What Works Better? A Study of Classifying Requirements

TL;DR

This study evaluates machine learning techniques for classifying requirements into functional and non-functional categories, proposing preprocessing methods that enhance classification accuracy and comparing various algorithms' effectiveness.

Contribution

It introduces a preprocessing approach for requirements and systematically compares multiple machine learning methods for requirement classification.

Findings

Preprocessing improves classification performance.

Significant differences found among algorithms like LDA, Biterm Topic Modeling, Naive Bayes.

Study conducted on 625 requirements from the OpenScience tera-PROMISE repository.

Abstract

Classifying requirements into functional requirements (FR) and non-functional ones (NFR) is an important task in requirements engineering. However, automated classification of requirements written in natural language is not straightforward, due to the variability of natural language and the absence of a controlled vocabulary. This paper investigates how automated classification of requirements into FR and NFR can be improved and how well several machine learning approaches work in this context. We contribute an approach for preprocessing requirements that standardizes and normalizes requirements before applying classification algorithms. Further, we report on how well several existing machine learning methods perform for automated classification of NFRs into sub-categories such as usability, availability, or performance. Our study is performed on 625 requirements provided by the OpenScience tera-PROMISE repository. We found that our preprocessing improved the performance of an existing classification method. We further found significant differences in the performance of approaches such as Latent Dirichlet Allocation, Biterm Topic Modeling, or Naive Bayes for the sub-classification of NFRs.