Unseen-Codebases-Domain Data Synthesis and Training Based on Code Graphs

TL;DR

This paper introduces UCD-Training, a novel two-stage training framework that synthesizes reasoning-aware data from unseen codebases using code graphs, improving large language models' understanding of new software environments.

Contribution

The paper presents UCD-Training, a new method combining dependency-preserving pretraining and graph-grounded fine-tuning with synthesized data for better reasoning on unseen codebases.

Findings

Enhanced code generation performance on unseen codebases.

Effective reasoning trace incorporation improves model understanding.

New benchmark, UnseenCodeBench, for evaluating unseen codebase reasoning.

Abstract

In the context of newly release software frameworks, large language models (LLMs) often exhibit poor performance and a high rate of hallucination, as they are not exposed to such environments during training. Although inference-time augmentation techniques such as retrieval-augmented generation (RAG) can partially mitigate hallucinations, knowledge injection through prompting alone is insufficient to enable models to fully understand the intrinsic relationships among different components of a codebase, or to reason about the correct compositions and apply. Although explicit knowledge injection can be achieved through post-training, compared with public code domains, unseen codebases typically provide only source code and lack large volumes of high-quality, usage-oriented code that can be directly leveraged as training data. Consequently, existing data synthesis approaches are insufficient to adequately capture unseen codebases usage scenarios when restricted to source code alone. To address these challenges, we propose UCD-Training, a two-stage training framework for reasoning-aware data synthesis grounded in a code graph constructed from unseen codebases. UCD-Training first parses the source code to build a code graph, then conducts dependency-preserving continued pretraining (CPT) using file-level dependency data, followed by graph-grounded supervised fine-tuning (SFT) on three types of synthesized data augmented with explicit reasoning traces: (1) single-hop relation reasoning data, (2) compositional API reasoning data, and (3) codebase utilization data. We further introduce a new benchmark, UnseenCodeBench, for code generation on unseen codebases and conduct comprehensive experiments across multiple codebases.