Code That Works, Environments That Don't: Measuring Environment Reproducibility in AI-Generated Software

Excerpt

arXiv:2610.00425v1 Announce Type: cross Abstract: Code generation has emerged as a central capability of large language models, with coding agents now able to produce functionally correct software projects from natural language prompts. However, functional correctness alone does not capture a critical dimension of generation quality: environment specification, defined as the accurate identification of the dependencies required to execute generated code, is equally critical. We develop an agent p