Static vs Dynamic Code Analysis: Finding the Best Fit for Your App

Explore the difference between Static Code Analysis and Dynamic Code Analysis to understand when and why it’s best to use a certain method or when it makes sense to combine them. 

What is Static Code Analysis?

When talking about Static Code Analysis (SCA), it should not be confused with a similar acronym, Software Composition Analysis, which identifies vulnerable dependencies and is often combined with SAST. Static Code Analysis examines an application’s source code before a program is run, analyzing code against predefined rules and coding standards without conducting taint/flow analysis. 

The crucial point regarding Static Code Analysis is that it involves pattern-based and flow-based approaches. 

The pattern-based method aims to identify code patterns that violate defined coding rules. This approach helps teams prevent resource leaks, performance issues, security breaches, API misuse, and logical errors, as pattern-based static analysis ensures that code meets regulatory compliance and internal initiatives.

Flow-based static analysis, on the other hand, focuses on identifying and analyzing the various paths through the code. Here, the key aspects of interest are control and data, specifically the order of lines in which code execution occurs and the sequences in which a variable/similar entity can be created, used, modified, and destroyed. Thus, flow-based static analysis tools help avoid several defects associated with bad code, such as:

• Memory leaks

• Buffer overwrites

• Memory access violations

• Null pointer dereferences

• Deadlocks & race conditions (possible in certain tools/languages)

Furthermore, static analysis measures and visualizes various aspects of the code, helping identify defects and providing insights into potential future maintenance issues. Such complexities are found in overly large components, long decision series, excessive nesting of loops, etc.

In general, developers can use static analysis to address code vulnerabilities early and enforce coding standards.

Benefits of Static Code Analysis

Static analysis is especially useful in the early stages of the software development process, providing organizations with a number of valuable benefits, such as:

• Vulnerability Scans. It effectively identifies SQL injection, XSS, buffer overflows, and other potential vulnerabilities, enabling developers to fix them before they are exploited. This is performed through taint analysis/rules; yet requires dynamic testing to confirm the possibility of exploitation.

• Bug Detection. Static code analysis tools help security teams catch issues and bugs before code is compiled or executed, making static analysis a practical, proactive approach.

• Compliance Requirements. By automating compliance with MISRA, CERT, CWE, and other standards, static analysis leads to improved code quality by enforcing coding guidelines. The OWASP ASVS (Application Security Verification Standard) can serve as a valuable reference for maintaining compliance.

• Shift Left Testing. By integrating static analysis into CI/CD pipelines, organizations can combine it with continuous monitoring to detect issues more effectively. IDE plugins, pre-commit hooks, and Continuous Integration (CI) are also helpful here for catching issues, preventing problematic code from being committed, and validating changes before deployment. 

All in all, Static Code Analysis tools allow for analyzing code in its entirety, implementing customized, defined rules, highlighting bugs early, and evaluating source code without executing it.

Static Code Analysis Limitations

Beyond the obvious pros of Static Code Analysis, there are some cons to this methodology that are worth keeping in mind:

• Time-Consuming Nature. Static analysis is often time-consuming because it involves reviewing and analyzing the entire codebase. This means the generated results can contain substantial amounts of data and information that require time to interpret, even if the processes are automated.

• False Positives. Another challenge with static analysis is false positives, which can occur when code is flagged as problematic/non-compliant, even though there is no actual issue. The most common cause of such triggers is the incorrect identification of problems in the code by analysis tools. This can also occur when the tool fails to detect missing security vulnerabilities.

• False Negatives. This issue refers to a situation in which the code contains vulnerabilities, but the tool doesn't report them. This typically occurs when a new vulnerability is discovered in an external component or the tool lacks knowledge of the runtime environment.

• High Cost of Tuning/Suppression. The effectiveness of static analysis depends on a proper configuration, which requires significant time and human resources. This is critical as SCA requires tailoring rules to the project's features, determining which checks to include/exclude, adapting tools, training the team to distinguish real issues from false alarms, and so on.

• Scope Limitations. Since static analysis tools can only identify issues that do not require code execution, security teams may struggle to detect performance or usability issues. Additionally, static analysis does not detect runtime errors (in configurations, integrations, and environments) and provides limited support and coverage for less popular and complex programming languages/frameworks. 

At the same time, some limitations can be minimized through automation, as well as by combining static and dynamic analysis methods. Adopting SARIF (Static Analysis Results Interchange Format) is a good idea, as it helps unify and streamline results from different tools.

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What is Dynamic Code Analysis?

Dynamic analysis involves running an application to detect and report internal failures as soon as they occur. This helps security teams precisely correlate runtime issues with test actions for incident reporting. It's also worth noting that since the source code can be run with a variety of inputs, there is no set of rules that can cover this approach.

Dynamic testing enables addressing runtime vulnerabilities that can arise from variations in the business context. This, in turn, provides valuable insights, answering questions such as what, when, and why it happened. Thus, dynamic analysis enables the detection, classification, and prioritization of all runtime-specific issues across multiple facets.

When talking about DCA, it is also worth highlighting such key aspects as Dynamic Application Security Testing (DAST), which involves black-box testing against running applications/APIs, Interactive Application Security Testing (IAST), which acts as an internal agent during runtime, and fuzzing, which provides automatic generation of input data.

