Building the Dependency Graph

Once components are discovered, SCA tools construct comprehensive dependency graphs that map relationships between components. This process involves recursively analyzing each component to identify its dependencies, creating a tree structure that can extend many levels deep. The challenge lies in accurately resolving version constraints, handling conflicting requirements, and dealing with optional or conditional dependencies that might only activate under specific conditions.

Version resolution follows the rules of each package management ecosystem, which vary significantly. npm uses semantic versioning with complex resolution algorithms that can result in multiple versions of the same package. Maven employs a "nearest definition wins" strategy that can mask vulnerable versions. Python's pip allows flexible version specifications that complicate analysis. SCA tools must understand and correctly implement these ecosystem-specific rules to accurately model what components actually load at runtime.

The dependency graph serves multiple purposes beyond simple visualization. It enables impact analysis—when a vulnerability is discovered, the graph shows all affected applications and components. It identifies redundant or conflicting dependencies that might cause issues. It reveals "deep dependencies" that pose particular risk due to their widespread use. Modern SCA tools optimize these graphs for quick traversal and update, enabling real-time analysis even for applications with thousands of dependencies.