A comprehensible analysis of the efficacy of Ensemble Models for Bug Prediction

TL;DR

This paper compares single and ensemble AI models for bug prediction in Java code, showing that ensemble models generally outperform individual models and can improve software reliability.

Contribution

It provides a comprehensive analysis demonstrating the effectiveness of ensemble AI models over single models in bug prediction tasks.

Findings

Ensemble AI models outperform individual models in bug prediction.

Factors influencing ensemble model performance are identified.

Ensemble models enhance software reliability.

Abstract

The correctness of software systems is vital for their effective operation. It makes discovering and fixing software bugs an important development task. The increasing use of Artificial Intelligence (AI) techniques in Software Engineering led to the development of a number of techniques that can assist software developers in identifying potential bugs in code. In this paper, we present a comprehensible comparison and analysis of the efficacy of two AI-based approaches, namely single AI models and ensemble AI models, for predicting the probability of a Java class being buggy. We used two open-source Apache Commons Project's Java components for training and evaluating the models. Our experimental findings indicate that the ensemble of AI models can outperform the results of applying individual AI models. We also offer insight into the factors that contribute to the enhanced performance of the ensemble AI model. The presented results demonstrate the potential of using ensemble AI models to enhance bug prediction results, which could ultimately result in more reliable software systems.