Victor May
Victor May is a Staff Machine Learning Engineer at Google, focused on building and evaluating autonomous AI agents for software engineering.
Before focusing on AI agents, Victor spent nearly two decades leading teams and architecting large-scale, production ML systems for recommender systems and natural language processing at companies including Taboola and Chegg. He holds an M.Sc. in Applied Mathematics from Tel-Aviv University and has published his research in venues such as ACL and IEEE Transactions on Image Processing.
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GitChameleon 2.0: Evaluating AI Code Generation Against Python Library Version Incompatibilities
Diganta Misra
Nizar Islah
Brice Rauby
Zihan Wang
Justine Gehring
Antonio Orvieto
Muawiz Chaudhary
Eilif Muller
Irina Rish
Samira Ebrahimi Kahou
Massimo Caccia
2025
Preview abstract
The rapid evolution of software libraries poses a considerable hurdle for code generation, necessitating continuous adaptation to frequent version updates while preserving backward compatibility. While existing code evolution benchmarks provide valuable insights, they typically lack execution-based evaluation for generating code compliant with specific library versions. To address this, we introduce GitChameleon 2.0, a novel, meticulously curated dataset comprising 328 Python code completion problems, each conditioned on specific library versions and accompanied by executable unit tests. GitChameleon 2.0 rigorously evaluates the capacity of contemporary large language models (LLMs), LLM-powered agents, code assistants, and RAG systems to perform version-conditioned code generation that demonstrates functional accuracy through execution. Our extensive evaluations indicate that state-of-the-art systems encounter significant challenges with this task; enterprise models achieving baseline success rates in the 48-51% range, underscoring the intricacy of the problem. By offering an execution-based benchmark emphasizing the dynamic nature of code libraries, GitChameleon 2.0 enables a clearer understanding of this challenge and helps guide the development of more adaptable and dependable AI code generation methods.
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