Static Vs. Dynamic Code Analysis

Modern software development teams are moving fast, pushing updates frequently, and working across multiple environments. With so much going on, keeping the code in check can be tricky.

However, code analysis is a must in the development process. It acts like a second (and third) pair of eyes, helping developers spot problems before they cause real headaches. Static and dynamic code analysis are two of the most common ways to do this. They might sound technical, but at their core, they help answer the same question: Is this code doing what it's supposed to do, and is it doing it safely and efficiently?

Static analysis checks your code without actually running it, whereas dynamic analysis watches how your code behaves when it's up and running.  "While each approach has its unique advantages, they are most effective when used together," said Arun Sreedharan, CEO of Zoondia. A combination of both helps catch bugs and improve code performance.

In the sections that follow, we'll break down what sets these two techniques apart and why combining them is a smart move.

What Is Static Code Analysis?

Static code analysis means checking the code without running it. In this technique, you scan the source code to find potential issues. There's no need to execute the program. Instead, static code analysis looks through the logic, structure, and syntax to catch things that might trip you later. 

Sreedharan explains that the static code analysis method "examines source code, bytecode, or binaries without executing the program. It helps detect vulnerabilities, security flaws, and coding standard violations early in the development process." Ultimately, this strategy allows developers to catch problems earlier, limiting the work required to fix them. 

Advantages of Static Code Analysis

A major advantage of static code analysis is the timing. The process catches problems early, often before the code gets compiled. So, it helps save time and a lot of debugging frustration down the road.

Static code analysis is good at spotting bugs and security vulnerabilities before your code runs. Plus, it flags deviations from coding standards, such as indentation and naming conventions, which can help maintain a consistent codebase.

Teams also maintain a clean code through static analysis. In highly regulated sectors, such as finance, this approach also supports compliance efforts.

Limitations of Static Code Analysis

The limitations of static code analysis stem from its inability to see how the behaves in real-world scenarios. It cannot detect runtime issues, which only take place when the code is actually running.

Sometimes, static analysis can also throw false positives. It may flag something as a problem even though it is not. Then, you may spend time debugging code that doesn't need it.

Since the code is static and doesn't interact with other parts, it can be possible to miss context-specific issues that only become clear when the code is executed. So, while static code analysis is a strong first line of defense, it works best when paired with its dynamic counterpart.

What Is Dynamic Code Analysis?

Dynamic code analysis takes a different approach than static analysis. Instead of looking at code in theory, developers use this technique to observe how the code behaves in real time while it's running.

"Dynamic analysis involves executing the application in a runtime environment,” explained Sreedharan. “This approach helps uncover runtime errors, memory leaks, and execution path issues that static analysis might miss." It's like taking a car out for a test drive instead of just reading its specifications.

Advantages of Dynamic Code Analysis

Since the code is in action, dynamic analysis can catch issues that only show up when the code runs. These include sneaky bugs that hide during development but crash the app at the worst time possible.

The process also allows real-time monitoring of how your application behaves under various conditions. For example, you can simulate heavy traffic to see if your app's performance degrades.

Dynamic analysis is also helpful in finding performance problems, such as memory leaks and excessive resource consumption, in live execution. Security vulnerabilities also become much easier to identify as dynamic analysis detects insecure coding practices and potential exploits.

Limitations of Dynamic Code Analysis

The main limitation of dynamic code analysis is the need for a working version of your app. So, you'll likely only be able to go through with this process later in development.

Plus, it can be resource-intensive and slow, especially with larger and more complex apps. In some cases, dynamic code analysis can miss problems that are only triggered during testing, such as race conditions or memory leaks. It can also overlook issues in unreachable code or third-party libraries.

When to Use Static vs Dynamic Code Analysis

The static vs. dynamic code analysis debate isn't about choosing one over the other. When used together, they cover more ground and reduce blind spots.

"Static analysis is implemented early in the development cycle to catch syntax errors, security flaws, and maintainability issues before code is executed," said Sreedharan. In contrast, "[d]ynamic analysis is used during testing or staging to identify runtime vulnerabilities, performance bottlenecks, and security threats," he shares.

While it's best to use their hybrid rather than one of them, the important thing is knowing when to use static and when to opt for dynamic analysis.

When To Use Static Code Analysis

Static code analysis is best suited for the early stages of development when you're writing and reviewing code. It helps you enforce coding standards from day one. You can also use it to spot common security issues like SQL injection or cross-site scripting (XSS).

The technique is also ideal for maintaining a clean and secure code. Since it flags issues like dead code or duplications early on, you can avoid carrying preventable issues deeper into the development process.

When To Use Dynamic Code Analysis

Dynamic code analysis shines during the testing phase, where the code is ready to be executed. Use it for performance testing and profiling after the code is compiled to see how your program behaves in a real-world environment.

It's also helpful for detecting problems that only arise during execution. For example, thread conflicts and race conditions can only occur when a program is running since they involve the interactions between different threads.

Another use case of dynamic code analysis is during debugging. Because dynamic analysis can track code execution, it can help you trace complex logic and pinpoint where errors occur. Plus, you can use it to run real-world security tests, such as penetration testing or looking for session hijacking risks.

Integrating Static and Dynamic Code Analysis Into the SDLC

Combining both static and dynamic code analysis is more effective than relying on just one method in software development. So, how do you integrate both processes in the software development life cycle (SDLC)? Here's a general guide.

