GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909382203146240 |
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| author | Islah, Nizar Gehring, Justine Misra, Diganta Muller, Eilif Rish, Irina Zhuo, Terry Yue Caccia, Massimo |
| author_facet | Islah, Nizar Gehring, Justine Misra, Diganta Muller, Eilif Rish, Irina Zhuo, Terry Yue Caccia, Massimo |
| contents | The rapid evolution of software libraries presents a significant challenge for code generation models, which must adapt to frequent version updates while maintaining compatibility with previous versions. Existing code completion benchmarks often overlook this dynamic aspect, and the one that does consider it relies on static code prediction tasks without execution-based evaluation, offering a limited perspective on a model's practical usability. To address this gap, we introduce \textbf{\GitChameleon{}}, a novel, manually curated dataset comprising 116 Python code completion problems, each conditioned on specific library versions and accompanied by executable unit tests. \GitChameleon{} is designed to rigorously assess the ability of modern large language models (LLMs) to generate version-specific code that is not only syntactically correct but also functionally accurate upon execution. Our comprehensive evaluations reveal that state-of-the-art LLMs struggle with this task; for instance, \textbf{GPT-4o} achieves a pass@10 of only 39.9\% (43.7\% when provided with error feedback), highlighting the complexity of the problem and the limitations of current models. By providing an execution-based benchmark that emphasizes the dynamic nature of code libraries, \GitChameleon{} serves as a critical tool to advance the development of more adaptable and reliable code generation models. For facilitation for further exploration of version-conditioned code generation, we make our code repository publicly accessible at \url{https://github.com/NizarIslah/GitChameleon}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05830 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models Islah, Nizar Gehring, Justine Misra, Diganta Muller, Eilif Rish, Irina Zhuo, Terry Yue Caccia, Massimo Software Engineering Machine Learning The rapid evolution of software libraries presents a significant challenge for code generation models, which must adapt to frequent version updates while maintaining compatibility with previous versions. Existing code completion benchmarks often overlook this dynamic aspect, and the one that does consider it relies on static code prediction tasks without execution-based evaluation, offering a limited perspective on a model's practical usability. To address this gap, we introduce \textbf{\GitChameleon{}}, a novel, manually curated dataset comprising 116 Python code completion problems, each conditioned on specific library versions and accompanied by executable unit tests. \GitChameleon{} is designed to rigorously assess the ability of modern large language models (LLMs) to generate version-specific code that is not only syntactically correct but also functionally accurate upon execution. Our comprehensive evaluations reveal that state-of-the-art LLMs struggle with this task; for instance, \textbf{GPT-4o} achieves a pass@10 of only 39.9\% (43.7\% when provided with error feedback), highlighting the complexity of the problem and the limitations of current models. By providing an execution-based benchmark that emphasizes the dynamic nature of code libraries, \GitChameleon{} serves as a critical tool to advance the development of more adaptable and reliable code generation models. For facilitation for further exploration of version-conditioned code generation, we make our code repository publicly accessible at \url{https://github.com/NizarIslah/GitChameleon}. |
| title | GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models |
| topic | Software Engineering Machine Learning |
| url | https://arxiv.org/abs/2411.05830 |