A Machine Learning Approach for Hierarchical Classification of Software Requirements

TL;DR

This paper introduces HC4RC, a novel hierarchical machine learning method that effectively addresses class imbalance and high-dimensional, low-sample-size issues in software requirements classification, outperforming traditional and deep learning approaches.

Contribution

The paper presents HC4RC, a new hierarchical ML approach that combines semantic feature selection and dataset decomposition to improve classification under challenging data conditions.

Findings

HC4RC effectively handles class imbalance and HDLSS problems.

HC4RC outperforms traditional statistical and deep learning models.

The approach is simple to implement and practical for real-world RE tasks.

Abstract

Context: Classification of software requirements into different categories is a critically important task in requirements engineering (RE). Developing machine learning (ML) approaches for requirements classification has attracted great interest in the RE community since the 2000s. Objective: This paper aims to address two related problems that have been challenging real-world applications of ML approaches: the problems of class imbalance and high dimensionality with low sample size data (HDLSS). These problems can greatly degrade the classification performance of ML methods. Method: The paper proposes HC4RC, a novel ML approach for multiclass classification of requirements. HC4RC solves the aforementioned problems through semantic-role-based feature selection, dataset decomposition and hierarchical classification. We experimentally compare the effectiveness of HC4RC with three closely related approaches - two of which are based on a traditional statistical classification model whereas one uses an advanced deep learning model. Results: Our experiment shows: 1) The class imbalance and HDLSS problems present a challenge to both traditional and advanced ML approaches. 2) The HC4RC approach is simple to use and can effectively address the class imbalance and HDLSS problems compared to similar approaches. Conclusion: This paper makes an important practical contribution to addressing the class imbalance and HDLSS problems in multiclass classification of software requirements.