A Comprehensive Taxonomy of Static Analysis Techniques for Modern Software Verification

Abstract

Static analysis has become a foundational discipline in modern software verification, enabling the detection of software defects, security vulnerabilities, and reliability issues without executing programs. Over time, static analysis has evolved from simple pattern-matching approaches into sophisticated mathematical frameworks capable of supporting safety-critical and large-scale industrial systems. This article presents a comprehensive taxonomy of major static analysis methodologies, including data-flow analysis, symbolic execution, abstract interpretation, model checking, and constraint solving. The study examines the theoretical foundations, technical characteristics, practical applications, and representative tools associated with each analytical paradigm. It further explores modern industrial tools such as SonarQube, CodeQL, Clang Static Analyzer, Frama-C, Infer, and Coverity, highlighting their roles in software quality assurance, cybersecurity, embedded systems, and cloud-native infrastructures. The article also analyzes the trade-offs between scalability, precision, soundness, and computational complexity that shape tool selection and practical adoption. Finally, the study concludes that hybrid verification strategies integrating multiple static analysis approaches represent the future direction of software verification in increasingly complex and safety-critical software ecosystems.