A feature-model-driven LLM pipeline that generates synthetic requirements data improves defect classification when combined with real data, but the headline gains rest on a 40-sample test set with high variance.
Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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.
fields
cs.SE 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models
A feature-model-driven LLM pipeline that generates synthetic requirements data improves defect classification when combined with real data, but the headline gains rest on a 40-sample test set with high variance.