The Role of QA in IoT Application Development

According to McKinsey, the potential economic value that IoT could unlock is estimated to range between $5.5 trillion to $12.6 trillion by 2030. However, capturing that value proves challenging, as many enterprises struggle to scale their initiatives successfully.

Securing connectivity, retrofitting legacy devices, and integrating new devices within existing systems can significantly increase a solution’s development time, cost, and testing complexity. As IoT systems become more autonomous, quality assurance (QA) becomes vital to ensure device and system reliability, safety, and security as the sector grows. And with advances in IoT technologies, more companies are seeing the benefits of QA as a service.
In this article, you’ll learn about approaches to testing IoT applications, potential challenges, and the ways to overcome them.

Peculiarities of QA in IoT Application Development

IoT applications are complex systems comprising functional hardware and software elements, among which are:

Sensors and devices: the “things” that generate data for an IoT system
Communication protocols: a set of rules and standards that enable the wireless exchange of information between devices
Data processing, storage, and analysis tools: management, analysis, and interpretation of data generated by the connected devices
Software applications: programs that let users interact with an IoT system

Testing IoT applications requires a good understanding of all these components and how they communicate. Testers must ensure that each part of the IoT network functions properly and the entire system delivers as expected.

Given the specifics and the complex architecture of IoT applications, QA engineers use specialized testing approaches and methodologies to examine IoT systems.

QA practices for IoT testing

The right approach to testing differs depending on the software development methodology the team is sticking to. Each methodology outlines the specifics of the team's actions to organize the QA process.

Here are some peculiarities of IoT testing approaches in the context of main software development methodologies:

Agile testing focuses on iterative and incremental development. It involves breaking down testing elements into “bite-size” blocks and narrowing the focus to a specific user story or technical requirement. Thanks to this, testing happens continuously as new features appear, making it faster to identify and fix issues as they arise.
DevOps testing, as an evolution of agile practices, simplifies complex deployments of connected technologies and reduces operating costs. Automation plays a key role in DevOps, streamlining security and performance testing.
Waterfall testing follows traditional software development methodology and involves sequential developmental and testing stages. Though it’s often criticized for requiring teams to wait until product or phase completion, waterfall testing works perfectly well for small IoT applications with well-defined requirements and specifications (e.g. proof-of-concept or MVPs).
Exploratory testing involves examining an IoT application without a defined test plan so that the testers can identify issues that may be missed using other testing approaches. It’s especially useful in IoT app development because it identifies system interoperability, data privacy, and security issues.
Risk-based testing focuses on the parts of the application most vulnerable to failure. It identifies and addresses potential issues early in the development process and reduces the time and cost associated with testing. However, it has limited coverage, so it is more efficient when complemented with other testing approaches.

The effectiveness of any QA practice relies heavily on its implementation and the types of testing you apply. Let’s find out what test types work best for testing IoT solutions.

Types of Testing for IoT

Testing IoT applications revolves around devices, networks, operating systems, platforms, and standards. To examine all of these components and guarantee their stability, you must combine several testing types.

The following testing types can help you ensure high-quality coverage of your IoT solution.

Functional testing

As the name suggests, this type of testing checks the system’s functionalities to ensure it works as intended. Here are the types of functional testing:

Smoke testing is a preliminary testing phase to check the most critical functionalities of the system, like connectivity or data transmission.
Unit testing is performed at the module or component level to ensure individual pieces of code function correctly.
Integration testing evaluates interactions between different components of an IoT system to ensure they work together as intended.
System (end-to-end) testing validates the system's overall functionality by testing the entire system, including all hardware and software components.
Regression testing ensures that new features and changes introduced to the IoT application later in development don’t break previously existing functionality.
Boundary testing verifies that the IoT system behaves correctly when it operates at or near the boundaries of its specified operating range (e.g., testing a sensor-specific configuration range like the camera’s FPS).

Given the complexity and variety of IoT system components and scenarios, functional testing can be daunting and require an experienced software services company.

Performance testing

This type of testing simulates real-world situations to ensure an application can handle different loads and perform efficiently under various scenarios. It identifies potential bottlenecks and weaknesses in the system that may impact an application’s performance.

IoT systems rely on the interaction of hardware and software to function correctly, which makes it difficult to isolate and test specific components. That’s why performance testing should cover the device, network and gateway level (i.e., communication protocols), system level (i.e., processing, analytics, database), and application level.

Performance testing includes:

Volume testing - evaluates the performance of an IoT system when dealing with large volumes of data.
Load testing - ensures the system can handle many users or devices accessing the system simultaneously.
Stress testing - measures how the IoT system performs under load spikes and what issues may arise.
Capacity testing - determines the maximum capacity an IoT system can handle without issues.
Scalability testing - measures the system’s ability to scale up and down as needed.

Automation can streamline the testing process and drive better outcomes. Running scripts can cut a tester’s work time from 20 days to one hour. With these time savings, they can conduct performance testing as often as the team pushes new features to production.

Data validation testing

IoT devices collect enormous amounts of data, so validation testing ensures that the device’s incoming data is valid and is processed and stored correctly. QA engineers verify the data matches the expected range of values and formats. They create test cases prior to testing to serve as the source of truth. This helps to prove the accuracy and consistency of the data IoT devices generate.

Model accuracy testing

This type of IoT testing aims to evaluate the accuracy of AI and ML model predictions based on data collected by IoT devices. To assess the performance of AI services, testers use a set of predefined test cases to compare predictions made by the model with actual data.

Compatibility testing

As the name suggests, compatibility testing checks that an application works seamlessly on different devices, platforms, and operating systems.

Given the complexity and diversity of hardware devices, software platforms, and communication protocols, testing IoT applications for quality may pose challenges for a QA team. Let’s learn more about the challenges and how to overcome them.

