Automated Software Testing for Medical Devices – A Guide for Visionary Products

In the 1900s, the average human lifespan was 32 years. Today, it has reached about seven decades. Revolutions in healthcare saved billions of lives. And the optimization of medical devices secured millions more. So, if you’re developing software for such a wondrous gadget, you ought to do anything to ensure its success.

While you’re under pressure to deliver, without exaggeration, a perfect product, you also need to think about:

• Deadlines.
• Available resources.
• And keeping the sanity of people working on this complicated project.

But then, automated medical software testing appeared to solve all your problems. Yet, automated testing (AT) is only effective when you set it up right. So, let’s talk about how to make AT worth your while.

Challenges in Medical Device Software Testing

When it comes to quality healthcare software testing services, one of the deciding factors is readiness. And we’re not talking about a positive work attitude. QA engineers need to be prepared for the inherent eccentricities of medical devices to do a better job. That means:

1. Understanding how such systems function.
2. Knowing the challenges that lay ahead.
3. And realizing that they will have to go the extra mile.

Without these three elements, the best-case scenario is rapid burnout. And the worst possible situation – compromised testing efforts. So, for now, let’s get to know “the devil” that hides in the details of healthcare software quality assurance.

System Complexity

Medical devices consist of many intricate components. Consider a medical imaging tool that integrates multiple subsystems, such as image acquisition, processing, and display. Not only would it take quite a while to develop and test it, the approval by regulatory bodies can take up to eight months.

Manually testing such systems would slow down the SDLC and increase the likelihood of human error, further prolonging the process.

Countless Integrations & Elaborate Interoperability

Healthcare software needs to integrate with various systems for refined functionality. Think hospital information systems, laboratory equipment, or other medical devices – all must cooperate flawlessly to avoid issues. Achieving interoperability, where these elements can exchange and interpret data accurately, is challenging as is.

Add to this the strain manual testing puts on QA specialists, and you get yourself a tedious task that fosters tension and deceleration.

The (Literally) Vital Role of Data Accuracy

On average, a wearable medical device makes 250,000 measurements a day. So, we can only imagine the amount of information healthcare software handles. Yet, a more important point is being able to process this data precisely. A tiny inaccuracy can have severe consequences, even loss of life.

And combing through such data volumes and management procedures by hand is like reading a book in one minute and then trying to retell it word for word.

Diversity of Mobile Devices that Operate (Some of) Medical Devices

Some medical devices operate through mobile interfaces. This introduces a new layer of complexity due to the diversity of, say, smartphones’ operating systems (OSs), screen sizes, and hardware capabilities.

• Their screen sizes range from 4.7″ to 9.7″.
• They can use any version of an OS (not everyone updates their gadgets).
• And people may rely on degraded hardware that still works for them.

Manually checking all these possibilities and accounting for each deviation is simply impractical.

User Data Privacy & Overall Security

Over half of medical devices and other interconnected hardware have security faults. And that’s with innovative development and testing solutions, as well as vast regulations. As you can see, QA for healthcare app needs to be on another level. As this software operates terabytes of sensitive patient data daily.

Regularly identifying and addressing security concerns manually would mean pouring most of your resources into this one thing.

While manual software testing remains the cornerstone of quality, alone, it’s often not enough. That’s why so many companies opt for automating over 50% of their tests. Automation can be a remarkable efficiency boost. But it can also turn into an innovation driver.

Benefits of Automated Medical Software Testing

Automation testing services offer diverse process improvements to the medical device development cycle. The benefits it provides range from the obvious ones to more subtle aspects. For example, AT reduces the labor required to run functional testing and helps devs take action to ensure the testability of the code from the design phase.

Let’s focus on the most impactful advantages of automated medical software testing.

Quick & Detailed Feedback

Automated software testing for medical devices is, in a way, a multitasking miracle. With it, you don’t just run scripts. You can also generate accurate reports at once. How exactly? As an AT tool executes tests, it records and analyzes the outcomes, automatically presenting reports based on the designed success criteria.

For example, such documents can present:

• Tests that pass, fail, and those that are pending.
• Duration of automated tests.
• List of all test cases.
• List of stories and related test cases.
• Steps executed in each test case.
• Screenshot and execution result for each step, along with error logs.

