpfulness rating of (M=3.26, SD=0.86) and ease of onboarding at (M=3.0, SD=0.96) out of four. While similar to tools like GitHub Copilot, Onboarding Buddy uniquely integrates a chain-of-thought reasoning mechanism with retrieval-augmented generation, tailored specifically for dynamic onboarding contexts. While our initial evaluation is based on eight participants within one project, we will explore larger teams and multiple real-world codebases in the company to demonstrate broader applicability. Overall, Onboarding Buddy holds great potential for enhancing developer productivity and satisfaction. Our tool, source code, and demonstration video are publicly available |
| title | A Multi-agent Onboarding Assistant based on Large Language Models, Retrieval Augmented Generation, and Chain-of-Thought |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2503.23421 |