IoT Testing Challenges

Here are the most common challenges associated with IoT application testing.

Need for continuous education

The IoT landscape is constantly evolving, with new technologies, protocols, and standards emerging monthly. For instance, the commercialization of 5G increased connectivity and pushed cellular IoT module deployment (especially in the automotive sector). A lack of expertise or knowledge in this sphere may result in insufficient testing and threaten the quality of an IoT system.

Remote hardware and software testing

As IoT applications are deployed in real-world settings, it’s challenging to create and simulate the various conditions the device may encounter (e.g., weather conditions). Testing the functionality and performance of IoT devices is especially challenging when they’re placed in remote or dangerous locations.

Lack of standardization

The lack of widely accepted standards for IoT hardware and software makes it difficult to develop comprehensive testing strategies. In addition to the 12 most commonly used communication protocols, there’s an extensive variety of others used in current IoT solutions, many of which are proprietary. That means testers must study the peculiarities of each protocol used in a particular IoT network.

Security

According to Statista, in June 2022, there were 13 million IoT attacks. Security concerns grow with an increasing number of connected devices, and weakly secured devices can compromise the entire network.

Inability to update hardware

Certain IoT devices may not support future updates due to low energy or deprecation. This has two major consequences. First, you must use newer versions when the device firmware can’t be updated. This increases the number of devices to test, and of course, costs. Second, devices that are no longer supported can have security defects that a potential attacker can exploit.

Unreliable QA strategy

The complexity of IoT systems makes it challenging to develop a comprehensive testing strategy that covers all aspects of a system. Consequently, undetected bugs, errors, and security vulnerabilities leave the system open to potential attacks.

The lack of a reliable testing strategy also increases product cost: issues identified late in the development cycle require more time and resources. If the team fails to fix them, this can lead to poor system performance, security breaches, and damage to a brand’s reputation.

QA and development team communication issues

A lack of effective communication between teams leaves critical issues undiscovered until later stages of development, leading to misunderstandings, misaligned goals, and delayed product release. Poor communication sometimes occurs when teams work in different time zones, or integration issues must be addressed during testing.

Hopefully, the right strategy and tools can help you tackle most of these challenges. Read on to learn how.

How to Address IoT QA Testing Challenges

Here are several strategies to handle IoT-related QA challenges and ensure thorough application testing.

Automation. Adopting automation can be particularly helpful in regression testing, where engineers perform the same tests repeatedly. By automating tests, developers can ensure an application functions correctly after every code update in a fraction of the time manual testing would take.
Cloud-based testing platforms. Cloud-based platforms can simulate real-world scenarios and give developers a better understanding of how an application performs under different conditions.
Virtualized test environments. Simulating an IoT system's hardware and software components makes it easier to test an application because you don’t need access to physical devices. Also, virtualized test environments reduce the cost of IoT testing.
Open-source testing frameworks. KAA IoT, EMQX, and other open-source frameworks provide developers with access to various testing tools and resources. They offer a basic infrastructure to augment information collection, storage, and distribution, making it easier to test an application. In addition, open-source frameworks can be customized to meet the app’s specific needs.
Integration with Google products. Tools like Cloud IoT Core and Firebase Test Lab can help your QA team test an app more effectively. For instance, Cloud IoT Core easily integrates with other Google services, including Cloud Pub/Sub, Cloud Functions, and BigQuery, so you can test IoT devices' connectivity and data collection. With Firebase, you can automate tests and, when integrated with Firebase Crashlytics, get insights into IoT app crashes and other issues.

While various approaches and tools mitigate most IoT testing challenges, a collaboration between QA and development teams can greatly enhance their effectiveness.

Collaboration between QA and Development Teams

The major challenge associated with IoT solutions is getting software and hardware to interact with each other without glitches or delays. This requires an experienced team and well-oiled collaboration between QA engineers and developers. But if QA engineers and developers don’t have a thorough knowledge of each other's role in the development process, the teams may encounter problems:

Inconsistent testing and development approaches
Delays in identifying and resolving issues
Gaps in testing coverage

Creating a culture of collaboration where teams share their expertise and knowledge speeds up the testing process and improves outcomes. By working together, teams can leverage their expertise and complementary skills to identify and address potential issues promptly and efficiently.

You can improve your team collaboration by

Having regular meetings where both teams actively participate
Encouraging joint planning
Using collaborative tools

Techstack has vast experience in providing QA as a service for IoT application development. The next section describes how we approached QA while working on one solution.

IoT Testing of a Video-Based Quality Control System (VBQCS)

For a VBQCS system, our QA team collaborated closely with the partner company's engineers. We started with smoke testing to detect any major issues in the software. Smoke testing involved running simple tests on the system to check its basic functionality.

To ensure the system’s accuracy, reliability, and efficiency, our team also conducted the following types of testing:

Functional testing. Our team tested the system's ability to identify and categorize different defects in the manufacturing process and evaluate the system's overall accuracy.
Performance testing. We tested the system's ability to process video data quickly and accurately and evaluated the system’s potential to handle multiple streams of video data simultaneously.
Regression testing. Our QA engineers tested the system before production to ensure new features and updates didn’t break existing functionality.

Combining different testing methods helped ensure that the VBQCS system was reliable, stable, and ready for release.

Conclusion

The primary factor that makes IoT application testing different from traditional testing is the inherent connections between external devices. IoT systems' complex, multilayered nature makes it difficult to isolate and test each component. IoT applications require a QA team that can apply specific testing approaches and strategies to ensure a system’s reliability and security.

Techstack has wide-ranging expertise in providing various Internet of Things services—from design to development and testing. Our team is well-versed in modern testing methodologies and frameworks to ensure no bugs will pass through to production. We can help you build and execute a comprehensive testing strategy and collaborate closely with your development team.