Testing is a crucial part of development because it identifies bugs before customers see the product. However, manual testing consumes significant time and resources. Testers must simulate every possible scenario to ensure all dependencies work correctly. Consequently, this process can take days depending on the project’s size and complexity.
Automated integration tests solve these problems by improving both efficiency and robustness. They also allow for quicker iterations and a faster time-to-market. Such automation is especially vital for complex embedded systems. These systems are growing due to the high volume of sensors and actuators used.
Similarly, IoT projects rely on automation to manage thousands of connected devices. Without it, integration becomes a bottleneck that slows down the entire project development.
Why manual integration testing slows embedded system development
Manual integration testing is very inefficient when it comes to embedded system development. It requires validating interactions between software units, software modules as well as hardware components and firmware modules. The number of different dependencies and connections make testing the entire project a daunting task.
Typical bottlenecks in manual testing
In order to verify a certain function or a module, the developer must manually set up the device. This usually includes connecting the hardware, resetting a device and flashing the firmware, which takes a significant amount of time. When it has to be done across multiple devices and different test scenarios it becomes a critical bottleneck. Such slow down causes the developers to test less frequently and thus exposing the project to more complex and deeply buried bugs.
Furthermore, embedded systems operate in real-time and many modules work on the precise input, which is impossible to reproduce consistently by a human tester. This can cause a problem to be very hard to debug or even not be detected. A bug can also slip through due to varying versions of the environment. Manual testing relies on the specific configuration of a developer’s local PC and while a program may work on that particular machine, it can fail on production because the environment was masking the issue. To mitigate these risks, industry leaders recommend following a structured framework for minimizing firmware bugs through automated testing and environment parity.
Lastly, manual test procedures often are known by heart by a senior engineer and are poorly documented. Not only that, it often relies on spreadsheets or vague Jira tickets without structured error reports, so new developers working on the project cannot perform effective root cause analysis. They have to spend hours trying to blindly reproduce issues based on sparse descriptions. This costs time and introduces unpredictability – the bugs may not be discovered until it enters production.
What are the four types of integration testing?
Integration testing is performed right after unit tests are carried out. In embedded development, “integration” specifically refers to how Hardware Abstraction Layer, Middleware/RTOS, and Application Logic communicate. Choosing the right way of testing is crucial as it dictates how early you can detect hardware-software mismatches.
Big Bang and why it’s risky in embedded
Big Bang Integration connects all modules simultaneously, and the system is tested as a complete whole. This is beneficial because it allows for testing interactions between all the existing components. However, it carries high risk for the embedded system. If it crashes or fails to boot, it is nearly impossible to determine the root cause, due to the enormous number of dependencies and hardware connections.
Incremental approaches (Top-Down, Bottom-Up, Sandwich)
Incremental techniques test components one at a time or in small groups. The Top-Down approach begins testing with the high-level Application Layer (UI, Business Logic, State Machines), and simulates low-level modules using stubs or mocks. For example, this allows us to test the UI, when the hardware is not yet available. It is useful for early prototyping, but critical low-level issues are discovered too late.
On the other hand, Bottom-Up Integration starts testing with the lowest-level components (like verifying that the microcontroller correctly communicates with its peripherals) before moving on to the complex logic on top. It is the most natural and the most robust approach for embedded systems, though you don’t see a working system until the very end of the testing process.
To address the cons of Top-Down and Bottom-Up Integrations, Sandwich approach combines both logic. You test the low-level hardware drivers and the high-level application logic in parallel. The two eventually meet at the Middleware layer. This method is the most efficient for complex projects, however it requires excellent coordination and a robust Continuous Integration (CI) pipeline to manage the parallel streams. For a full explanation, see our detailed guide: Best Practices for Embedded Software Integration Testing.
How automation changes integration testing
Automation fundamentally shifts the testing paradigm from human-dependant activity to a systematic, continuous process. In embedded development, this transformation happens in three stages, moving from simple script execution to a fully orchestrated Continuous Integration (CI) pipeline.
