Impact of Large Language Models on Generating Software Specifications
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Software specifications are essential for ensuring the reliability of software systems. Existing specification extraction approaches, however, suffer from limited generalizability and require manual efforts. We study the effectiveness of Large Language Models (LLMs) in generating software specifications from software documentation, utilizing Few-Shot Learning (FSL) to enable LLMs to generalize from a small number of examples. We compare the performance of LLMs with FSL to that of state-of-the-art specification extraction techniques and study the impact of prompt construction strategies on LLM performance. In addition, we conduct a comprehensive analysis of their symptoms and root causes of the failures to understand the pros and cons of LLMs and existing approaches. We also compare 11 LLMs' performance, cost, and response time for generating software specifications. Our findings include that (1) the best performing LLM outperforms existing approaches by 9.1–13.7 a few similar examples, (2) the two dominant root causes combined (ineffective prompts and missing domain knowledge) result in 57–60 (3) most of the 11 LLMs achieve better or comparable performance compared to traditional techniques. Our study offers valuable insights for future research to improve specification generation.
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