PoTo: A Hybrid Andersen's Points-to Analysis for Python
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911721992486912 |
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| author | Rak-amnouykit, Ingkarat Milanova, Ana Baudart, Guillaume Hirzel, Martin Dolby, Julian |
| author_facet | Rak-amnouykit, Ingkarat Milanova, Ana Baudart, Guillaume Hirzel, Martin Dolby, Julian |
| contents | As Python is increasingly being adopted for large and complex programs, the importance of static analysis for Python (such as type inference) grows. Unfortunately, static analysis for Python remains a challenging task due to its dynamic language features and its abundant external libraries. To help fill this gap, this paper presents PoTo, an Andersen-style context-insensitive and flow-insensitive points-to analysis for Python. PoTo addresses Python-specific challenges and works for large programs via a novel hybrid evaluation, integrating traditional static points-to analysis with concrete evaluation in the Python interpreter for external library calls. Next, this paper presents PoTo+, a static type inference for Python built on the points-to analysis. We evaluate PoTo+ and compare it to two state-of-the-art Python type inference techniques: (1) the static rule-based Pytype and (2) the deep-learning based DLInfer. Our results show that PoTo+ outperforms both Pytype and DLInfer on existing Python packages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03918 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PoTo: A Hybrid Andersen's Points-to Analysis for Python Rak-amnouykit, Ingkarat Milanova, Ana Baudart, Guillaume Hirzel, Martin Dolby, Julian Programming Languages Software Engineering As Python is increasingly being adopted for large and complex programs, the importance of static analysis for Python (such as type inference) grows. Unfortunately, static analysis for Python remains a challenging task due to its dynamic language features and its abundant external libraries. To help fill this gap, this paper presents PoTo, an Andersen-style context-insensitive and flow-insensitive points-to analysis for Python. PoTo addresses Python-specific challenges and works for large programs via a novel hybrid evaluation, integrating traditional static points-to analysis with concrete evaluation in the Python interpreter for external library calls. Next, this paper presents PoTo+, a static type inference for Python built on the points-to analysis. We evaluate PoTo+ and compare it to two state-of-the-art Python type inference techniques: (1) the static rule-based Pytype and (2) the deep-learning based DLInfer. Our results show that PoTo+ outperforms both Pytype and DLInfer on existing Python packages. |
| title | PoTo: A Hybrid Andersen's Points-to Analysis for Python |
| topic | Programming Languages Software Engineering |
| url | https://arxiv.org/abs/2409.03918 |