Software testing strategies help engineering teams decide what to test, which problems to focus on, and how to check that software works as expected. Examples include testing PLC programs, checking embedded firmware, testing robotics software, and verifying engineering calculations.
The right strategy depends on what could go wrong, how serious the problem could be, and how the software will be used. Most projects need a combination of approaches.
By Jordan Lee | Last updated: October 2026
Software Testing Strategies at a Glance
| Strategy | What it does |
|---|---|
| Risk-based testing | Gives more attention to features where failure could have serious consequences. |
| Model-based testing | Uses a model of expected behavior to create test cases. |
| Methodical testing | Uses checklists and defined test conditions to check important cases systematically. |
| Standards-based testing | Follows the testing rules and records required by a project or applicable standard. |
| Regression testing | Checks that software changes have not broken existing features. |
| Exploratory testing | Investigates unexpected behavior that predefined tests may miss. |
| Test automation | Uses tools to run suitable tests repeatedly with less manual work. |
These approaches can work together. For example, an engineer might prioritize critical PLC functions, test them in a simulator, and automate repeatable checks after each program change.
1. Risk-Based Testing
Risk-based testing focuses on the parts of a system where failure could have the greatest impact. It is useful when time is limited or some functions are more important than others.
How to use it:
- Identify critical functions and possible failures.
- Consider how likely each failure is and what its consequences would be.
- Test the most important risks first.
- Review priorities when the software or its requirements change.
Engineering example: When testing motor-control firmware, engineers may give extra attention to sensor failures, communication loss, operating limits, and protective responses.
Risk-based testing helps teams use their time wisely. It does not mean ignoring required safety checks or leaving important functions untested.
2. Model-Based Testing
Model-based testing uses a model of how a system should behave to create test cases. The model might show different states, control sequences, or responses to inputs.
How to use it: Define the expected behavior, identify important states and conditions, then create tests to check the results.
Engineering example: An engineer developing a temperature controller can test how the model responds to changes in temperature, operating limits, and simulated sensor faults. Selected tests can then help compare model results with the behavior of generated control code.
This approach is useful for control systems, robotics, and other software with complex behavior. The model must still represent the requirements correctly.
3. Methodical Testing
Methodical testing uses checklists and defined test conditions to reduce the chance of missing important cases.
Instead of testing only normal operation, engineers also check invalid inputs, unusual conditions, and expected responses to faults.
Engineering example: A PLC test checklist might cover startup, restart, incorrect sensor readings, lost communication, alarms, and recovery after a fault.
Each check should have an expected result. Update the checklist when new problems or software changes reveal missing tests.
4. Standards-Based Testing
Some engineering projects must follow specific standards, customer requirements, or documented testing procedures. Standards-based testing helps teams check the required conditions and keep evidence of the results.
How to use it: Identify the requirements that apply, connect them to test cases, and record the software version, test conditions, results, and unresolved problems.
Engineering example: A controller project may require records linking each important requirement to its test and result. Engineers keep these records so the work can be reviewed.
Following a standard or procedure alone does not prove that software is safe or correct. The required checks must be carried out properly, and their results must meet the applicable criteria.
5. Regression Testing
Regression testing checks whether a software change has broken something that previously worked.
It is particularly useful when a project receives frequent updates or when different parts of the software depend on one another.
How to use it: Keep tests for important functions, select the tests affected by each change, and run them again. Automate stable tests when repeated execution makes automation worthwhile.
Engineering example: An engineer changes a Python script that processes finite element analysis results. Regression tests compare the updated calculations with known reference results and check that units and output dimensions remain correct.
For numerical calculations, define acceptable tolerances rather than assuming every result must match exactly.
6. Exploratory Testing
Exploratory testing involves probing software through different scenarios and adjusting the checks based on what the engineer discovers, rather than relying entirely on predefined test cases. It is useful when testing new features or investigating problems that are difficult to predict.
How to use it: Start with a clear goal, try realistic and unusual conditions, investigate unexpected results, and record the steps needed to reproduce any defects.
Engineering example: During a robotics simulation, a developer notices that navigation changes when sensor data arrives late. The developer investigates the timing and interactions between components, then creates a repeatable test for the problem.
Exploratory testing works best alongside planned testing. Important discoveries should be documented so the team can check for the same problem in future versions.
7. Test Automation
Test automation uses tools and scripts to run tests repeatedly. It can save time when the same checks need to run after code changes, model updates, or new software builds.
How to use it:
- Start with tests that run often and have clear expected results.
- Choose tools that fit the programming language or engineering environment.
- Run the tests as part of the development workflow where practical.
- Review failures and maintain the test suite as the software changes.
Engineering example: Engineers can create automated checks for Python calculations and selected PLC simulations, allowing them to verify these functions again whenever the software is updated.
