A machine-learning study on class imbalance in requirements engineering, using TF-IDF, SMOTE-Tomek, and leakage-safe stratified cross-validation.
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% ± 2.58%, substantially exceeding its baseline of 59.85% ± 2.52%. These results highlight the applicability and efficiency of machine learning models as scalable and interpretable solutions.
Applies SMOTE-Tomek only to the training folds inside stratified K-fold cross-validation, keeping validation folds untouched for a realistic evaluation.
Uses TF-IDF and classical machine-learning models to classify 12 PROMISE requirement types, including severely underrepresented non-functional categories.
Shows strong gains for Logistic Regression, reaching 76.16% +/- 2.58% accuracy and improved MCC, while preserving readable feature-coefficient analysis.
