# Supervised Semantic Similarity-based Conflict Detection Algorithm: S3CDA

arXiv: https://arxiv.org/abs/2206.13690 · HTML: https://mlsanity.com/paper/2206.13690/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2022.

## Built on by (most cited first, 5 of 5)

- [The Future of AI-Driven Software Engineering](https://mlsanity.com/paper/2406.07737/) (2024)
- [Transfer learning for conflict and duplicate detection in software requirement pairs](https://mlsanity.com/paper/2301.03709/) (2023)
- [Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs](https://mlsanity.com/paper/2305.09608/) (2023)
- [Empowering Business Transformation: The Positive Impact and Ethical Considerations of Generative AI in Software Product Management -- A Systematic Literature Review](https://mlsanity.com/paper/2306.04605/) (2023)
- [A transfer learning approach for automatic conflicts detection in software requirement sentence pairs based on dual encoders](https://mlsanity.com/paper/2511.23007/) (2025)

## Abstract (arXiv)

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.