A machine learning approach for hierarchical classification of software requirements

Manal Binkhonain, Liping Zhao (Corresponding)

Research output: Contribution to journalArticlepeer-review

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.

Methods:
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 using 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.
Original languageEnglish
Article number100457
JournalMachine Learning with Applications
Volume12
Early online date27 Feb 2023
DOIs
Publication statusPublished - 15 Jun 2023

Keywords

  • Requirements engineering
  • Requirements classification
  • Machine learning
  • Hierarchical classification
  • Imbalanced classes
  • High Dimensional Data with Low Sample Size (HDLSS)

Fingerprint

Dive into the research topics of 'A machine learning approach for hierarchical classification of software requirements'. Together they form a unique fingerprint.

Cite this