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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.08876 |
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| _version_ | 1866911201763524608 |
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| author | Bevziuk, Kostiantyn Fatula, Andrii Opanasenko, Svetozar Lashin Yaroslav Tukhtarova, Anna Sharma, Ashok Jallepalli Pradeepkumar Shrivastava, Hritvik |
| author_facet | Bevziuk, Kostiantyn Fatula, Andrii Opanasenko, Svetozar Lashin Yaroslav Tukhtarova, Anna Sharma, Ashok Jallepalli Pradeepkumar Shrivastava, Hritvik |
| contents | We present a repository decomposition system that converts large software repositories into a vectorized knowledge graph which mirrors project architectural and semantic structure, capturing semantic relationships and allowing a significant level of automatization of further repository development. The graph encodes syntactic relations such as containment, implementation, references, calls, and inheritance, and augments nodes with LLM-derived summaries and vector embeddings. A hybrid retrieval pipeline combines semantic retrieval with graph-aware expansion, and an LLM-based assistant formulates constrained, read-only graph requests and produces human-oriented explanations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08876 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Vector Graph-Based Repository Understanding for Issue-Driven File Retrieval Bevziuk, Kostiantyn Fatula, Andrii Opanasenko, Svetozar Lashin Yaroslav Tukhtarova, Anna Sharma, Ashok Jallepalli Pradeepkumar Shrivastava, Hritvik Software Engineering Artificial Intelligence We present a repository decomposition system that converts large software repositories into a vectorized knowledge graph which mirrors project architectural and semantic structure, capturing semantic relationships and allowing a significant level of automatization of further repository development. The graph encodes syntactic relations such as containment, implementation, references, calls, and inheritance, and augments nodes with LLM-derived summaries and vector embeddings. A hybrid retrieval pipeline combines semantic retrieval with graph-aware expansion, and an LLM-based assistant formulates constrained, read-only graph requests and produces human-oriented explanations. |
| title | Vector Graph-Based Repository Understanding for Issue-Driven File Retrieval |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2510.08876 |