Best Practices for Integrating Static Analysis

Static analysis works best when it's introduced early in software development. The sooner you catch a problem in the code, the easier (and cheaper) it is to fix. That's why you should set it up during the coding phase.

An effective way to do this is by baking static analysis into your CI/CD pipeline. Every time the code is pushed or a pull request is made, the analysis will run automatically without any extra steps required of your team.

You can make the process more useful by giving developers real-time feedback. Most static analysis tools integrate nicely with IDEs and version control systems. As a result, issues get flagged right when the code is written.
It's also key to encourage developers to fix issues as a part of their regular workflow so that the feedback loop is effective. Over time, this approach can improve overall code literacy in your team.

Best Practices for Integrating Dynamic Analysis

Since dynamic code analysis comes later in the SDLC, you need a working environment for it. The best time to run dynamic tests is during unit testing, integration testing, and system testing. It's also useful in staging environments, where the app closely mimics production. You can then see how the code performs under pressure.

Your code's working environment must be well-configured for the analysis to yield the best results. Set up the right data and simulate user interactions to mimic real-world usage. You may also use automated test scripts and scenarios to keep the process consistent and repeatable. It can help you catch memory leaks and concurrency issues that may not be noticeable otherwise.

Combining Both Approaches for Comprehensive Coverage

Now that you know the strengths of both types of code analysis, you can combine them for the best outcomes. Static analysis helps you write cleaner, safer code from the start. It enforces coding standards, highlights security risks, and keeps your codebase from turning into a tangled mess over time.

Dynamic analysis picks up where static leaves off. It catches runtime problems and gives you visibility into the issues users might actually experience when using your app.

A balanced approach means using static analysis early and often, then following up with dynamic tests as your app takes shape. With this combination, you create a layered defense, which gives your team a better shot at building high-quality and secure software.

Popular Static Code Analysis Tools

While there are many static code analysis tools you can use, the following are some options widely praised by developers.

SonarQube 

SonarQube is a well-known static code analysis tool, as it goes beyond flagging bugs. It also gives you insight into code quality and security in the same place. The tool supports multiple programming languages, which makes it a solid fit for polyglot projects or large-scale teams.

Sreedharan praises SonarQube for offering "deep insights into code quality, security vulnerabilities, and technical debt across multiple programming languages." It also integrates with CI/CD pipelines and version control systems to make it easy for developers to spot problems before they hit production.

ESLint 

For developers working on JavaScript or TypeScript projects, ESLint is a go-to option. Sreedharan calls it an "essential tool for JavaScript projects, helping enforce coding standards and detect potential issues early."

The tool is also highly customizable. You can select which rules to enforce and plug in additional rule sets. Plus, ESLint is lightweight and integrates well with most modern development environments.

Checkmarx 

If you're working in a regulated environment or handling sensitive data, Sreedharan suggests Checkmarx as a "leading solution for secure code analysis, especially useful in enterprise environments with strict compliance requirements."

Checkmarx is particularly useful in enterprise settings thanks to its advanced scanning features, compliance support, and robust reporting tools. It's also designed to work well within large DevSecOps pipelines and governance models.

Popular Dynamic Code Analysis Tools

Here are some notable tools for dynamic code analysis.

OWASP ZAP 

The Zed Attack Proxy (ZAP) from OWASP is a powerful open-source tool for security testing. It's excellent for penetration testing and catching runtime vulnerabilities in web applications. Sreedharan calls it a "powerful open-source tool for penetration testing and identifying runtime security vulnerabilities."

Its automation features further make it easy to include the tool in your CI/CD pipeline. If you don't want to spend a fortune but still keep your web applications secure, ZAP is an excellent choice.

Valgrind

Valgrind is a dynamic analysis tool best suited for performance-sensitive environments like high-performance computing or embedded systems. Sreedharan says that it's "ideal for memory leak detection and performance profiling in C/C++ applications."

So, you can use it to find bugs and performance issues that are notoriously hard to spot. Plus, Valgrind comes with a suite of tools that help you trace and debug the cause of those bugs.

Appium and Selenium

Sreedharan says that Appium and Selenium are "essential for automated functional and UI testing, helping catch runtime issues in web and mobile applications." You can use them to test your app's behavior in real-world uses on both web and mobile platforms.

Since they can simulate user actions across browsers and devices, these tools offer a smart way to catch issues that users might encounter before your app goes live.

Static and Dynamic Code Analysis Both Have A Place In The SDLC

Static and dynamic code analysis serve different purposes, but both are imperative for a well-rounded SDLC. The former catches problems before the code is run, while the latter spots runtime issues like security vulnerabilities and memory leaks.

Instead of only using one of the two, it's best to combine these techniques to find more issues before real-world users start using your app. Use the guide to implement both at the right time for a clean and error-free code.

About the Author

Hannah Hicklen Content Marketing Manager at Clutch

Hannah Hicklen is a content marketing manager who focuses on creating newsworthy content around tech services, such as software and web development, AI, and cybersecurity. With a background in SEO and editorial content, she now specializes in creating multi-channel marketing strategies that drive engagement, build brand authority, and generate high-quality leads. Hannah leverages data-driven insights and industry trends to craft compelling narratives that resonate with technical and non-technical audiences alike. 

See full profile