Comparison of two dominant approaches for Program Analysis
We recently published an article: Why Static analysis will not resolve your performance problem where we discuss Static Analysis and compare it with Dynamic Analysis from a performance perspective.
This time, we will focus on Static Analysis vs Dynamic Analysis approaches for Program Analysis and compare them in detail in all aspects, not only performance.
Table of Contents
Why does Program Analysis matter?
Program analysis is an essential practice in software development. It aims to improve code quality, security, and performance. Different approaches to program analysis help us identify bugs, vulnerabilities, and inefficiencies in their code. Program analysis is crucial for several reasons:
- Quality Assurance
- Security
- Performance
- Cost Savings
- Compliance
- Collaboration
Program Analysis vs Code Analysis
Program analysis and code analysis are related but distinct concepts in software engineering. While they overlap in certain areas, they serve different purposes and involve different techniques.
Program analysis encompasses a broader range of techniques than Code Analysis to understand and improve program behavior. These can include both static analysis and dynamic analysis.
Different Approaches for Program Analysis
Several approaches to program analysis exist, each with strengths and weaknesses. We may use a combination of these techniques to analyze their code effectively.
In general, program analysis is split into two main categories:
- Approaches that analyze the program without executing it (e.g., static analysis)
- Approaches that analyze the program during runtime (e.g., dynamic analysis)
What is Static Analysis?
Now that we know about program analysis and aspects that can improve software development let’s explore these two approaches in depth.
Static analysis is a method of examining computer software without executing the program.
This analysis is typically performed on the source, intermediate, or binary codes.
Aspects Covered by Static Analysis
Static analysis can cover these aspects:
- Early Issue Detection: Static analysis can detect issues early in the development process, even before the code is run. This early detection can save time and resources by preventing bugs from propagating to later stages.
- Improved Code Quality: Static analysis helps maintain a high level of code quality by enforcing coding standards and best practices. This includes ensuring consistent code formatting, proper naming conventions, and adherence to design patterns, which collectively make the codebase more readable, maintainable, and scalable.
- Enhanced Security: Static analysis tools can identify security vulnerabilities, such as SQL injection and buffer overflows, by analyzing the code for known patterns and practices. Static analysis tools are designed to detect such vulnerabilities, helping to protect the application from potential attacks and ensuring compliance with security standards.
- Comprehensive Coverage: Since static analysis examines all code paths, it can identify potential issues that might not be executed during runtime testing.
- Automation: Integrating static analysis tools into the development workflow is low-cost, and it will provide continuous feedback and ensure that code quality remains high.
Along with all these capabilities, Static Analysis tools can not cover some aspects and have some cons:
- Runtime Performance: Static analysis tools cannot measure or evaluate an application’s runtime performance. Performance bottlenecks, response times, and resource utilization metrics can only be gathered through runtime analysis.
- False Positives: Static analysis can generate false positives, flagging non-issues as potential problems. This can lead to wasted time and effort in reviewing and addressing these warnings.
- Limited Context Awareness: Static analysis lacks the context of runtime data, making it difficult to identify issues that depend on specific runtime conditions.
What is Dynamic Analysis?
Dynamic analysis is the examination of a software program as it runs, providing insights into its behavior, performance, and interaction with the environment.
This approach is used to identify runtime issues, such as memory leaks, performance bottlenecks, and concurrency problems
that cannot be detected by static analysis alone.
Aspects Covered by Dynamic Analysis
As we know, the main difference between these two approaches is that dynamic analysis is based on executing the application, bringing many benefits to this approach. We will start with the main benefits:
- Detecting Runtime Issues: Issues such as memory leaks, performance bottlenecks, and concurrency problems can only be detected during execution.
- Runtime Behavior: Analyzing dynamic behavior helps in understanding how code interacts with various system components and external services.
- Accurate Performance Metrics: Developers can gather precise performance metrics, such as response times and resource utilization, which can be very helpful for optimizing applications.
- Real-Time Insights: Provides immediate feedback on how the software behaves during execution, allowing for the identification of issues that occur only at runtime.
- Security Vulnerability Detection: Helps identify security weaknesses by simulating real-world attacks and monitoring the program’s response.
Is Dynamic Analysis always better than Static Analysis?
Static Analysis vs Dynamic Analysis: Dynamic analysis is not always better than static analysis. Each has its own strengths and weaknesses, making them suitable for different purposes within the software development lifecycle. These are some examples of the downsides of dynamic analysis, which static analysis might perform better:
- Limited Coverage: Runtime analysis only covers the code paths that are executed during the test runs. Unexecuted paths remain unanalyzed. In comparison to the comprehensive code coverage provided by static analysis by examining all code paths, this is one aspect where static analysis performs better.
- Late Detection: Issues identified during runtime analysis are typically found later in the development cycle, potentially increasing the cost and effort required to fix them. As we saw before, Static Analysis can detect many issues earlier and perform better in this case.
- Cost: To perform a good analysis, the application, dynamic analysis tools, and test runners need dedicated infrastructure and comprehensive test scenarios.
- Environment Dependency: The results of runtime analysis can be highly dependent on the specific environment and input data used during testing.
How Digma acts as a complete Dynamic Analysis stack
Digma provides a complete Dynamic Analysis stack for us and covers all we need for a comprehensive Dynamic Analysis, from executing our application to collecting observability data and then analyzing our application’s dynamic behavior to find issues, such as bottlenecks, scaling problems, performance and db query issues and more.
Digma vs. Traditional Dynamic Analysis Tools
As we discussed, late issue detection is one of the major downsides of Dynamic Analysis tools. Digma continuously observes our application’s behavior in development (Using the Digma IntelliJ plugin), testing, and production to find issues in our code so we can identify issues before it is too late.
Another advantage of Digma is that it integrates with our IDE and CI pipeline. When an issue is detected, it automatically highlights and prioritizes critical problems and their locations in the code. Unlike traditional dynamic analysis tools, we don’t need to spend long hours manually checking for issues, jumping between different tools and dashboards, and tracking down where the problem is in your code. By using Digma, we can immediately see issues highlighted in your code, down to the method, class, or even query level.
Lastly, as I mentioned, Digma integrates with IDEs, so it is easy to use. Having a comprehensive dynamic Analysis stack costs almost nothing, and you can run it on developer machines.
Final Thought: Static Analysis vs Dynamic Analysis
We recently explored “Top 7 Techniques to Optimize Caching in Spring Boot”. In this article, we compared Static Analysis vs Dynamic Analysis, two dominant approaches for Program Analysis. Both static and runtime analysis are essential techniques in the software development lifecycle. Static analysis offers the advantage of early detection and comprehensive coverage without execution but fails to address runtime-specific issues. On the other hand, runtime analysis provides insights into real-world behavior and dynamic interactions but is limited by relying on execution and specific test scenarios. By combining both approaches, developers can better understand their application’s robustness, performance, and security.
Download Digma: https://plugins.jetbrains.com/plugin/19470-digma-continuous-feedback
