PCREQ: Automated Inference of Compatible Requirements for Python Third-party Library Upgrades

• URL: http://arxiv.org/abs/2508.02023v1
• Date: Mon, 04 Aug 2025 03:34:30 GMT
• Title: PCREQ: Automated Inference of Compatible Requirements for Python Third-party Library Upgrades
• Authors: Huashan Lei, Guanping Xiao, Yepang Liu, Zheng Zheng,
• Abstract summary: Python third-party libraries (TPLs) are essential in modern software development, but upgrades often cause compatibility issues, leading to system failures.<n>Existing tools mainly detect dependency conflicts but overlook code-level incompatibilities.<n>We propose PCREQ, the first approach to automatically infer compatible requirements by combining version and code compatibility analysis.
• Score: 5.857193811761703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Python third-party libraries (TPLs) are essential in modern software development, but upgrades often cause compatibility issues, leading to system failures. These issues fall into two categories: version compatibility issues (VCIs) and code compatibility issues (CCIs). Existing tools mainly detect dependency conflicts but overlook code-level incompatibilities, with no solution fully automating the inference of compatible versions for both VCIs and CCIs. To fill this gap, we propose PCREQ, the first approach to automatically infer compatible requirements by combining version and code compatibility analysis. PCREQ integrates six modules: knowledge acquisition, version compatibility assessment, invoked APIs and modules extraction, code compatibility assessment, version change, and missing TPL completion. PCREQ collects candidate versions, checks for conflicts, identifies API usage, evaluates code compatibility, and iteratively adjusts versions to generate a compatible requirements.txt with a detailed repair report. To evaluate PCREQ, we construct REQBench, a large-scale benchmark with 2,095 upgrade test cases (including 406 unsolvable by pip). Results show PCREQ achieves a 94.03% inference success rate, outperforming PyEGo (37.02%), ReadPyE (37.16%), and LLM-based approaches (GPT-4o, DeepSeek V3/R1) by 18-20%. PCREQ processes each case from REQBench in 60.79s on average, demonstrating practical efficiency. PCREQ significantly reduces manual effort in troubleshooting upgrades, advancing Python dependency maintenance automation.

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