# Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs

arXiv: https://arxiv.org/abs/2305.09608 · HTML: https://mlsanity.com/paper/2305.09608/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 12 of 12)

- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Data Augmentation using Pre-trained Transformer Models](https://mlsanity.com/paper/2003.02245/) (2020)
- [Data Augmentation for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1705.00440/) (2017)
- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge](https://mlsanity.com/paper/1908.07245/) (2019)
- [AEDA: An Easier Data Augmentation Technique for Text Classification](https://mlsanity.com/paper/2108.13230/) (2021)
- [Improving short text classification through global augmentation methods](https://mlsanity.com/paper/1907.03752/) (2019)
- [Local Additivity Based Data Augmentation for Semi-supervised NER](https://mlsanity.com/paper/2010.01677/) (2020)
- [Supervised Semantic Similarity-based Conflict Detection Algorithm: S3CDA](https://mlsanity.com/paper/2206.13690/) (2022)

## Built on by (most cited first, 3 of 3)

- [PassionNet: An Innovative Framework for Duplicate and Conflicting Requirements Identification](https://mlsanity.com/paper/2412.01657/) (2024)
- [Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models](https://mlsanity.com/paper/2505.03265/) (2025)
- [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)

This paper explores the use of text data augmentation techniques to enhance conflict and duplicate detection in software engineering tasks through sentence pair classification. The study adapts generic augmentation techniques such as shuffling, back translation, and paraphrasing and proposes new data augmentation techniques such as Noun-Verb Substitution, target-lemma replacement and Actor-Action Substitution for software requirement texts. A comprehensive empirical analysis is conducted on six software text datasets to identify conflicts and duplicates among sentence pairs. The results demonstrate that data augmentation techniques have a significant impact on the performance of all software pair text datasets. On the other hand, in cases where the datasets are relatively balanced, the use of augmentation techniques may result in a negative effect on the classification performance.