Evaluating Incompatible Third-party Library API Usage in LLM-based Code Completion
TL;DR
An automated framework that builds a versioned TPL API Knowledge Base and a large-scale benchmark that proposes two lightweight solutions, Real-time Detection and Lightweight Repair, to mitigate ITAUs and provide a foundation for more compatibility-aware code generation in evolving software ecosystems.
Abstract
Large Language Models (LLMs) have advanced code completion, but their ability to generate API usages compatible with evolving third-party libraries (TPLs) remains uncertain. As TPL APIs frequently change, LLMs risk producing code incompatible with installed library versions, causing build failures or incorrect behaviors. We define such issues as Incompatible Third-party Library API Usage (ITAU) and conduct a systematic study to evaluate how state-of-the-art LLMs handle this challenge. To this end, we propose an automated framework that builds a versioned TPL API Knowledge Base and a large-scale benchmark with 10,867 realistic code completion tasks. Through comprehensive evaluation of six state-of-the-art LLMs, we find that even top-performing models frequently generate incompatible completions. We further propose two lightweight solutions, Real-time Detection and Lightweight Repair, to mitigate ITAUs. This framework and benchmark provide a foundation for more compatibility-aware code generation in evolving software ecosystems.