Environment-Aware Code Generation: How far are We?

• URL: http://arxiv.org/abs/2601.12262v1
• Date: Sun, 18 Jan 2026 04:58:15 GMT
• Title: Environment-Aware Code Generation: How far are We?
• Authors: Tongtong Wu, Rongyi Chen, Wenjie Du, Suyu Ma, Guilin Qi, Zhenchang Xing, Shahram Khadivi, Ramesh Periyathambi, Gholamreza Haffari,
• Abstract summary: It is unclear whether large language models (LLMs) can reliably generate executable code tailored to a user's specific environment.<n>We present the first systematic study of Environment-Aware Code Generation (EACG), where generated code must be functionally correct and directly executable under arbitrary software configurations.<n>Our results show that current LLMs struggle with environment-specific code generation, while our adaptations improve environment compatibility and executability.
• Score: 52.69113158357018
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent progress in large language models (LLMs) has improved code generation, but most evaluations still test isolated, small-scale code (e.g., a single function) under default or unspecified software environments. As a result, it is unclear whether LLMs can reliably generate executable code tailored to a user's specific environment. We present the first systematic study of Environment-Aware Code Generation (EACG), where generated code must be functionally correct and directly executable under arbitrary software configurations. To enable realistic evaluation, we introduce VersiBCB, a benchmark that is multi-package, execution-verified, and deprecation-aware, capturing complex and evolving environments that prior datasets often overlook. Using VersiBCB, we investigate three complementary adaptation axes: data, parameters, and cache, and develop representative strategies for each. Our results show that current LLMs struggle with environment-specific code generation, while our adaptations improve environment compatibility and executability. These findings highlight key challenges and opportunities for deploying LLMs in practical software engineering workflows.

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