codeql
- Mikal Doles (Deactivated)
CodeQL is a semantic code analysis engine and a query language developed by GitHub (formerly Semmle, which was acquired by GitHub in 2019). It is designed for analyzing and finding security vulnerabilities in source code. CodeQL allows you to create queries to inspect code repositories and identify patterns or issues in the code, such as security vulnerabilities, code smells, and other potential problems.
Key features and use cases of CodeQL include:
Code Security Analysis: CodeQL is particularly useful for identifying security vulnerabilities in code. It can help you find vulnerabilities like SQL injection, cross-site scripting (XSS), and other common security issues.
Customizable Queries: You can create custom queries to analyze code repositories. These queries are written in a domain-specific language called QL (Query Language), and they allow you to define specific coding patterns, security rules, or best practices to check for.
Integration with Development Workflow: CodeQL can be integrated into the software development lifecycle. It can automatically scan code repositories and report issues to developers, allowing for early detection and resolution of problems.
Cross-Language Analysis: CodeQL supports multiple programming languages, including C, C++, C#, Java, JavaScript, Python, and more. This versatility makes it suitable for analyzing a wide range of codebases.
Community and Open Source: CodeQL has an active community of users and contributors. It's available as open-source software, and the community often shares queries and rules for various programming languages and code analysis purposes.
GitHub Security Features: GitHub has integrated CodeQL into its security features. It powers the "Code scanning" and "Code scanning alerts" functionality on GitHub, allowing developers to receive automated security analysis results directly in their pull requests.
CodeQL is especially valuable for organizations and projects that prioritize code security and want to proactively find and address security vulnerabilities and other code quality issues. It helps automate and scale the process of code analysis and is particularly effective when integrated into the development pipeline, providing quick feedback to developers during the code review process.