Dynamic analysis can be applied at different stages of the software development lifecycle, whether pre-production or live production. This methodology is perfectly suitable for identifying and analyzing:

• Security vulnerabilities

• Performance bottlenecks

• Concurrency problems

• Runtime behavior

• System interactions

• Resource management issues

Benefits of Dynamic Code Analysis

Static and Dynamic Code Analysis are often combined. This is because dynamic analysis enables the identification and correction of errors during the development process, which cannot be done with static analysis alone. In general, dynamic analysis is functional when it comes to:

• Runtime Error Detection. When developers use dynamic analysis they can identify a range of runtime errors, such as leaks, null pointer dereferences, unhandled exceptions, etc.

• Security Vulnerabilities. With Dynamic Application Security Testing (DAST) tools, teams can simulate attacks and analyze responses, thereby preventing SQL injection, XSS, SSRF, auth/authz flaws, configuration errors, insecure CSP/HTTP headers, CORS misconfigurations, and injection vulnerabilities in real execution paths.

• False Positives. Since static analysis can flag issues that don't exist, dynamic analysis is an effective way to minimize such triggers.

• Proprietary Code. Using dynamic analysis tools on compiled binaries/applications at the interface/API level without requiring access to source code allows third-party software and closed-source components to be tested.

Dynamic Code Analysis Challenges

Dynamic testing has some challenges and limitations that should be considered and anticipated in advance:

• Code Coverage Limitations. Since dynamic analysis measures the extent of source code tested during execution, this means that some parts of the code cannot be tested by dynamic analysis tools.

• Resource-Intensive Process. This method can be resource-intensive, as dynamic testing requires significant computing power and time.

• Setup Complexity. Creating a realistic test environment can be quite complex, requiring a proper definition of the project's scope and goals.

What is the Difference Between Static and Dynamic Code Analysis?

Let's now look at Dynamic vs. Static Code Analysis.

While the static approach reviews source code to identify security flaws and coding-standard/compliance violations without actually running the program, the dynamic method examines programs while they are running to identify vulnerabilities that only appear during execution.

The key differences between Static Code Analysis vs Dynamic Code Analysis are as follows: 

When to Apply Static or Dynamic Code Analysis

Static and dynamic analysis each has its own specific purposes and application timing.

Regarding static analysis, developers typically use this method in the following situations:

• During development in the IDE

• At pre-commit stage via hooks

• When code is committed & during pull request (PR) gates with SAST/SCA checks

• Finding issues before human code review

• Detecting problems during the "Create" phase

• Finding vulnerabilities in CI/CD pipelines

Examples of static analysis in practice can include:

• Integrating SA tools into the CI/CD pipeline to prevent vulnerabilities such as SQL injection, XSS, and the use of outdated libraries. For example, if it is detected that user input is being directly inserted into an SQL query without parameterization.

• With SA tools, developers can see problems in the IDE before committing, such as when a function is too complex or a variable is declared with "var" instead of "const/let".

Dynamic analysis is typically performed:

• When the application/program is running

• During integration into the Software Development Lifecycle (SDLC)

• During pre-production testing in QA/staging environments with DAST, IAST, and fuzzing tools

• During quality assurance in CI/CD pipelines

• In canary releases and production via runtime sensors

Examples of practical uses of dynamic analysis include:

• Security testing using fuzzing, where, for example, the fuzzer sends a 10,000-character string to an API endpoint to detect a buffer overflow vulnerability (which SA analysis might miss).

• Finding bottlenecks in code, such as when an API responds slowly to requests because a particular JSON serialization function does not handle large objects well.

Combining Static and Dynamic Analysis: Best Practices

Due to the differences between Dynamic Code Analysis vs Static Code Analysis, these methods are usually combined, complementing each other.

The most effective practices for combining static and dynamic analysis include:

• Integration into the CI/CD pipeline. This approach provides multi-layered verification. Static analysis enables early detection of issues, while dynamic analysis evaluates runtime behavior later. You can also implement severity gates (for example, build fails on High/Critical findings) and use baseline & diff scanning to focus on new vulnerabilities that occurred in recent changes.

• Prioritization and confirmation. Since static analysis tools can return false positives, adding dynamic analysis helps determine whether a detected vulnerability is actually exploitable.

• Triage workflow and automation. It makes sense to establish a structured triage process with auto-created tickets for findings, as well as waiver mechanisms with expiration deadlines for accepted risks. Additionally, security teams can aggregate results from multiple tools into a unified view using the SARIF format.

• Code coverage and static metrics. Developers can combine static complexity metrics with coverage reports from dynamic tools to determine which parts of the code were actually executed.

• Compliance mapping. Following the OWASP ASVS framework ensures coverage of required security controls and facilitates compliance reporting by mapping findings.

• Feedback loop. When dynamic analysis finds a bug, it's a good practice to add it as a new rule for the static analyzer. This helps prevent similar issues in the future.

• Hot path profiling. Static analysis can identify slow operations; adding a dynamic profiler lets you test whether they are actual bottlenecks.

Summary

Static and dynamic analysis can be used separately, but they are typically combined. This ensures comprehensive code reviews, as these two methods focus on different aspects. While static analysis focuses on what might happen, dynamic analysis focuses on what is already happening. 

By following these two methodologies, developers and security teams can effectively identify defects and potential vulnerabilities, addressing them before they cause damage. Thus, Static and Dynamic Code Analysis are the best ways to ensure application security and stable performance.