Static vs Dynamic Code Analysis

When developing or reviewing your code, you should know when to use static vs dynamic code analysis. In this article, we cover their differences, explaining what each method can and cannot detect. You’ll learn the sub-categories and the most popular tools.

We will also discuss how a high-performing DevOps team combines static analysis and dynamic analysis.

Static code analysis is an automated software review method that checks your code without running the program.

You usually run static checks as you write code to avoid defects early on. Most static analysis uses automated rules to catch obvious mistakes and patterns that reviewers can miss.

Static analysis typically checks for:

• Code style: Checks and enforces rules that keep the codebase readable, like naming conventions, indentation, file structure, and import order.
• Dead code: Identifies code that can’t run or branches that became unreachable for the program (such as old helper functions left behind after a refactor).
• Control flow and logic: Flags patterns that cause bugs, like contradictory conditions, missing default cases in decision statements, or infinite loops.
• Misuse: Detects incorrect use of language features, libraries, and application programming interfaces (APIs), like passing the wrong value type or ignoring return values that signal a failure.
• Security patterns: Scans for common risky patterns, like hard-coded secrets, inconsistent input validation, and unsafe string building.
• Architecture: Checks imports and dependencies to see which parts of the code reach into another module’s internal code.
• Maintainability: Finds code that is too hard to understand or change safely, like overly long functions, deeply nested conditions, or duplicated code blocks.

When to use static code analysis

Developers use static code analysis to find issues in the code before it’s fully compiled for deployment or when it’s inefficient to run. These include situations such as:

• Gating changes early: Running static analysis checks on every pull request (PR) helps catch predictable issues before they enter the main branch.
• Speeding up human review: Catching stylistic and other rule-based issues allows reviewers to focus on design choices, logic, and security vulnerabilities.
• Protecting large refactorings: Detecting broken usage and unsafe patterns immediately helps as you rename, split, or move code across codebases.
• Keeping consistent coding standards: Applying the same rules across your repositories en masse helps when many developers contribute simultaneously.
• Catching issues without realistic data: Checking for basic issues helps when you can’t run the code in production-like scenarios.
• Shortening the codebase: Decluttering your codebase by removing dead code, unused modules, and unreachable branches reduces the chance that developers mistakenly update them.
• Reducing risks in the source code: Finding hard-coded secrets, inconsistent input validation, and risky data handling patterns helps prevent someone from committing them during testing.
• Preventing accidental coupling: Detecting code that reaches into internal parts of another component or service helps when there’s no need.
• Catching easily missed problems: Making sure the codebase doesn’t have issues in “broken” branches helps because dynamic tests won’t analyze them.

While it helps find mistakes and risky patterns early, static analysis cannot see what happens when the program runs. A function can pass static rules and still fail to account for edge cases or map fields incorrectly. Similarly, a static checker can flag a pattern as too risky without knowing another part of the system protects it at runtime.

Developers can choose from numerous static checkers to analyze the codebase.

Popular static code analysis tools

Static analysis tools come in different types based on the types of rules they check. Below are the most widely used tools in each group.

Formatters

Formatters rewrite your source files into consistent formatting, standardizing the layout, line breaks, indentation, spacing, and quote styles.

• Prettier: Formats JavaScript, TypeScript, and JSON codebases
• Black: Enforces style rules in Python
• gofmt: Standardizes Go code

Linters

Linters run in code editors and in PRs to detect common rule violations, such as unused variables, suspicious conditions, and inconsistent logic.

• ESLint: Enforces rules in JavaScript and TypeScript
• Pylint: Enforces consistency in Python
• golangci lint: Runs multiple Go linters in one command

Type checkers

Type checkers verify that code passes expected values without mismatches, like passing a string where a number is expected or calling a function with missing fields.

• TypeScript compiler: Checks types in TypeScript
• mypy: Adds static type checking to Python projects

Code quality checkers

Cde quality checkers scan the code for duplicated logic, overly complex functions, risky exception handling, and commonly known defective patterns.

• SonarQube: Analyzes multiple programming languages for reliability, security, and maintainability and supports quality gates (blocks merges that don’t meet your quality criteria)
• PMD: Rules-based analyzer often used for Java code

Dataflow analyzers

These tools track how data moves through code across function calls to detect untrusted inputs that call for dangerous operations (like building SQL queries or executing system commands).

