论文标题
基于监督语义相似性的冲突检测算法：S3CDA
Supervised Semantic Similarity-based Conflict Detection Algorithm: S3CDA
论文作者
论文摘要
确定冲突的要求是软件需求工程的关键挑战，在自动解决方案中通常被忽略。大多数现有的方法都依赖于手工制作的规则或努力跨不同领域的概括。在本文中，我们介绍了S3CDA，这是一种旨在自动检测软件需求冲突的两阶段算法。我们的方法首先使用语义相似性确定了潜在的冲突需求对，然后通过分析重叠域特异性实体来验证它们。我们评估了五个不同现实世界数据集的S3CDA，并将其与流行的大型语言模型（如GPT-4O，Llame-3，Sonnet-3.5和Gemini-1.5）进行了比较。尽管LLMS表现出希望，尤其是在一般数据集上，但S3CDA在具有更高性能的域特异性设置中持续表现更好。我们的发现表明，将自然语言处理（NLP）技术与域感知的见解相结合，为需求中的冲突检测提供了一种实用有效的替代方法。
Identifying conflicting requirements is a key challenge in software requirement engineering, often overlooked in automated solutions. Most existing approaches rely on handcrafted rules or struggle to generalize across different domains. In this paper, we introduce S3CDA, a two-phase algorithm designed to automatically detect conflicts in software requirements. Our method first identifies potentially conflicting requirement pairs using semantic similarity, and then validates them by analyzing overlapping domain-specific entities. We evaluate S3CDA on five diverse real-world datasets and compare it against popular large language models like GPT-4o, Llama-3, Sonnet-3.5 and Gemini-1.5. While LLMs show promise, especially on general datasets, S3CDA consistently performs better in domain-specific settings with higher performance. Our findings suggest that combining Natural Language Processing (NLP) techniques with domain-aware insights offers a practical and effective alternative for conflict detection in requirements.
