How Software Composition Analysis Works

SCA tools begin by creating a comprehensive inventory of application components. This discovery process analyzes various sources including package manager files (package.json, pom.xml, requirements.txt), lock files that specify exact versions, binary files containing embedded dependencies, and container images with their layered components. Modern SCA tools can even identify components without explicit declarations by analyzing file signatures and code patterns.

Vulnerability identification relies on multiple data sources to ensure comprehensive coverage. Tools query public databases like the National Vulnerability Database, GitHub Advisory Database, and vendor-specific security feeds. Commercial tools often maintain proprietary vulnerability databases with earlier disclosure or additional context. Machine learning models increasingly supplement traditional matching by identifying potentially vulnerable code patterns even before formal CVE assignment.

License analysis represents another crucial SCA capability. Open-source licenses create legal obligations that can impact commercial software distribution. SCA tools identify all component licenses and flag potential conflicts or compliance issues. This analysis helps organizations avoid legal risks while ensuring they meet open-source licensing obligations. Advanced tools can even analyze license compatibility across the entire dependency tree.