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LibEvolutionEval: A benchmark and study for version-specific code generation

By Sachit Kuhar, Wasi Ahmad, Zijian Wang, Nihal Jain, Haifeng Qian, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras
2025
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Recent advancements in code completion models have primarily focused on local file contexts (Ding et al., 2023b; Jimenez et al., 2024). However, these studies do not fully capture the complexity of real-world software development, which often requires the use of rapidlyevolving public libraries. To fill the gap, we introduce LIBEVOLUTIONEVAL, a detailed study requiring an understanding of library evolution to perform in-line code completion accurately. LIBEVOLUTIONEVAL provides a versionspecific code-completion task comprised of eight libraries (torch, torchvision, scipy, pil, tqdm, pyyaml, matplotlib, and pandas) as they evolve over the year along with a detailed analysis of the evolution of two popular and well-maintained public libraries: PyTorch and Matplotlib. We evaluate popular public models and find that public library evolution significantly influences model performance. We explored mitigation methods by studying how retrieved version-specific library documentation and prompting can improve the model’s capability in handling these fastevolving packages, paving a promising future path in better handling fast-evolving libraries.
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