Large Language Models for Code Summarization
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910462538416128 |
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| author | Szalontai, Balázs Szalay, Gergő Márton, Tamás Sike, Anna Pintér, Balázs Gregorics, Tibor |
| author_facet | Szalontai, Balázs Szalay, Gergő Márton, Tamás Sike, Anna Pintér, Balázs Gregorics, Tibor |
| contents | Recently, there has been increasing activity in using deep learning for software engineering, including tasks like code generation and summarization. In particular, the most recent coding Large Language Models seem to perform well on these problems. In this technical report, we aim to review how these models perform in code explanation/summarization, while also investigating their code generation capabilities (based on natural language descriptions). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19032 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Models for Code Summarization Szalontai, Balázs Szalay, Gergő Márton, Tamás Sike, Anna Pintér, Balázs Gregorics, Tibor Artificial Intelligence Machine Learning Programming Languages Software Engineering Recently, there has been increasing activity in using deep learning for software engineering, including tasks like code generation and summarization. In particular, the most recent coding Large Language Models seem to perform well on these problems. In this technical report, we aim to review how these models perform in code explanation/summarization, while also investigating their code generation capabilities (based on natural language descriptions). |
| title | Large Language Models for Code Summarization |
| topic | Artificial Intelligence Machine Learning Programming Languages Software Engineering |
| url | https://arxiv.org/abs/2405.19032 |