TICoder: A Repository-Level Code Generation Framework with Test-Driven Planning and Implementation-Aware Reuse

• URL: http://arxiv.org/abs/2606.08135v1
• Date: Sat, 06 Jun 2026 12:23:30 GMT
• Title: TICoder: A Repository-Level Code Generation Framework with Test-Driven Planning and Implementation-Aware Reuse
• Abstract summary: We propose TICoder, a novel repository-level code generation framework that improves both planning and reuse.<n> TICoder introduces a test-driven iterative planning mechanism that leverages test cases as behavioral specifications to refine implementation steps.<n>We then identify relevant usage patterns through a dual-stage selection strategy, combining structure-based clustering and perplexity-based filtering.
• Score: 6.560303090465954
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Repository-level code generation with Large Language Models (LLMs) remains challenging, primarily due to complex dependencies and limited context windows. Recent approaches adopt retrieval-augmented generation (RAG) and the planning mechanism to reuse potential callee functions in the repository. However, these approaches often suffer from two limitations: lack of test-driven behavioral guidance during planning and overlooking the implementation logic embedded in repository code during reuse. As a result, generated plans may not align with expected behaviors, and retrieved functions may not be effectively reused. In this paper, we propose TICoder, a novel repository-level code generation framework that improves both planning and reuse. TICoder introduces a test-driven iterative planning mechanism that leverages test cases as behavioral specifications to refine implementation steps. Furthermore, TICoder employs an implementation-aware code reuse strategy, which retrieves potential callee functions using a dual-view similarity that captures both functional and implementation aspects. We then identify relevant usage patterns through a dual-stage selection strategy, combining structure-based clustering and perplexity-based filtering. We conduct extensive experiments on widely used repository-level code generation benchmarks with various LLMs. Experimental results demonstrate that TICoder outperforms state-of-the-art (SOTA) methods, achieving an average improvement of 11.52%.

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