A Machine Learning Based Ensemble Method for Automatic Multiclass Classification of Decisions

TL;DR

This paper presents an ensemble machine learning approach to automatically classify software development decisions into five types, improving documentation and understanding of decisions during the software lifecycle.

Contribution

It introduces an ensemble classification method optimized for decision type classification, demonstrating its superiority over base classifiers with specific feature selection and extraction techniques.

Findings

Ensemble classifiers outperform base classifiers when well constructed.

Feature selection significantly improves classification accuracy.

Best results achieved with BoW + 50% features, combining NB, LR, and SVM.

Abstract

Stakeholders make various types of decisions with respect to requirements, design, management, and so on during the software development life cycle. Nevertheless, these decisions are typically not well documented and classified due to limited human resources, time, and budget. To this end, automatic approaches provide a promising way. In this paper, we aimed at automatically classifying decisions into five types to help stakeholders better document and understand decisions. First, we collected a dataset from the Hibernate developer mailing list. We then experimented and evaluated 270 configurations regarding feature selection, feature extraction techniques, and machine learning classifiers to seek the best configuration for classifying decisions. Especially, we applied an ensemble learning method and constructed ensemble classifiers to compare the performance between ensemble classifiers and base classifiers. Our experiment results show that (1) feature selection can decently improve the classification results; (2) ensemble classifiers can outperform base classifiers provided that ensemble classifiers are well constructed; (3) BoW + 50% features selected by feature selection with an ensemble classifier that combines Na\"ive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM) achieves the best classification result (with a weighted precision of 0.750, a weighted recall of 0.739, and a weighted F1-score of 0.727) among all the configurations. Our work can benefit various types of stakeholders in software development through providing an automatic approach for effectively classifying decisions into specific types that are relevant to their interests.