This feature allows you to minimize the feedback loop. QA engineers can quickly identify where and how an issue occurred and redirect it straight to the development team for fixing (while the remaining tests are still running).

Reduced Execution Time & Shorter Release Cycles

Here’re some evidence-based insights to put things in perspective. When a manual sanity test cycle takes eight hours, automation diminishes this number to 0.8 hours. Automated tests can be executed concurrently, reducing the time required for comprehensive validation. So, you can do more within a shorter period as:

• Automated medical software testing frees up QA specialists to focus on other tasks.
• It widens the test coverage as the software checks scenarios and functionalities simultaneously.
• And you can easily scale your testing by adding new scripts or pausing existing ones.

More Accurate Test Results

Now, you may think that this point is self-explanatory: a mature system makes no mistakes. But the edge AT offers here is more nuanced.

Imagine a device that continuously monitors the vital signs of patients in a critical care unit.

• To properly validate this software, QA engineers would need to engage in long-duration testing to ensure that it can accurately collect and analyze data over time.
• They would also have to test under various conditions, such as normal patient conditions, simulated emergencies triggering alarms, and disruptions (e.g., loss of sensor data).
• Further, the medical device software may need to be tested for scalability, ensuring it can handle a large number of patients being monitored simultaneously.
• And accurate data processing would mean spending considerable effort on generating realistic and precise data inputs (lots of them).

It’s not that a machine is better. It’s only that a human touch is best used elsewhere. For instance, functional UI testing for healthcare apps would produce optimal results with a QA engineer. Simply because a person would have an easier time identifying what is optimal for user experience.

In short, yes, AT is more accurate. It follows predefined steps consistently and eliminates the risk of human error. But it’s critical to understand what should be automated at all.

More Complex Validation Scenarios Covered

Expanding on the example above, some scenarios are simply too impractical to test manually. For a medical device that involves delicate interactions between different modules, automated tests can make a stunning difference.

Think about a test that takes six hours to execute. And you need to do it, say, twice a week. That’s 12 hours per seven days that could be used for checking usability, accessibility, or devising an improvement strategy. Hence, automation is a matter of logic.

When you see that something takes too long or makes QA specialists toil, you automate it. Because medical devices automated software testing takes up fewer resources and advances team motivation. And as tests run in the background, QA engineers work on the next script to automate. So, you cover more scenarios with less effort.

Cost-Effectiveness in the Long Run

Some studies show that manual testing efforts, including test development, maintenance, and execution, go down by 31% after the second cycle. And automation? Well, it decreased by over 99%.

You may have heard that AT implementation requires an upfront investment first. That’s because QA engineers need to set up automation frameworks, develop scripts, and maintain them. But you can also reuse automated tests, run them simultaneously, and update them instead of creating new ones from scratch.

So, when you come across another “AT proves to be cost-effective in the long run” – don’t doubt it. You put in a bit more work from the start for everything to be easier onward. This simple truth turns into an irresistible advantage later.

Best Practices for QA Automation for Medical Devices

Speaking of automated medical software testing being pricy at first – there’s a way to mitigate that. You can maximize AT’s value. How? By not missing out on QA insights and adopting a few best practices. We’ve found these to be irreplicable for result-driven automation.

Thoughtful & Careful Selection of Test Cases

Always strive for more segmentation and higher automated test execution. A good starting point is automating tests based on their impact on the system’s core functionalities. You can also go for newer approaches, like test isolation, contract testing, etc. They will solve the challenge around domain skills and decrease the strain on the teams.

Prioritizing automated tests is all about focusing on the most crucial aspects of your software. So, you reduce the risk of overlooking critical scenarios. Plus, you can also optimize testing efforts better and save resources.

Shift-Left Approach

The shift-left approach means integrating automated medical software testing earlier in the SDLC. In other words, you ought to incorporate AT from the initial stages of design and development. This lets you locate and solve potential issues faster. In turn, you minimize the likelihood of discovering critical defects in later stages, where error fixing is much more difficult.

This practice promotes a more proactive and preventive testing strategy, contributing to the overall quality and safety of your product.