From ad-hoc tests to CI pipelines
The first step in automation is replacing physical human actions with software commands. In a manual setting, a developer might press a physical button and watch an LED blink, while in an automated environment, this interaction is abstracted.
Once individual interactions are automated, they must be organized – here the orchestration tool manages the setup and teardown phases, so that every test is run from a known state. To ensure a completely automated testing procedure, the final CI pipeline is developed.
Testing is triggered automatically by a commit and is entirely automated. It includes building a clean environment, flashing the devices, running integration tests, and finally generating concise logs that are sent to the team of developers.
Hardware-in-the-Loop and simulation setups
It often happens that you need hardware in order to test the software, but the hardware is not available, expensive or still in design during the early coding phases. To ensure proper testing of the software, physical devices are simulated. There are functional simulators that can create an entire virtual board without actually having the prototype.
This approach allows the implementation of continuous integration of embedded systems before working with actual hardware. When the hardware drivers (HAL) are not ready, you can use Mocks and Stubs. They can be a temporal substitute for sensors and feed the system data needed for testing. Stubs are more simple and return a specified value every time, while mocks can also verify behavior and check whether application logic works correctly.
But testing software on its own isn’t enough. We need to make sure that connected hardware will be able to handle all challenges it faces. Testing devices is usually carried out using Hardware-in-the-Loop (HIL) simulation.
This technique singles out the device, cutting off everything connected to it, allowing us to control signals reaching the device and test its functionality for a specific input. HIL makes testing dangerous or extreme scenarios (like engine overheating, or battery over-voltage) safely in a lab without risking physical damage.
Best tools for integration testing in embedded system development
Choosing the right tool for integration testing can be a difficult and confusing task. Especially in the embedded world, there isn’t a single greatest one. The best results come from a carefully curated ecosystem of tools that can handle everything from low-level register checks to high-level cloud communication. At WizzDev we make sure to select the right collection of tools for every project that meets its requirements.
CI and orchestration tools
As the orchestration engine that builds our projects, flashes the devices, and generates entire reports we use Jenkins. It is a powerful tool, allowing for creation of custom pipelines varying for every project and requiring specific plugins to manage board locking and power cycling. Jenkins handles building of the firmware and ensuring a clean compile, while also flashing target devices. It provides clean test reports that can be easily read by our developers.
Test frameworks
Once the pipeline is up and running, we need frameworks to actually execute the logic. We usually go with CppUTest and GoogleTest, which are the gold standards for unit and integration testing within the C/C++ code. They allow you to mock hardware dependencies (GoogleMock) and verify that the firmware logic works flawlessly before it even touches the hardware.
While firmware uses C or C++, Python scripts usually drive these device tests. Specifically, developers use pytest to automate external inputs. This tool sends UART commands and triggers GPIOs to simulate real-world use. It also analyzes logs to verify that the device behaves as expected.
API and communication testing
Since most modern embedded systems connect IoT devices to a backend we use Postman API to manually develop tests, which verify that the device is correctly sending MQTT or HTTP payloads to the cloud.
Hardware-level validation
In order to make sure the hardware works properly, we run a series of tests on our prototype boards. This includes simulating the voltage spikes, monitoring the motor loads, or checking sensor noise.
At WizzDev, we combine these frameworks into tailored pipelines for our clients’ embedded projects helping them save time and reduce testing costs.
Conclusion
Automated integration testing is no longer a luxury in embedded development, but a necessity for survival in a competitive market. Relying on manual testing is not enough, it creates bottlenecks that slow down development of the entire project and leaves the door open for critical failures.
By shifting from manual, human-dependent verification to automated pipelines, teams can speed up the development process and gain stability. Automation ensures constant testing and continuously catches regressions the moment they appear. It ensures that complex dependencies between modules are verified with a level of precision and frequency that human testing simply cannot match.
