Cybersecurity 101 – DevSecOps: Code Linting

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πŸ“ Code lints are the pre-commit hooks in DevSecOps pipeline to automate checking the code for the following:
πŸ“Œ detect code errors that can lead to a security vulnerabilities
πŸ“Œ check for β€œbad code smells” flagging programming errors, bugs, style, and construct errors
πŸ“Œ suggest code quality improvements
πŸ“Œ identify unreachable codes
πŸ“Œ help dereferencing null pointers
πŸ“Œ help to measure quality & complexity metrics
πŸ“Œ compare against security focused coding standards

πŸ›‘οΈ Few open-source linting tools
πŸ“Œ JSLint, JSHint & ESLint: Popular JavaScript linters that can also be used with other languages such as TypeScript.
πŸ“Œ SonarLint: A static code analysis tool that can be used to perform code analysis, measure technical debt, and detect security vulnerabilities.
πŸ“Œ Pylint & Flake8: Linters for Python that can help detect errors and improve code quality.
πŸ“Œ RuboCop: A code linting tool for the Ruby programming language that can help enforce coding conventions and best practices.
πŸ“Œ Checkstyle: A development tool to help programmers write Java code that adheres to a coding standard for Java
πŸ“Œ Clang-Tidy: A clang-based C++ linter tool that checks for coding errors and style issues in C++ code.
πŸ“Œ TFLint & KubeLinter: Linters for Infrastructure as Code. TFLinter used for terraform code reviews & KubeLinter for analyzing K8S Yaml files

πŸ›‘οΈ Benefits of using Code Linters:
πŸ“Œ Immediate feedback to developers while coding on standard & security vulnerabilities
πŸ“Œ Improves code quality, accelerates development, reduces time & cost
πŸ“Œ Results in readable code which makes code understandable & secure
πŸ“Œ Enforces coding standard including error & exception handling

πŸ›‘οΈ Code Linters challenges:
πŸ“Œ Different linters for different languages & frameworks
πŸ“Œ false-positives and information overload for developers

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