LibEvolutionEval: A Benchmark and Study for Version-Specific Code Generation

Sachit Kuhar, Wasi Uddin Ahmad, Zijian Wang, Nihal Jain, Haifeng Qian, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras

Abstract: Recent advancements in code completion models have primarily focused on local file contexts. However, these studies do not fully capture the complexity of real-world software development, which often requires the use of rapidly-evolving public libraries. To address this gap, we introduce LibEvolutionEval, a comprehensive study that emphasizes the need to understand library evolution to perform accurate in-line code completions. LibEvolutionEvaloffers a version-specific code-completion task across eight libraries as they evolve over the years, along with an in-depth analysis of the evolution of two widely used and well-maintained public libraries: PyTorch and Matplotlib. We evaluate several popular models and find that public library evolution significantly affects their performance. To mitigate this, we explored how retrieving version-specific library documentation and prompt-based techniques can enhance model capability in dealing with these fast-evolving packages. This suggests a promising path forward for better handling fast-evolving libraries. Our tasks will be made publicly available upon acceptance.