  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.
