Auto-generated unit testing using PCA-aDynaMOSA

Abstract

Automated test case generation is essential for improving software quality; however, many-objective search-based testing approaches often experience scalability issues when the number of test objectives increases. This condition leads to slower convergence, higher computational effort, and reduced ability to cover complex program structures. To address this gap, this study proposes an enhanced version of the aDynaMOSA algorithm by incorporating principal component analysis (PCA) to reduce redundant objectives during the search process. The proposed method preserves essential objective information while eliminating dependency noise that typically slows the evolutionary search. Experiments were conducted using the SF110 benchmark dataset through EvoSuite, and the approach was compared with standard many-objective search strategies. The findings demonstrate that PCA-based objective reduction can improve performance, achieving up to 3.87%, 5.50%, and 3.75% for coverage of line, branch, and mutation respectively. These results indicate that dimensionality reduction can significantly enhance scalability and efficiency in automated evolutionary test generation, providing a foundation for future adaptive and hybrid optimization strategies.