Software Dependency Analysis Fundamentals

Dependency analysis is the practice of identifying, cataloging, and understanding all external code components that your software relies on to function. This includes libraries you explicitly install, transitive dependencies those libraries bring in, and even system-level packages your runtime environment requires.

Consider a typical Node.js web application. You might install 50 packages directly through npm install. But when you examine your node_modules folder, you find 1,500 packages. Those extra 1,450 packages are transitive dependencies—libraries that your direct dependencies need to function. Each one is code running in your production environment, subject to the same security and licensing considerations as code you wrote yourself.

Dependency analysis answers critical questions that every engineering team should be able to answer:

• What external code is running in production? Not just what you installed, but everything.
• Which versions of each component are deployed? Different versions have different vulnerabilities.
• Where did this code come from? npm? PyPI? A private registry? A forked repository?
• Who maintains these dependencies? Active community or abandoned project?
• What licenses govern their use? FOSS? Commercial? Viral copyleft?

Not all dependencies are equal. Understanding the different types helps you assess risk and prioritize remediation efforts.

Dependency analysis isn't just a security checkbox—it's a fundamental capability that affects reliability, compliance, and velocity.

Security: Knowing Your Attack Surface

Every dependency is code that runs with your application's privileges. A vulnerability in any dependency is a vulnerability in your application. In 2023, over 80% of application code typically comes from open-source dependencies, meaning your attack surface is largely outside your direct control.

Effective dependency analysis enables you to:

• Detect known vulnerabilities before deployment
• Prioritize remediation based on actual usage and exposure
• Track when new CVEs affect your deployed software
• Respond quickly to zero-day disclosures

Compliance: Meeting Regulatory Requirements

Modern compliance frameworks increasingly require software composition transparency:

• US Executive Order 14028 mandates SBOMs for software sold to federal agencies
• EU Cyber Resilience Act requires vulnerability disclosure and SBOM provision
• PCI-DSS 4.0 emphasizes software inventory and vulnerability management
• SOC 2 audits often examine dependency management practices

Without systematic dependency analysis, demonstrating compliance becomes an expensive, manual exercise repeated for every audit.

Reliability: Preventing Breaking Changes

Dependencies change. Maintainers release new versions, deprecate features, and sometimes abandon projects entirely. When a transitive dependency makes a breaking change, your application can fail in production even though you didn't change anything in your own codebase.

Dependency analysis provides the visibility needed to:

• Understand the impact of upgrades before applying them
• Identify abandoned or unmaintained packages
• Detect when dependency resolution produces unexpected versions
• Pin versions strategically to balance stability and security

Legal: Managing License Obligations

Open-source licenses come with terms. Some require attribution, some require source disclosure, and some are incompatible with commercial distribution. Every dependency—including transitives—brings its own license into your project.

Dependency analysis surfaces license information so legal teams can assess compliance before software ships, rather than discovering conflicts during due diligence or litigation.

Dependency analysis combines data extraction with graph construction and enrichment. Here's the process:

1. Manifest Parsing

The first step is extracting declared dependencies from manifest files. Each package ecosystem has its own format:

2. Lock File Resolution

Manifest files often use version ranges (^1.0.0, ~2.3.0, >=1.0.0). Lock files capture the exact versions resolved at install time. For accurate analysis, you need both: manifests tell you intent; lock files tell you reality.

Lock files are essential because the same manifest can resolve to different versions on different days, as new packages are published. This is why the industry moved toward deterministic builds with lock files as a standard practice.

3. Transitive Resolution

Each direct dependency has its own dependencies. Resolving the complete dependency graph requires:

• Fetching dependency metadata for each package from the registry
• Applying version constraints from all packages that depend on a shared library
• Resolving conflicts when multiple packages require incompatible versions
• Building the complete graph of what actually gets installed

This is computationally expensive. Modern package managers cache metadata locally to speed repeated resolutions.

4. Metadata Enrichment

Raw package names and versions aren't enough for decision-making. Useful analysis requires enrichment:

• Vulnerability data: CVE numbers, severity scores (CVSS), exploit availability
• License information: SPDX identifiers, license text, compatibility
• Maintainer activity: Last release date, commit frequency, maintainer count
• Popularity metrics: Download counts, GitHub stars, dependent project count
• Provenance: Where the package was published from, signing status

5. Graph Construction

The final output is a directed graph where nodes are packages and edges represent dependencies. This graph structure enables queries that flat lists cannot answer:

• "Show me all paths from my application to this vulnerable package"
• "If I upgrade package X, what else might break?"
• "Which of my services share this dependency?"
• "What's the shortest path to introduce a fix for this CVE?"

Dependency analysis can be performed manually (running npm list and reviewing output) or automated through specialized tools. The choice depends on scale and requirements:

If you're new to systematic dependency analysis, here's a practical starting path:

1. Inventory your projects. Before analyzing dependencies, you need a complete list of repositories and deployable units. This step alone often reveals forgotten or shadow IT projects.
2. Generate SBOMs. Use ecosystem-native tools (npm ls --json, pip freeze,go mod graph) or cross-platform tools like Syft or CycloneDX generators.
3. Scan for vulnerabilities. Feed your SBOM into a vulnerability scanner. Free options include npm audit, pip-audit, and Trivy.
4. Establish a baseline. Document your current state: how many dependencies, how many known vulnerabilities, what's your oldest unmaintained package?
5. Integrate into CI/CD. Automate SBOM generation and vulnerability scanning as part of your build pipeline. Block deployments for critical/high severity CVEs.
6. Build organizational visibility. This is where platforms like Li'nage Cloud add value— aggregating dependency data across services, visualizing relationships, and enabling impact analysis at the organization level.