Unit testing, as a critical means of ensuring software quality, is often constrained in practice by the high cost and low efficiency of manual test case construction, resulting in limited test coverage and scarcity of unit test cases in real-world projects. Traditional test generation tools can improve coverage but suffer from poor readability and limited generalization. In recent years, large language models (LLMs) have demonstrated strong potential in the field of test generation, owing to their powerful generalization and reasoning capabilities. However, the static nature of training data often causes hallucinations, undermining the reliability of generated tests. To address this, we propose MUATC, a multi-agent unit test generation framework based on LLMs. This work introduces, for the first time in coverage-driven LLM-based test generation, a multi-agent collaborative mechanism that integrates Chain-of-Thought reasoning and Retrieval-Augmented Generation to enhance both the quality and coverage of generated test cases. Additionally, we propose a unit test repair algorithm MTCRA aimed at further improving test coverage. The experimental results show that MUATC achieves 4.8%–5.5% higher coverage than Coverup, with performance gains independent of model architecture and programming languages. Compared with advanced LLM-based coverage enhancement tools such as ChatUniTest, TestPilot and Coverup, MUATC achieves a 12.7% improvement in test coverage on the benchmark dataset provided by ChatUniTest. To demonstrate the superior readability of test cases generated by MUATC, we conducted a readability study via the HumanEval platform. The results indicate that MUATC-generated test cases are significantly more readable than those produced by Pynguin. Therefore, to leverage the high readability of generated test cases, we also develop UnitTestPlat, a user-oriented platform for visualized unit test generation.

Code generation has advanced with large language models (LLMs), but LLMs still struggle with complex tasks, especially in competitive programming. These tasks require understanding complex problems, generating correct code that passes numerous test cases, and meeting tight time and memory limits. We observed that there are some critical hints provided by competition platforms, which often point to the most critical information needed to solve the problem, thus guiding participants to accurate solutions. Inspired by these observations, we propose TEACH1, an approach that tutors LLMs in solving competitive programming by combining critical hints with a structured Chain of Thought (CoT). The key insight of TEACH is to employ a domain-specialized hint generator that is fine-tuned on curated data from competitive programming platforms, enabling it to produce concise and targeted algorithmic hints. By integrating these hints into the reasoning process of LLMs, TEACH helps LLMs bridge the gap between complex tasks and solutions. Furthermore, TEACH simulates human problem-solving through a structured CoT that covers problem understanding, analysis, algorithm selection, and coding. We extensively evaluate TEACH on both proprietary (GPT-3.5, GPT-4o, Claude-3.5-Sonnet, Gemini-2.5-Flash) and open-source (DeepSeek-V3) LLMs. TEACH achieves up to 6.56 absolute (17.4% relative) gain in pass@1 on LeetCode, and demonstrates strong generalization to APPS and ASAC, with maximum pass@1 relative improvements of 17.6% and 26.9%, respectively. Furthermore, existing CoT methods with the hints generated from TEACH yield additional gains, demonstrating its compatibility and extensibility across models and prompting strategies.