When it comes to the testing phase of the software development lifecycle, there are various opinions on how to incorporate static vs. dynamic testing. For best results, teams should use these complementary methods together—and augment their efforts with ML-driven testing.
Thorough software testing is essential for delivering reliable, high-quality applications—which is fundamental to giving users a great experience and reducing churn.
Both static testing and dynamic testing offer unique benefits, and understanding how to use each effectively can greatly improve operations for quality assurance (QA) and development teams. They are used together to address different aspects of application development.
The Launchable by CloudBees platform builds on top of existing dynamic testing frameworks. Launchable leverages machine learning and historical data to offer greater flexibility, scalability, and adaptability compared to traditional analysis methods.
Let’s dive deeper into the static vs. dynamic testing world, and explain how ML-driven test analysis is augmenting the testing landscape for QA pros and developers.
What is Static Testing?
Static testing is a method of software testing that evaluates code, requirements, and documentation without actually executing the code of the application. In many cases, static testing involves reviewing and analyzing software artifacts such as source code, design documents, and specifications.
The end goal of static testing is to identify defects early in the development process—before they become costly bugs in later stages of development. Traditional static testing activities include code reviews, walkthroughs, and static code analysis.
Strengths and Weaknesses of Static Testing
The pros and cons of static testing mostly come down to the stage of the software testing lifecycle and the time required to conduct it.
Strengths of static testing include:
Early Detection of Defects: Static testing can identify issues before code execution, which helps teams catch bugs early, reducing rework and improving the overall development timeline.
Cost-Effective: Fixing errors during the early stages is far more affordable than addressing them after deploying software.
Enhances Code Quality: By identifying coding standards and architectural errors, static testing reinforces best practices that improve the maintainability and quality of code.
Costs of static testing include:
Limited to Syntax and Structure: Static testing doesn’t execute the code, meaning it can only identify surface-level issues related to syntax, structure, or documentation errors.
Dependent on Code Readability: For manual static testing, reviewers need to understand the code’s intent.
False Positives: Automated static analysis tools may generate false positives, identifying issues that may not impact functionality. This can force QA teams and developers to review each flagged issue.
Static Testing Techniques
There are several techniques to execute static testing methods, including:
Code Reviews: Developers and team members review code to ensure adherence to coding standards and best practices. Code reviews can be formal (with structured steps) or informal (a quick pair programming session).
Walkthroughs: A code walkthrough involves developers presenting code to their team—often a QA team—for feedback. This process tends to be informal and provides opportunities for feedback on the design approach.
Automated Tools: Certain automated tools can analyze source code for syntax errors, security vulnerabilities, and any deviations from coding standards.
A core traditional software testing method, static testing can be effective for cost-conscious organizations that want to catch any defects early in the dev lifecycle.
What Is Dynamic Testing?
Dynamic testing is a method of software testing that involves executing the code to check for functionality, performance, and behavior. This testing method assesses how the software operates in a runtime environment and seeks to ensure that code does not introduce regressions into the existing system.
Dynamic testing can include strategies such as unit testing, integration testing, system testing, and user acceptance testing (UAT).
Strengths and Weaknesses of Dynamic Testing
The fact that dynamic testing occurs within the coding environment brings about unique pros and cons:
Strengths of dynamic testing include:
Comprehensive Test Coverage: Dynamic testing checks how software operates in real time, providing insights into functionality, performance, and stability.
Identifies Runtime Issues: By running the code, dynamic testing reveals errors that static testing cannot, including issues related to memory management, data handling, and integration with other systems.
Improves User Experience: By testing functionality and performance, dynamic testing helps deliver a smooth, reliable user experience.
Costs of dynamic testing include:
Time-Consuming: Dynamic testing can be resource-intensive, particularly for large applications with extensive test suites, which may slow down the development pipeline.
Higher Cost of Error Resolution: Since dynamic testing typically occurs later in the development lifecycle, fixing errors can be more costly than catching them during static testing.
Requires Detailed Test Environment: For the system testing or UAT phases of the software dev lifecycle, dynamic testing requires a representative test environment that mirrors production settings, adding to the complexity.
Dynamic Testing Techniques
There are several techniques to execute the dynamic testing method, including:
Unit Testing: This method tests individual components or units of code, ensuring they work as expected in isolation. Unit tests are often automated and are the first line of defense in identifying code errors.
Integration Testing: Integration testing assesses how different modules or services work together, identifying issues that arise when components interact.
System Testing: This technique evaluates the entire system to verify that it meets specified requirements, often including tests for performance, security, and usability.
UAT: UAT is the final phase of dynamic testing, where real users test the software to confirm it meets their needs and functions as intended.
Static vs. Dynamic Testing: What Are the Differences Between Static and Dynamic Testing?
Static and dynamic testing complement each other since they focus on different aspects of an application. Static testing typically focuses on foundational code and documentation, whereas dynamic testing validates that the software runs correctly in a live environment.
Every dev and QA team should be using both methods to take advantage of their unique strengths and capabilities.
| Feature | Static Testing | Dynamic Testing |
|---|---|---|
| Timing | Early stages, before code execution | During or after development, during code execution |
| Focus | Syntax, standards, documentation | Functionality, performance, user experience |
| Tools | Code review tools, static analysis tools | Unit testing frameworks, integration testing frameworks |
| Types of Errors Found | Syntax errors, coding standard errors, documentation flaws | Runtime errors, performance issues, memory leaks |
| Cost of Fixing Errors | Low – identifies issues early | Higher – issues identified later in the process |
The Role of Machine Learning In Software Testing
Artificial intelligence helps to optimize the time and resources required for traditional dynamic testing methods. QA and development teams rely on machine learning tools to predict which tests are likely to catch errors based on historical test data.
ML models can improve testing performance and efficiency by identifying the most relevant tests for specific code changes, reducing redundant test execution, and accelerating feedback cycles.
Machine learning models adapt continuously, learning from past test outcomes to refine their recommendations. This dynamic approach enables more precise and flexible test selection, effectively reducing the time and resources required for testing. As software changes, ML-driven test impact analysis offers a scalable way to ensure that code is thoroughly vetted without overwhelming the testing pipeline.
Launchable by CloudBees: Using Machine Learning For Test Impact Analysis
Launchable by CloudBees’ ML-driven testing methodology helps teams lean on historical test data, build dynamic associations between tests and code changes, and employ Predictive Test Selection to identify the most relevant tests.
Here’s how our platform augments traditional testing methods across four key focus areas for developers and QA teams:
| Area | Benefits of Launchable by CloudBees’ ML-Driven Testing |
|---|---|
| Language and Framework Flexibility |
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| Risk Management and Confidence |
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| Test Selection and Prioritization |
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| Adaptability and Continuous Improvement |
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Companies such as GoCardless, which reduced testing time by 50%, rely on Launchable’s data-driven insights.
“CI times were a growing bottleneck for developer productivity for us at GoCardless; as our key mono repo expanded and more tests were added, CI times increased and consequently productivity slowed. Launchable offered a quick and easy-to-integrate solution that surpassed our expectations,” said Sam Robson, CI lead at GoCardless.
The static vs. dynamic testing conversation should never be a “one or the other” scenario. Launchable by CloudBees complements traditional methods to provide all the capabilities that modern development and QA teams need, including Predictive Test Selection, risk-based prioritization, continuous adaptation, and cross-language compatibility.
Chat with an expert to learn how our platform’s ML-driven approach to test impact analysis can provide a boost for your organization.