BiVCoder: A Multi-Agent Framework for Code Generation via Bidirectional Code-Test Diagnosis

Abstract

Large Language Models (LLMs) have demonstrated remarkable potential in automated code generation. However, existing test-driven code generation and refinement frameworks are often hindered by the tests' quality: they typically treat self-generated tests as ground truth, leading to ineffective debugging loops where code is modified to satisfy erroneous tests. To address this, we propose BiVCoder, a diagnosis-driven multi-agent framework featuring a novel bidirectional code-test diagnosis mechanism. BiVCoder coordinates three specialized agents—Coding Agent, Test Agent, and Review Agent. Central to this architecture is the Review Agent, which serves as a diagnosis and decision-making hub. By integrating an MCP-based code-test execution tool, the Review Agent rigorously executes programs to diagnose failure root causes, distinguishing between implementation bugs and test case deficiencies, and subsequently triggers targeted repairs (Coding Agent or Test Agent). Furthermore, we introduce BiVCoder-SFT, a role-specific instruction fine-tuning scheme. We construct high-quality datasets to fine-tune the Qwen3-4B base model into specialized agents for coding, testing, and reviewing. Extensive experiments on HumanEval, MBPP, and their rigorous ''ET'' variants demonstrate the superiority of our approach. With GPT-3.5, BiVCoder achieves a Pass@1 of 77.3%. Notably, the specialized BiVCoder-SFT achieves an average score of 79.5% across the four datasets, not only outperforming its base model (69.7%) but also surpassing larger general-purpose models such as Qwen3-8B (77.8%). Additionally, BiVCoder boosts the performance of more powerful models, improving DeepSeek-V3.2 from 87.2% to 92.1% on HumanEval.