Static vs Dynamic Code Analysis: Key Differences and How to Use Both in DevSecOps

Originally published on April 7, 2015; fully revised and updated.

Static and dynamic code analysis are two complementary ways to improve software quality and security. This guide is for developers, QA engineers, and DevOps teams who want to understand when to use each method, what each one catches best, and how to wire both into a CI/CD pipeline.

Static vs dynamic analysis at a glance

Static analysis inspects source code, bytecode, or configuration without executing the application. Dynamic analysis evaluates application behavior while the software is running (for example during tests or in a test environment).

What each method is best at

Where static analysis helps most

• Catches issues early, often before code is merged.
• Scales well across large repositories and frequent commits.
• Enforces coding standards and secure coding rules consistently.
• Supports fast feedback loops in pull requests.

Where dynamic analysis helps most

• Finds defects that appear only when code executes.
• Validates behavior under realistic inputs and environments.
• Exposes issues tied to runtime state, concurrency, and integration boundaries.
• Helps verify whether fixes actually resolve observed runtime failures.

In practice, high-performing teams use both approaches because each one covers blind spots the other cannot fully address.

Workflow: combining static and dynamic analysis in CI/CD

A common pattern is to run static checks first (fast fail), then run dynamic checks after build/deploy to a test target.

flowchart TD
    A[Developer commit] --> B[Static analysis job]
    B --> C{Static issues}
    C -- yes --> D[Fail pipeline and fix code]
    C -- no --> E[Build and deploy to test]
    E --> F[Runtime and security tests]
    F --> G{Runtime issues}
    G -- yes --> D
    G -- no --> H[Release approval]

Example commands for both approaches

The commands below are representative examples. Adjust tools and flags for your language, framework, and build environment.

# Static analysis examples
semgrep --config auto .
eslint src/

# Runtime or dynamic analysis examples
go test ./...
go test -race ./...
valgrind --leak-check=full ./bin/myserviceCode language: PHP (php)

name: quality-and-runtime-checks
on:
  pull_request:

jobs:
  verify:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Static analysis
        run: semgrep --config auto .
      - name: Unit tests
        run: go test ./...
      - name: Race detection
        run: go test -race ./...Code language: PHP (php)

Tooling categories compared

Best practices

• Run static checks on every pull request to catch issues early.
• Run dynamic checks in repeatable test environments, not ad hoc.
• Treat findings as triageable data: prioritize by risk and exploitability.
• Fail builds on high-severity issues, but avoid noisy rules that teams ignore.
• Track trend metrics over time (new issues, fixed issues, recurring classes).
• Use both methods together for better coverage and fewer late surprises.

Rajesh Kumar

I’m Rajesh Kumar, a DevOps, SRE, DevSecOps, Cloud, and Platform Engineering expert passionate about sharing practical knowledge, real-world experiences, and industry best practices. I have worked at Cotocus and regularly write about technology, travel, investing, health, product reviews, and digital marketing through my various platforms.

I publish technical articles at DevOps School, travel stories at Holiday Landmark, stock market insights at Stocks Mantra, health and fitness guidance at My Medic Plus, product reviews at TrueReviewNow, and SEO and digital marketing strategies at Wizbrand.