Automatic Generation of Python Programs Using Context-Free Grammars
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917610326589440 |
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| author | Yamani, Kamel Naïr, Marwa Baghdadi, Riyadh |
| author_facet | Yamani, Kamel Naïr, Marwa Baghdadi, Riyadh |
| contents | In recent years, data has emerged as the new gold, serving as a powerful tool for creating intelligent systems. However, procuring high-quality data remains challenging, especially for code. To address this, we developed TinyPy Generator, a tool that generates random Python programs using a context-free grammar. The generated programs are guaranteed to be correct by construction. Our system uses custom production rules (in the Backus-Naur Form (BNF) format) to recursively generate code. This allows us to generate code with different levels of complexity, ranging from code containing only assignments to more complex code containing conditionals and loops. Our proposed tool enables effortless large-scale Python code generation, beneficial for a wide range of applications. TinyPy Generator is particularly useful in the field of machine learning, where it can generate substantial amounts of Python code for training Python language models. Additionally, researchers who are studying programming languages can utilize this tool to create datasets for their experiments, which can help validate the robustness of code interpreters or compilers. Unlike existing research, we have open-sourced our implementation. This allows customization according to user needs and extends potential usage to other languages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06503 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automatic Generation of Python Programs Using Context-Free Grammars Yamani, Kamel Naïr, Marwa Baghdadi, Riyadh Programming Languages Computation and Language Machine Learning In recent years, data has emerged as the new gold, serving as a powerful tool for creating intelligent systems. However, procuring high-quality data remains challenging, especially for code. To address this, we developed TinyPy Generator, a tool that generates random Python programs using a context-free grammar. The generated programs are guaranteed to be correct by construction. Our system uses custom production rules (in the Backus-Naur Form (BNF) format) to recursively generate code. This allows us to generate code with different levels of complexity, ranging from code containing only assignments to more complex code containing conditionals and loops. Our proposed tool enables effortless large-scale Python code generation, beneficial for a wide range of applications. TinyPy Generator is particularly useful in the field of machine learning, where it can generate substantial amounts of Python code for training Python language models. Additionally, researchers who are studying programming languages can utilize this tool to create datasets for their experiments, which can help validate the robustness of code interpreters or compilers. Unlike existing research, we have open-sourced our implementation. This allows customization according to user needs and extends potential usage to other languages. |
| title | Automatic Generation of Python Programs Using Context-Free Grammars |
| topic | Programming Languages Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2403.06503 |