mlsanityData to 7 Oct 2026

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

arXiv 2305.09608 ↗First posted 16 May 2023cs.SE cs.LGcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 12 of its 23 references that are in the map. First posted on arXiv 16 May 2023.

Builds on

12 of 23 references.

References · 12

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  12. 2022Supervised Semantic Similarity-based Conflict Detection Algorithm: S3CDAcited by 5

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Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024PassionNet: An Innovative Framework for Duplicate and Conflicting Requirements Identificationcited by 2
  2. 2025Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Modelscited by 1
  3. 2025A transfer learning approach for automatic conflicts detection in software requirement sentence pairs based on dual encoderscited by 0