"Learning to Generate Unit Tests for Automated Debugging"

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https://arxiv.org/abs/2502.01619

The challenge lies in effectively debugging faulty code produced by LLMs due to the scarcity of unit tests that can pinpoint errors. This paper addresses the trade-off between creating unit test inputs that expose code errors and accurately predicting the correct output of these tests, especially without access to a perfect code solution.

This paper introduces a method named UTGen. UTGen trains LLMs to generate unit tests that are both error-revealing and have correct expected outputs. This is then integrated into UTDebug, a debugging pipeline that leverages these generated tests to improve LLM debugging capabilities.

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📌 UTGen innovatively addresses the unit test generation problem by training models to balance error detection and output correctness. This supervised approach using perturbed code and chain of thought rationale significantly improves the quality of generated tests.

📌 UTDebug effectively tackles noisy unit test feedback through self-consistency and backtracking. Test-time scaling enhances output accuracy, while validation-based backtracking prevents overfitting, creating a robust debugging pipeline.

📌 The paper highlights a critical trade-off: attack rate versus output accuracy in unit test generation. UTGen demonstrably improves upon zero-shot methods by explicitly training models to generate unit tests that are both adversarial and accurate.

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Methods Explored in this Paper 🔧:

→ The paper proposes UTGen, a training method for LLMs to generate unit tests.

→ UTGen focuses on creating unit tests that have two key properties: high attack rate and high output accuracy.

→ High attack rate means the generated unit test inputs are likely to reveal errors in faulty code.

→ High output accuracy ensures the predicted unit test output is correct, aligning with the task description.

→ UTGen is trained using a novel data creation recipe that starts from existing code generation datasets.

→ This recipe perturbs correct code to simulate errors, generates failing unit tests for these errors, and includes chain-of-thought rationales to improve output prediction.

→ The paper also introduces UTDebug, a debugging pipeline that uses UTGen generated tests.

→ UTDebug addresses the noisy nature of model-generated tests by using test-time compute scaling to improve unit test output accuracy.

→ UTDebug also uses a validation and backtracking strategy based on multiple generated unit tests to prevent overfitting to noisy feedback.

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Key Insights 💡:

→ A key insight is the identification of a trade-off between attack rate and output accuracy in unit test generation.

→ Zero-shot LLMs struggle to balance this trade-off, often excelling in one metric at the expense of the other.

→ Training with UTGen helps LLMs to better balance this trade-off, leading to unit tests that are both error-revealing and have accurate outputs.

→ Noisy feedback from generated unit tests can hinder debugging; UTDebug mitigates this through test-time scaling and backtracking.

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Results 📊:

→ UTGen outperforms baseline unit test generation methods by 7.59% in generating unit tests that are both error-revealing and have correct outputs.

→ Using UTGen-generated unit tests in UTDebug improves the pass@1 accuracy of Qwen-2.5 7B by over 3% on HumanEvalFix compared to other LLM-based unit test generation baselines.

→ On a harder debugging split of MBPP+, UTDebug with UTGen improves pass@1 accuracy of Qwen-2.5 7B by over 12.35% compared to other baselines.

→ Ablation studies show that both test-time scaling and backtracking in UTDebug are crucial for effective debugging with generated unit tests, with test-time scaling boosting accuracy by up to 11.4%.