# PassionNet: An Innovative Framework for Duplicate and Conflicting Requirements Identification

arXiv: https://arxiv.org/abs/2412.01657 · HTML: https://mlsanity.com/paper/2412.01657/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 75 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://mlsanity.com/paper/2104.12250/) (2021)
- [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://mlsanity.com/paper/2003.10555/) (2020)
- [Physics-informed Learning for Identification and State Reconstruction of Traffic Density](https://mlsanity.com/paper/2103.13852/) (2021)
- [Automatically detecting the conflicts between software requirements based on finer semantic analysis](https://mlsanity.com/paper/2103.02255/) (2021)
- [Transfer learning for conflict and duplicate detection in software requirement pairs](https://mlsanity.com/paper/2301.03709/) (2023)
- [Dronology: An Incubator for Cyber-Physical System Research](https://mlsanity.com/paper/1804.02423/) (2018)
- [Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs](https://mlsanity.com/paper/2305.09608/) (2023)
- [Few-shot learning for sentence pair classification and its applications in software engineering](https://mlsanity.com/paper/2306.08058/) (2023)
- [A Prompt-based Few-shot Learning Approach to Software Conflict Detection](https://mlsanity.com/paper/2211.02709/) (2022)
- [Requirements Elicitation in Government Projects: A Preliminary Empirical Study](https://mlsanity.com/paper/2404.05425/) (2024)

## Built on by (most cited first, 2 of 2)

- [GVD: Governed Versioning and Deduplication for Document Repositories](https://mlsanity.com/paper/2609.17696/) (2026)
- [Towards Energy-aware Requirements Dependency Classification: Knowledge-Graph vs. Vector-Retrieval Augmented Inference with SLMs](https://mlsanity.com/paper/2603.23954/) (2026)

## Abstract (arXiv)

Early detection and resolution of duplicate and conflicting requirements can significantly enhance project efficiency and overall software quality. Researchers have developed various computational predictors by leveraging Artificial Intelligence (AI) potential to detect duplicate and conflicting requirements. However, these predictors lack in performance and requires more effective approaches to empower software development processes. Following the need of a unique predictor that can accurately identify duplicate and conflicting requirements, this research offers a comprehensive framework that facilitate development of 3 different types of predictive pipelines: language models based, multi-model similarity knowledge-driven and large language models (LLMs) context + multi-model similarity knowledge-driven. Within first type predictive pipelines landscape, framework facilitates conflicting/duplicate requirements identification by leveraging 8 distinct types of LLMs. In second type, framework supports development of predictive pipelines that leverage multi-scale and multi-model similarity knowledge, ranging from traditional similarity computation methods to advanced similarity vectors generated by LLMs. In the third type, the framework synthesizes predictive pipelines by integrating contextual insights from LLMs with multi-model similarity knowledge. Across 6 public benchmark datasets, extensive testing of 760 distinct predictive pipelines demonstrates that hybrid predictive pipelines consistently outperforms other two types predictive pipelines in accurately identifying duplicate and conflicting requirements. This predictive pipeline outperformed existing state-of-the-art predictors performance with an overall performance margin of 13% in terms of F1-score