  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.