Behavior-Driven Development (BDD)

BDD is a collaborative approach that aligns the development and testing teams with the business objectives. How exactly does it work? BDD involves creating executable specifications that outline the expected behavior of the system from the end-user perspective. These specifications, often written in natural language, serve as a basis for automated test scripts.

BDD fosters clearer communication between stakeholders, enhances test coverage, and ensures that the medical device functions as intended in real-world scenarios.

Risk-Based Regression Testing

Risk-based regression centers testing efforts on areas that are prone to changes or have a higher risk of failure. The technique involves prioritizing features based on their impact on the system. So, instead of checking everything now, you work in stages. First, you test the elements that are “destined” to break. And after you validate those with fewer concerns.

By concentrating testing resources on high-risk areas, development teams can ensure that critical functionalities remain robust and error-free.

Parallel Test Execution

Parallel test execution involves running multiple automated test cases concurrently. With medical device software, testing can be extra expensive and time-consuming. And parallel execution helps expedite the testing process without compromising thoroughness.

This practice leverages the capabilities of modern testing frameworks and tools to distribute tests across multiple environments. As a result, you get faster feedback on the system’s performance and prompt issue resolution.

Integration of Emerging Technologies (Cloud, AI, ML)

Modern tech isn’t simply a trend. It has swiftly become an asset. The integration of cloud computing, AI, and ML enhances the efficiency of your testing efforts. For example, cloud-based testing platforms provide scalable and on-demand resources. And AI and ML algorithms can analyze vast amounts of test data to predict potential issues.

By embracing these technologies, you ensure that AT strategies remain adaptive and robust in the quickly shifting space of healthcare software.

Continuous Monitoring & Updates

Continuous monitoring and updates are indispensable for maintaining automated testing sharp throughout the product lifecycle. This means regularly reviewing and updating test cases, scripts, and testing environments to adapt to changes in the software and compliance.

These two allow your testing process to remain aligned with the evolving nature of medical device development. And your team can address emerging risks and incorporate modifications easily.

Testing Types to Automate

You probably know well that you can’t automate everything, which is a dream for many. But for now, the human touch can’t be replaced for certain things. What you can do is focus on repetitive and tiresome testing that incapacitates QA engineers. That’s always a great option.

Otherwise, you should strive to automate the following testing types, as they prove to have the strongest impact on the QA and dev teams.

Functional Testing

During functional testing, a QA engineer checks every feature of your software to ensure it works as needed. So, each operation the product performs needs to be validated. But the biggest issue lies not in the quantity of functions. It’s about testing them continuously and accurately. That means rewriting tests with every app change and running them again and again.

QA automation for medical devices helps you avoid the “groundhog day” of testing. You can create AT scripts once and run them whenever you need. And updating them is much easier.

Integration Testing

Integration testing focuses on verifying the proper communication of software modules. If we take a look at the modern iterations of the Testing Pyramid, most of them dedicate the biggest chunk of testing to integrations. That’s why these tests are exceptionally important – no part functions in isolation. So, in a way, integration testing secures your product’s optimized internal communication.

Automated test scripts can execute integration tests much faster and with greater accuracy. You can also run them repeatedly with the same inputs for consistent and reproducible results. Ergo, you relieve your team of stressing over this impactful testing stage and advance the outcomes, too.

Compatibility Testing

Compatibility testing is seeing how your app works across different devices, browsers, and operating systems. To show you how tiresome this testing is, let’s look at some numbers:

• There’re over 600 OSs.
• The number of browsers goes beyond 200.
• And the amount of gadgets is simply unimaginable (but you only work with those your software needs).

Without automated medical software testing, checking each variation would take months. Automating compatibility testing eliminates the threat these tests feel like and enormously simplifies the process itself. You can test for any configuration quickly and with precision, providing consistent UX.

API Testing

APIs function as communication channels for your software. Briefly, API testing is about ensuring that the app can use these “dialogue links” without issues. And there’s a lot to check:

• How the system formulates the request.
• How it communicates the query.
• How it processes the received data, etc.

That’s why automating API testing is so powerful.

• You can execute the scripts whenever there’s a change in the codebase.
• Automated tests can cover a wide range of data variations and parameter combinations.
• They facilitate the creation of scripts for load and performance testing of APIs and more.

