Quality Control in Embedded Systems. Methods for Dynamic and Static Code Analysis in C# and Python

In embedded systems development, where reliability and security are paramount, code quality control is a critical aspect. Using programming languages like C# and Python requires careful approaches to code analysis since each language has its own characteristics and potential risks. This article discusses methods for static and dynamic code analysis, as well as modern tools like GitHub Copilot, which can be applied to improve quality and security in embedded projects.

1. Why is Quality Control Important for Embedded Systems in C# and Python?

Embedded systems often operate in resource-constrained environments and must provide high reliability in real-time. In projects involving building automation, industrial controllers, and smart devices, any coding errors can lead to critical failures or even pose security risks. Therefore, implementing a strict quality control process that combines both static and dynamic analysis methods helps detect errors early in the development process and mitigate risks.

2. Static Code Analysis: Detecting Errors at the Source Code Level

Static code analysis allows developers to detect errors before the software execution phase by analyzing the source code for compliance with rules and standards. For C# and Python, several tools are widely used in the industry:

For C#:

• SonarQube: SonarQube remains one of the most popular tools for static code analysis in 2024. It supports C# and helps detect vulnerabilities, errors, performance issues, and code style violations. It is still heavily used in CI/CD processes to automate code analysis.
• Rider by JetBrains: Instead of ReSharper, which is still relevant, more developers are transitioning to Rider, an integrated development environment (IDE) by JetBrains that includes analysis and refactoring capabilities similar to ReSharper but is more optimized and has built-in support for C#.
• Roslyn Analyzers: The built-in analyzers of the Roslyn compiler (part of the .NET Compiler Platform) allow the detection of issues during compilation. The support for analyzers continues in Visual Studio 2022 and later, making this tool relevant for style and security analysis in C#.

For Python:

• Pylint: Continues to be one of the most popular tools for analyzing Python code in 2024. It helps detect syntax errors, code style violations, and potential bugs, and supports compliance with coding standards like PEP 8.
• Bandit: Remains a relevant tool for security analysis of Python code, especially in projects where security is critical. It is actively updated and used to find vulnerabilities such as code injections and insecure library usage.

3. Dynamic Code Analysis: Testing During Runtime

Dynamic code analysis helps detect errors that cannot be identified during the compilation phase by running the program in a test environment. Various approaches and tools are available for C# and Python:

For C#:

• dotMemory and dotTrace: These tools from JetBrains for memory usage and performance analysis remain relevant in 2024. They are used to detect memory leaks, performance issues, and optimize application performance. These tools support the latest versions of .NET.
• xUnit and NUnit: Both frameworks for unit testing are actively used and supported in 2024. They integrate into CI/CD pipelines and support test automation, helping to ensure code quality.

For Python:

• Pytest: Remains one of the best testing tools for Python and continues to evolve. Pytest supports automatic test execution, performance testing, and code coverage.
• Fuzzing with Atheris: The fuzz testing method, still relevant in 2024, helps identify rare errors in Python code through the generation of random data. Atheris is actively used for security and reliability testing.

4. Integration of Modern Tools Like GitHub Copilot

GitHub Copilot, an AI-based tool, continues to be used by developers to speed up the process of writing and analyzing code. Copilot automatically suggests code snippets and fixes based on context and best practices, helping minimize the likelihood of errors and speeding up the development process. In 2024, Copilot is actively used for embedded development in C# and Python, helping to solve typical tasks and increase productivity.

5. Automating Quality Control Processes in CI/CD

Automating code analysis and testing remains a crucial aspect of quality assurance in 2024. Integrating tools into CI/CD pipelines allows for automatic code checks with every change, reducing the chance of errors entering the main branch.

• Integrating Static Analysis in CI/CD: Automatically running tools like SonarQube, Rider, Pylint, and Bandit allows for identifying and fixing errors early, even before changes are merged into the main branch.
• Automated Dynamic Testing: Using dotMemory, dotTrace, xUnit, and Pytest in the CI/CD pipeline allows for testing not only functionality but also evaluating the stability and security of the system.

6. Results and Conclusions

Applying static and dynamic analysis methods in real projects allows for:

• Reducing code errors by 40% at later stages of development, significantly lowering the cost of fixing them.
• Improving system performance and reliability by timely identifying memory issues and optimizing code.
• Enhancing application security, which is especially important for systems operating in high-risk environments.

Conclusion

Code quality control is a key aspect of development in C# and Python for embedded systems. Using up-to-date tools such as static and dynamic analysis, as well as modern AI-driven solutions like GitHub Copilot, and integrating them into CI/CD processes, not only increases reliability and security but also accelerates development, making it more predictable and manageable.