Automation does not replace all manual testing. Engineers still need to investigate unexpected results and judge whether the tests cover the risks that matter.
Software-in-the-Loop, Processor-in-the-Loop, and Hardware-in-the-Loop Testing
Engineering software may eventually run on a target processor or control physical equipment. Testing it only on a development computer may miss problems related to hardware, timing, or interfaces.
Three common testing setups are SIL, PIL, and HIL.
| Method | How it works | Main use |
|---|---|---|
| SIL — Software-in-the-Loop | Software or generated code runs on a development computer in a simulation setup. | Checks code behavior against expected results before target deployment. |
| PIL — Processor-in-the-Loop | Code built for the target processor runs on the target processing hardware. | Checks the compiled code on the target processor. |
| HIL — Hardware-in-the-Loop | A physical controller interacts with a real-time simulation of the system it controls. | Checks controller behavior, timing, and interfaces under simulated conditions. |
Example: Testing a motor controller
An engineering team might first test its control model, then run software-in-the-loop tests, followed by processor-in-the-loop tests where needed. The team can then use hardware-in-the-loop testing to examine the physical controller’s response to simulated operating conditions.
After simulation-based testing, the project may still need physical tests to check behavior that the simulation cannot represent fully.
These methods are supported by tools such as MATLAB and Simulink. The exact setup depends on the controller, target hardware, and available test equipment.
Tools That Support Software Testing Strategies
Different tools support different parts of the testing process. Some run tests, some simulate hardware, and others help teams organize test results.
| Tool | What it is useful for |
|---|---|
| MATLAB and Simulink Test | Testing models and generated code, including supported SIL, PIL, and HIL workflows. |
| Siemens SIMATIC S7-PLCSIM Advanced | Simulating supported PLC systems to test control programs. |
Python pytest | Automating tests for Python functions and analysis scripts. |
ROS 2 launch_testing | Testing interactions between running robotics software components. |
| Jenkins Pipeline | Running repeatable build and test steps after software changes. |
| TestRail | Organizing test cases, test runs, results, and reports. |
These tools are examples, not a ranking. Choose according to your programming language, hardware, simulation environment, and testing requirements. Check current compatibility and licensing before selecting a product.
For a wider view of how testing fits with design and analysis tools, see our engineering software stack guide.
For robotics examples, read our guides to Robot programming and the Robot Operating system(ROS). Our CAE Software guide also covers simulation tools used in FEA and CFD workflows.
How to Build a Software Testing Strategy
A practical strategy should make clear what needs testing, how the work will be carried out, and what counts as an acceptable result.
1. Understand the software. Identify its main functions, requirements, interfaces, and operating conditions.
2. Identify the main risks. Decide which failures could cause the greatest problems and which functions deserve the most attention.
3. Choose the testing approaches. Combine suitable methods, such as risk-based testing, model-based testing, and regression testing.
4. Select tools and test environments. Decide whether testing should use a development computer, a simulator, target hardware, or a controlled physical setup.
5. Define expected results. Set clear pass criteria, including acceptable numerical tolerances where needed.
6. Record and review the results. Keep useful test records and update the strategy when changes or new defects reveal gaps.
The amount of planning should fit the project. A small calculation script may need only a focused test suite, while a controller connected to physical equipment may require more extensive testing and records.
Common Testing Mistakes
Testing only normal conditions. Invalid inputs, boundary cases, communication problems, and fault responses may reveal defects that ordinary tests miss.
Assuming a successful run proves correctness. A script or solver can finish without errors while using incorrect inputs or assumptions. Check its results against requirements or suitable reference cases.
Relying only on simulation. Simulations are useful, but hardware behavior and physical conditions may require additional tests.
Automating tests without maintaining them. Review automated tests when software or requirements change so they continue to check the right behavior.
Frequently Asked Questions
Which software testing strategy is best?
There is no single strategy for every project. Risk-based testing helps prioritize important failures, model-based testing checks expected system behavior, and regression testing protects existing functions. The right combination depends on the software and its risks.
Does simulation replace physical testing?
No. Simulation can find many problems before hardware is used, but it cannot represent every physical condition. Additional testing may be needed to check hardware, timing, interfaces, and real-world behavior.
How can engineers test Python or MATLAB analysis scripts?
They can check known inputs against expected results, test boundary conditions, verify units and dimensions, and define acceptable numerical tolerances. Python’s pytest and MATLAB’s unit testing framework are examples of tools that support these checks.
Conclusion
Software testing strategies help engineering teams focus on important risks, check expected behavior, and catch problems after changes. Risk-based, model-based, methodical, regression, and exploratory testing each serve a different purpose.
Combining the right approaches with suitable tools and simulation environments gives engineers better evidence that their software behaves as expected. The goal is not to run the largest number of tests, but to run useful tests that address the risks that matter.