Smoke Testing

To properly explain this testing, imagine that you’ve just built a circuit. To check if it works at all, you turn it on. If it doesn’t blow up – great. You can proceed with further checks. That’s the essence of smoke testing. It’s a quick review of core features that secures a stable backbone.

Automating smoke tests allows for rapid verification of critical functionalities, enabling a quick assessment of the device’s stability. You can also integrate them into the CI/CD pipeline to verify that the deployment of new builds or releases has not introduced severe errors.

Regression Testing

Regression testing confirms that changes haven’t impacted existing operations. From this definition, you can see that regression you ought to run after almost each alteration to the software.

Automated software testing for medical devices abolishes this back and forth. You don’t have to build, check, recheck, and build again. Your team can create a “core package” testing suite and run it when needed. And with proper documentation you can swiftly modify the suite to perfectly fit the new version.

Sanity Testing

When it comes to sanity vs smoke testing, there’s always some confusion. So, let’s deal with that right away. Smoke testing checks the systems’ core functions. Sanity testing goes more in-depth for specific functions, for example, high-risk aspects.

With manual sanity testing, you’d have to lurk around unstable elements. By automating it, you can obtain accurate verdicts much faster. And proceed to amending present issues instead of continuing to look for them.

Live System Monitoring

Live system monitoring involves observing the behavior of your software in real time. It provides up-to-date information on system processes, events, and performance, enabling quick detection of anomalies.

By automating this process, you don’t spend QA manpower and resources on “guarding” the product. AT does this for you. It can process larger volumes of data faster and notify you with detailed reports once something goes wrong.

The Testing Process

If you’re considering a partnership with a QA company for automated medical software testing, you ought to know how this collaboration should go. We say “should” because not all software testing services providers work the same way. However, from our practice, we found that some aspects just can’t be dismissed.

Preparation Stage

Arguably, this is the most important phase. It defines how testing will be set up, thus determining its effectiveness.

Here, your QA partner needs to learn what they can about your product to tailor their efforts to your project specifically. Let’s review this process in steps.

1. Project analysis. The QA team studies the technical requirements and examines the software to grasp its particularities. They should also focus on regulatory standards applicable to your medical device, such as ISO 13485 or FDA’s guidelines.
2. AT strategy organization. At this point, QA experts devise the optimal way to approach automation for your needs. This includes identifying required PLs, frameworks, reporting procedures, etc.
3. Documentation generation. If you don’t have test cases or checklists, the QA team needs to create comprehensive documentation for your app. It will not only be the base for testing but also will become an asset for knowledge exchange.
4. Communication setup. Here, you need to present guidelines on how and when your teams will communicate with one another. Establishing comfortable communication channels and schedules will facilitate faster issue resolution and make sure everyone knows what to do in case of a hurdle.

Active Stage

Now, QA automation for medical devices should be nearly finalized. The teams should finish up with the testing procedures and run AT scripts. Simultaneously, they observe the entire process to monitor software behavior (in case something needs changes). QA professionals also record the test results, report errors, and gather insights on whether the testing process should be modified.

Here’s how this stage looks at QA Madness:

1. Test framework creation.
2. Tests writing on the basis of test documentation.
3. Configuration of test runs.
4. Setting up test results reporting.
5. Test execution.
6. Bug report creation.

Support Stage

A QA crew’s job isn’t done when they finish with bug reports. The support stage is potentially the most influential as it’s time for the analysis of the job done. The team collects feedback from automated testing and improves the process based on it.

They also use this data to create “How to” instructions for future engineers. This step is necessary if you want to keep your medical device’s quality standard. QA specialists also continue with test maintenance, updates, and can begin automating new features.

To Sum Up

Allow us to be frank. Automation isn’t a silver bullet. Sure, it promises you heaps of captivating benefits. But it’s only as good as the people who organize it. Even if you know all the best practices and come up with a god-tier plan for realizing them, how everything will be done is determined by the level of expertise of your team.

So, before getting excited about unleashing the power of AT, find the right professionals to do it. Then, the outcomes you envisioned for your project will not be met, but exceeded.