Comparative Analysis of Ensemble Learning Methods for Software Reliability Prediction
TL;DR
The results demonstrate that ensemble methods provide superior performance in identifying reliability levels, and which classification method is most suitable for predicting software reliability based on code metrics such as Cyclomatic Complexity and Halstead Volume.
Abstract
Ensuring software reliability is a fundamental requirement in the software development lifecycle, as early detection of potential failures significantly reduces maintenance costs. The literature documents several mathematically based classification techniques that provide developers with a versatile toolkit. Among these are Logistic Regression, Random Forest, Extra Tree Classification, Gaussian Naive Bayes, XGB Classifier, LGBM Classifier, CatBoost Classifier, and Voter Classifier. The present research aims to identify which classification method is most suitable for predicting software reliability based on code metrics such as Cyclomatic Complexity and Halstead Volume. Therefore, the research will use relevant datasets and graphics in its analysis with the goal of identifying the most effective classification approach from the techniques cited. The study evaluates various models including Logistic Regression, Random Forest, XGBoost, and Voting Classifier. The results demonstrate that ensemble methods provide superior performance in identifying reliability levels. Its primary purpose is to ensure the resulting data pattern is immune to compromise. Many options are available when it comes to mathematical classification types. As indicated in the literature, these include, but are not limited to, the examples previously listed.