<![CDATA[Static testing has become a critical approach in ensuring the quality, security, and reliability of software systems. Recent developments include the application of machine learning, abstract interpretation, and query-based approaches to improve the effectiveness of analysis. Objectives and methods: This systematic review aims to consolidate and analyse findings from five recent studies (2024-2025) on static testing methods across various domains, including source code analysis, vulnerability detection, Android malware detection, business process modelling, and data leakage prevention in machine learning. The methods used were thematic and comparative analysis of the contributions, methodologies, and limitations of each study. Main results: The synthesis shows that static testing approaches are increasingly integrating dynamic techniques, machine learning, and formal analysis to address the complexity of modern systems. However, challenges such as limited coverage, the need for industry validation, and computational complexity remain obstacles. Conclusions and implications: Static testing continues to evolve with hybrid and data-driven approaches. Further research is needed that focuses on expanding coverage, integrating industrial pipelines, and improving accessibility for practitioners.]]>
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