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Static code analysis

(also static analysis, source code analysis)

Static code analysis definition

Static code analysis is a technique employed to examine and assess a computer program's source code without running it. The goal is to identify possible vulnerabilities, programming mistakes, and compliance with coding guidelines, thus enhancing the software's overall quality, security, and maintainability. Linters or static analyzers, also known as static code analysis tools, assist developers in automatically pinpointing these concerns.

See also: fuzz testing

Static code analysis examples

  • Code style checking: Analyzing source code for compliance with specific coding standards, such as naming conventions, indentation rules, and proper use of comments.
  • Security vulnerability detection: Identifying code patterns that may lead to exploitable vulnerabilities, such as buffer overflows, SQL injections, and cross-site scripting.
  • Performance issue detection: Detecting inefficient code that could cause performance bottlenecks or resource consumption issues.

Static code analysis vs. dynamic code analysis

Static code analysis inspects source code without executing it, while dynamic code analysis tests software during runtime. Both methods have their advantages and limitations. Static analysis can detect issues early in the development process, even before compiling the code, while dynamic analysis can reveal runtime issues not visible through static analysis alone.

Pros and cons of static code analysis

Pros:

  • Early detection of issues, reducing the cost and effort of fixing them later.
  • Improved code quality and maintainability.
  • Enhanced security by identifying potential vulnerabilities.

Cons:

  • Can produce false positives and negatives, requiring manual review and fine-tuning.
  • May not detect all issues, especially ones that only manifest during runtime.

Tips for using static code analysis

  • Integrate static analysis tools into your development environment and build processes for continuous feedback.
  • Customize the analysis rules to match your project's coding standards and requirements.
  • Regularly review and update the analysis configuration to stay current with evolving coding practices and vulnerability patterns.