A Systematic Literature Review on Software Testing Prediction Models

Abstract

Software testing prediction models play a critical role in improving software quality, reducing faults, and optimizing testing. Although past studies show that these models have improved over time, little focus has been given to them. Many studies rely on limited datasets that do not fully capture the complexity of real software systems, which limits how well the models can generalize. There is also insufficient evidence on how these models use existing datasets in practical, real-life settings, since most evaluations are conducted under controlled or experimental conditions. In addition, key aspects such as interpretability, generalizability, and practical usability are still not adequately addressed, which reduces trust and slows down adoption in practice. This study presents a systematic literature review of recent empirical research on predictive approaches in software testing, focusing on machine learning, deep learning, and hybrid techniques. A structured methodology was used, including clearly defined inclusion and exclusion criteria, systematic searches across major academic databases, quality assessment, and data extraction from 22 selected studies. The analysis considered model types, datasets, feature methods, evaluation metrics, and methodological approaches. The findings show a shift from traditional statistical models to advanced artificial intelligence techniques, including graph neural networks, contrastive learning, and deep fuzzy clustering. In addition, optimization and data balancing techniques improve predictive performance, while explainable artificial intelligence enhances model interpretability. However, challenges such as limited cross-project generalization and insufficient industrial validation still exist.