Static Code Analysis definition
Static code analysis is the automated examination of source code without running it, to find bugs, security vulnerabilities, style violations and maintainability problems early. Tools such as ESLint, SonarQube, Semgrep and CodeQL parse code into structures they can reason about, then report issues in the editor or in CI before changes reach production.
How static analysis works
Analyzers parse source code into an abstract syntax tree and often build control flow and data flow graphs on top. Simple rules match patterns, such as an unused variable or a comparison that is always true. Deeper analysis follows values through the program, for example tracing user input from an HTTP request to a database query to detect SQL injection without ever running the code.
Because nothing executes, static analysis can check every path, including error branches that tests rarely reach, and it runs in seconds on each change. The trade-off is false positives: tools sometimes flag code that is actually safe, so tuning rules is a normal part of adoption rather than a sign the tool is broken.
Types of static analysis tools
The term covers a family of tools that most teams combine, each catching a different class of problem. Many run both in the developer's editor, giving instant feedback while typing, and as required checks in the pull request pipeline, so nothing merges with unresolved findings.
Choosing tools is mostly about your stack and risk profile. A TypeScript web app might combine ESLint, the TypeScript compiler, Semgrep and a secret scanner, while a regulated fintech adds a SAST platform with audit-ready reports. Common categories:
- Linters such as ESLint, Pylint, RuboCop and SwiftLint for bugs, risky patterns and style
- Type checkers such as TypeScript, mypy and the Kotlin and Swift compilers
- Code quality platforms such as SonarQube tracking complexity, duplication and coverage
- SAST (static application security testing) tools such as Semgrep, CodeQL, Checkmarx and Snyk Code
- Infrastructure as code scanners such as Checkov and Trivy for misconfigured cloud resources
- Secret scanners such as Gitleaks and GitHub secret scanning for leaked keys
Static vs dynamic analysis
Static analysis examines code at rest; dynamic analysis tests a running application, for example with DAST scanners that probe a staging site or with penetration testing. Static tools find issues early and point to the exact line, but cannot see runtime configuration or how components behave together. Dynamic tools see real behavior but only on the paths they exercise, so mature security programs use both, plus dependency scanning.
Static analysis also complements human code review. When tools enforce formatting, catch common bugs and flag risky patterns automatically, reviewers can spend their attention on design, business logic and the questions no tool can answer.
Adopting static analysis without drowning in warnings
Turning on every rule in a large legacy codebase produces thousands of warnings that everyone ignores. Start with a small, high-value rule set, fail builds only on new issues in changed code, and fix existing findings gradually. Show results in pull requests rather than separate dashboards, and suppress false positives with a comment explaining why.
Nexzem runs linters, type checks and SAST in CI on client projects, so issues are caught while the change is still fresh in the author's mind and long before they become production incidents or findings in a security audit.