Breaking the Myth: Can Small Models Infer Postconditions Too?

• URL: http://arxiv.org/abs/2507.10182v1
• Date: Mon, 14 Jul 2025 11:44:04 GMT
• Title: Breaking the Myth: Can Small Models Infer Postconditions Too?
• Authors: Gehao Zhang, Zhenting Wang, Juan Zhai,
• Abstract summary: We show that a small, fine-tuned language model can achieve high-quality postcondition generation with much lower computational costs.<n>Our approach tackles real-world repository dependencies and preserves pre-state information, allowing for expressive and accurate specifications.
• Score: 15.725275719200303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Formal specifications are essential for ensuring software correctness, yet manually writing them is tedious and error-prone. Large Language Models (LLMs) have shown promise in generating such specifications from natural language intents, but the giant model size and high computational demands raise a fundamental question: Do we really need large models for this task? In this paper, we show that a small, fine-tuned language model can achieve high-quality postcondition generation with much lower computational costs. We construct a specialized dataset of prompts, reasoning logs, and postconditions, then supervise the fine-tuning of a $7$B-parameter code model. Our approach tackles real-world repository dependencies and preserves pre-state information, allowing for expressive and accurate specifications. We evaluate the model on a benchmark of real-world Java bugs (Defects4J) and compare against both proprietary giants (e.g., GPT-4o) and open-source large models. Empirical results demonstrate that our compact model matches or outperforms significantly larger counterparts in syntax correctness, semantic correctness, and bug-distinguishing capability. These findings highlight that targeted fine-tuning on a modest dataset can enable small models to achieve results formerly seen only in massive, resource-heavy LLMs, offering a practical and efficient path for the real-world adoption of automated specification generation.

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