Breaking the Myth: Can Small Models Infer Postconditions Too?
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911054350516224 |
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| author | Zhang, Gehao Wang, Zhenting Zhai, Juan |
| author_facet | Zhang, Gehao Wang, Zhenting Zhai, Juan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10182 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Breaking the Myth: Can Small Models Infer Postconditions Too? Zhang, Gehao Wang, Zhenting Zhai, Juan Software Engineering Artificial Intelligence 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. |
| title | Breaking the Myth: Can Small Models Infer Postconditions Too? |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2507.10182 |