• CodeQL: A query-based analyzer that can analyze how data moves in your systems

Security scanners

Security scanners detect secure code patterns, enforce policies, and monitor the codebase for vulnerabilities in real time.

• Semgrep: Pattern-driven scanner for various programming languages with shareable and customizable rule sets
• Checkmarx SAST: An enterprise-grade static application security testing (SAST) platform that enforces scanning across projects
• Fortify Static Code Analyzer: A SAST tool that can categorize vulnerabilities and provide remediation guidance
• Gitleaks: A secret scanner that can detect hard-coded credentials, including private keys and tokens

Dependency checkers

Dependency checks reveal parts of your system that depend on other parts. Some of these tools can detect vulnerabilities in third-party packages that interact with your code.

• ArchUnit: Allows Java teams to write tests that enforce architecture rules and boundaries
• Nx module boundaries: Enforces boundaries in JavaScript and TypeScript monorepos
• OWASP Dependency Check: Scans dependencies against known vulnerability databases

Of course, the software analysis doesn’t stop at the code. You should also see how it runs.

Dynamic (runtime) code analysis checks software while it runs. You usually do it after writing executable workflows to see what happens as the code runs with real or simulated traffic.

Dynamic analysis commonly checks for:

• Runtime errors: Logs crashes, exceptions, and error spikes, recording where they happened and what triggered them.
• Integration behavior: Captures errors that happen as the software interacts with external systems, such as databases, caches, message queues, and third-party APIs.
• Security: Applies both safe and unsafe inputs to verify it enforces data handling and access verification rules.
• Performance: Sends heavy traffic to the software to measure its throughput and response time, which helps detect bottlenecks.
• Efficiency: Shows which parts of the code consume the most time and compute resources during operations.
• Memory: Tracks memory use while the app runs to catch leaks and unusually high usage.
• Post-run artifacts: Reviews unusual patterns in logs, suspicious changes across metrics, and other anomalies that produce non-critical errors you can easily miss.

When to use dynamic code analysis

Dynamic checks reveal problems you can’t uncover by reading the code, such as:

• Validating release behavior: Confirming workloads behave correctly with real inputs in a production-like runtime environment.
• Finding environment-specific failures: Finding bugs that can happen only with specific operating systems, virtualization environments, deployment settings, or third-party APIs.
• Verifying integration contracts: Catching incorrect behavior that may only surface when the software calls databases and external services.
• Diagnosing timing and concurrency bugs: Detecting failures that appear when two operations access the same data (race conditions) or get stuck waiting for each other (deadlocks).
• Detecting resource inefficiencies: Identifying memory usage or resource allocation problems that slow the system down over time.
• Confirming security controls: Exposing gaps in authentication, authorization, and input handling during real user inputs or while simulating a hacker attack.
• Tracing incidents to a root cause: Recreating errors found in your logs, traces, and monitoring reports, so you can identify their causes (exact functions, request parameters, event sequences, and more).

Dynamic analysis also has limits. It needs runnable builds, test cases, and a realistic environment. It won’t work if your tests don’t cover a workflow or your app isn’t ready to run.

Teams lean on different dynamic analysis tools to cover all possible failure types.

Popular dynamic code analysis tools

Like static checkers, dynamic code analysis tools are divided into several categories:

Debuggers

A debugger can pause a program while it runs, so developers can see how the behavior changes one step at a time.

• Gdb: A debugger for low-level languages (such as C and C++)
• Lldb: A commonly used debugger for macOS and LLVM toolchains, primarily used for C-family languages and Swift
• Chrome DevTools debugger: A Chrome-based debugger for JavaScript

Runtime profilers

Profilers measure the time each function takes to run, which helps developers analyze what caused slowdowns.

• Java VisualVM: A tool that can inspect and profile running Java programs with in-depth information about CPU, thread, and memory behavior
• Python cProfile: A runtime profiler for Python codebases
• Valgrind Memcheck: A memory profiler that shows how programs allocate memory and whether they keep using resources after no longer needing them
• pprof: A profiling toolkit used widely in Go (and services integrated in this language)

Thread analyzers

Thread analyzers (concurrency analyzers) monitor how operations access shared data, reporting locks, races, and deadlocks with evidence that could’ve caused the issues.

