Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Environment-Aware Code Generation: How far are We?

Tongtong Wu, Rongyi Chen, Wenjie Du, Suyu Ma, Guilin Qi, Zhenchang Xing, Shahram Khadivi, Ramesh Periyathambi, Gholamreza Haffari

TL;DR

The paper tackles the challenge of environment-aware code generation by formalizing EACG, introducing the VersiBCB benchmark, and evaluating three adaptation strategies—RAG, MoE, and Memory—against tasks that require functional executability across diverse library versions. VersiBCB combines real-world Python environments with execution-verified generation and migration tasks across multiple libraries and versions, enabling evaluation of adaptability to deprecations and cross-version changes. Empirical results show that current LLMs struggle under environment constraints, but memory-augmented and mixture-of-experts approaches can improve executability and compatibility, with memory showing particular strength for migration and partial robustness to unseen configurations. The findings underscore the need for continual, software-aligned LLMs and runtime-aware integration to bridge the gap between research benchmarks and production software engineering needs, and they point to future work in runtime introspection, container-level profiling, and ecosystem expansion.

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.

Environment-Aware Code Generation: How far are We?

TL;DR

The paper tackles the challenge of environment-aware code generation by formalizing EACG, introducing the VersiBCB benchmark, and evaluating three adaptation strategies—RAG, MoE, and Memory—against tasks that require functional executability across diverse library versions. VersiBCB combines real-world Python environments with execution-verified generation and migration tasks across multiple libraries and versions, enabling evaluation of adaptability to deprecations and cross-version changes. Empirical results show that current LLMs struggle under environment constraints, but memory-augmented and mixture-of-experts approaches can improve executability and compatibility, with memory showing particular strength for migration and partial robustness to unseen configurations. The findings underscore the need for continual, software-aligned LLMs and runtime-aware integration to bridge the gap between research benchmarks and production software engineering needs, and they point to future work in runtime introspection, container-level profiling, and ecosystem expansion.

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.
Paper Structure (28 sections, 2 equations, 7 figures, 9 tables)

This paper contains 28 sections, 2 equations, 7 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Motivation and Problem Formulation
  3. Problem Formulation
  4. VersiBCB
  5. Dataset Construction
  6. Dataset Statistics
  7. Domain Distribution
  8. Dataset Analysis
  9. LLM Customization Strategies
  10. Data-based Customization: RAG
  11. Parameter-based Customization: MoE
  12. Cache-based Customization: Memory
  13. Experimental Setup and Results
  14. RQ1: Can the generated or migrated code execute successfully in the specified environment? (Executability)
  15. RQ2: Are all API calls in the generated code consistent with the specified environment? (Compatibility)
  16. ...and 13 more sections

Figures (7)

  • Figure 1: Task definition covering both environment-aware code generation and code migration.
  • Figure 2: Overview of dataset construction via bidirectional environment traversal.
  • Figure 3: Distribution of VersiBCB tasks across domains, based on the presence of domain-specific libraries.
  • Figure 4: Three axes of environment-aware LLM customization. Left: Data-based (RAG) prepends retrieved context. Middle: Parameter-based (MoE) activates version-aware experts via gates. Right: Cache-based (Memory) injects layerwise key-value pairs learned from prior runs.
  • Figure 5: Task-wise Pass@1 across domains (e.g., data processing, computation, ML).
  • ...and 2 more figures