Improving Requirements Classification with SMOTE-Tomek Preprocessing

TL;DR

This paper demonstrates that applying SMOTE-Tomek preprocessing with stratified cross-validation significantly improves requirements classification accuracy, especially for minority classes, using machine learning models like logistic regression.

Contribution

It introduces a novel combination of SMOTE-Tomek preprocessing with stratified cross-validation for requirements classification, enhancing minority class representation and model performance.

Findings

Logistic regression accuracy improved to 76.16%.

SMOTE-Tomek preprocessing outperforms baseline methods.

Effective handling of class imbalance in requirements data.

Abstract

This study emphasizes the domain of requirements engineering by applying the SMOTE-Tomek preprocessing technique, combined with stratified K-fold cross-validation, to address class imbalance in the PROMISE dataset. This dataset comprises 969 categorized requirements, classified into functional and non-functional types. The proposed approach enhances the representation of minority classes while maintaining the integrity of validation folds, leading to a notable improvement in classification accuracy. Logistic regression achieved 76.16\%, significantly surpassing the baseline of 58.31\%. These results highlight the applicability and efficiency of machine learning models as scalable and interpretable solutions.