• ThreadSanitizer: An analyzer that finds data races and synchronization bugs during testing
• Valgrind Helgrind: A tool to detect concurrency problems in multi-threaded native programs

Observability platforms

Observability platforms monitor running systems over time, collecting key performance indicators, logs, and request traces.

• New Relic: A platform that monitors your application and infrastructure across environments
• AppDynamics: Software that traces transactions (services, calls, requests, and more) across distributed services

Code coverage

Coverage tools detect which lines and branches of code ran during tests and which logic branches were ignored.

• JaCoCo: A coverage tool for Java that integrates with build tools and continuous integration (CI) systems
• coverage.py: Coverage measurement software for Python tests

Load and stress testers

These tools allow you to simulate different usage loads so you can see how your software responds.

• Apache JMeter: A load testing tool with various protocols and in-depth performance reporting
• Locust: A Python-based load tester that allows writing customer behavior scripts

Application security testing tools

These tools scan your system for security vulnerabilities like injections, missing headers, exposed API endpoints, and attempts to exploit your apps.

• OWASP ZAP: An open-source security scanner that detects vulnerabilities in web apps
• Contrast Security IAST: An interactive application security testing (IAST) tool that can detect risky operations that run without proper validation
• Dynatrace: A runtime application self-protection (RASP) program that monitors how your app handles requests to detect exploit attempts

The table below compares static and dynamic code analysis tools with their key differences and use cases.

In most development projects, you don’t choose whether to use static or dynamic checks. You actually have to use both.

Static and dynamic code analysis complement each other because your software can fail on two levels.

Sometimes the problem is only with the code. However, some issues surface when the software interacts with databases or handles multiple requests at once.

DevOps teams adopt the Shift Left approach to move static and dynamic checks earlier. In practice, it works like this:

• When a developer commits code, formatters and linters check the formatting.
• Type and style checkers detect obvious mismatches, and security tools flag risky patterns.
• Once the code is executable, the team runs automated tests and runtime checks in a realistic test environment.
• After release, the team monitors errors, latency, and suspicious security signals at runtime.
• When runtime issues appear, the team traces them back to the code, applies fixes, and runs another series of tests.

This combined approach works because each technique addresses the other’s blind spots. But teams can go further and implement code checks into the CI/CD pipeline.

Static and dynamic analysis tools work inside your continuous integration and continuous deployment (CI/CD) pipeline to check code commits.

Static analysis tools provide feedback on every PR. They help you catch issues before anyone spends time building, deploying, or running tests.

Dynamic analysis runs on a built application. Teams implement it for automated runtime testing to verify the app’s behavior where static analysis can’t.

Static and dynamic analysis reinforce each other in CI/CD. Static tests can show risky inputs, which can tell you what test cases to add; runtime failures reveal what static rules you can add to block the same defect next time.

However, adding all your code checks to an entire CI/CD pipeline can be too costly and time-consuming.

You shouldn’t run heavy scans on every commit or use strict gates for every type of mistake. To prevent as many defects as possible without hurting delivery, you should know when to implement dynamic vs static code analysis.

• Add an integrated development environment (IDE) or pre-commit hooks to run formatters, linters, and key security rules locally (before the code reaches the repository).
• Use fast static checks on every pull request, but reserve slower scans and heavier runtime checks for merges, release pipelines, and nightly runs.
• Separate fresh issues from baseline (legacy) findings in different backlogs so developers can focus on newly introduced changes.
• Define which failure criteria should stop a build and which should only alert errors.
• Use static analysis logs to understand which parts of the code need additional runtime checks. (For example, add edge case testing to paths with risky input handling.)
• Put runtime checks on parts that static tools can’t check, such as execution paths, external integrations, and security validation.
• Regularly update rules for dynamic or static checks based on recurring failures.
• Revisit your rules over time to remove ineffective checks.

Static and dynamic analysis look simple on paper. One only inspects the code, and the other checks how it runs.

Skilled DevOps teams use static and dynamic analysis to cover as much of the software as possible. Static checks block the most obvious issues before merges, then runtime analysis determines how the app runs. If runtime failures repeat, teams add new static rules and test cases.

DevCom provides source code audit and review services that cover both types of reviews. Contact us if you want to strengthen your codebase and tighten your development workflow.