One of the biggest challenges facing the IoT industry is the need to recall faulty devices, which can cost companies billions of dollars in lost revenue and reputation. To avoid this, it’s crucial to conduct thorough and extensive IoT testing to ensure that devices are ready to operate in the market. But testing IoT devices is easier said than done. Due to the complex nature of IoT systems and their interactions with various devices, networks, and cloud services, testing IoT devices poses unique challenges.

In this article, we’ll explore the top 5 IoT testing challenges and their corresponding solutions, to help you release market-ready IoT devices. From interoperability issues to security concerns, we’ll delve into the complexities of IoT testing and provide practical solutions to overcome these challenges. With the right approach and a thorough understanding of the testing landscape, you can ensure that your IoT devices meet the highest standards of quality and reliability, setting the stage for success in the rapidly evolving world of IoT.

Top 5 IoT Testing Challenges

1. Diverse Communication Protocols

In the world of IoT, communication is key. With a wide variety of devices communicating with one another and with central servers, ensuring that the communication protocols used are reliable and secure is of utmost importance. However, testing these protocols can be a major challenge for IoT testers.

IoT devices rely on various communication protocols to interact with controllers and other devices, such as Constrained Application Protocol (CAP), Extensible Messaging and Presence Protocol (XMPP), and Message Queuing Telemetry Transport (MQTT). These protocols play a crucial role in building a connection between servers and devices, enabling data exchange and enabling devices to perform their intended functions. However, testing these protocols is complex, as they must be tested under real-world conditions to ensure they function as intended. To overcome this challenge, IoT testers must employ a range of testing methods and tools to validate communication protocols, ensuring the reliability and security of the IoT devices they test.

2. Interoperability Issues

One of the biggest challenges in testing IoT devices is the wide variety of hardware and software configurations used by different manufacturers. In a smart home, for example, there may be countless combinations of hardware, software, operating systems, and firmware for IoT devices to be tested, leading to potential interoperability issues. Given the vast number of possible combinations, it’s difficult to test every scenario, making it challenging to ensure compatibility between devices. To overcome this challenge, IoT testers must employ a range of testing techniques, including the use of simulation tools and emulation environments, to validate the functionality and compatibility of IoT devices under various scenarios.

3. Device Diversity

Testing an IoT app is a complex challenge due to the wide variety of devices with varying shapes, screen sizes, and operating systems, each with unique hardware and software specifications. Ensuring that an IoT app runs seamlessly across all these devices and combinations is challenging because each device may have different requirements and capabilities.

Additionally, IoT devices often receive version updates, firmware updates, and software updates, making it difficult to test them effectively each time an update is made. This demands robust testing capabilities and a focus on continuously testing and validating IoT devices to ensure they function as intended.

4. Security Concerns

IoT devices are highly vulnerable to cyberattacks due to their lack of robust built-in security features. This makes them a prime target for hackers looking to exploit sensitive data collected and processed by these devices. The dynamic nature of security threats and the fact that most IoT device users believe it’s the manufacturers’ responsibility to secure the devices makes the situation even worse. Additionally, the wireless nature of these devices puts them at a higher risk than those connected via a wired system, making it even more critical to secure them against cyber threats.

To overcome these challenges, IoT testers must continuously develop new tools and techniques to effectively test and validate IoT devices and ensure that they remain secure from potential cyber threats.

Also Read : IoT and Fire Hazards: How Quality Testing Niche Govt Apps can Save Lives

5. NetworkConnectivity Problems

The performance of an IoT device is significantly impacted by network connectivity, which must be seamless, rapid, and consistent to ensure that the device operates effectively. However, this poses a significant challenge for IoT testers, given that various devices are connected to different networks that have varying levels of security and reliability.

Network configurations can also be unstable, and channels may be hindered, making it hard to test IoT devices in all possible network conditions. As a result, IoT testers must devise innovative techniques and tools to assess the device’s performance under different network conditions and ensure that it works effectively, regardless of the connectivity issues.

Solutions to IoT Testing Challenges

What are the solutions to overcome these distinct challenges faced in IoT testing? Let’s explore the proposed solutions to each of the identified obstacles:

1. Test the Communication Protocols

Communication protocols are crucial to establishing a connection between servers and devices. Therefore, you can’t afford to ignore the diversity of communication protocols. You need to ensure that the technology or tools used by the IoT testing team support the key communication protocols used by various IoT devices. Also, IoT testers should use application programming interfaces (APIs) and protocols for automated tests. Note that the test design should depend on the protocols and APIs used.

2. Critically Test the IoT System’s Interoperability

The IoT automation team should perform a thorough investigation to determine the system’s interoperability. This will ensure various IoT devices can seamlessly work together. To do so, the testers can collect information from end users to assess the software versions and devices they are using. Doing so will help them identify the most common combinations. This allows them to select the most reasonable subset that can be effectively tested.

Evaluating the performance and behaviour of the IoT system under various hardware and software versions can be done by your team using simulation and emulation tools, as well.

3. Test the Device Compatibility of the IoT Device

You must comprehend the limitations and capabilities of IoT devices because they come in a variety of sizes and shapes in order to test them properly. As a result, you need a solid test approach to determine the device’s architecture and determine whether it depends on outside services. This is so that tests won’t always pass if a third-party service is changed. Continuous automated tests can aid in identifying changes to third-party services in this situation. Alternatively, use device simulation tools to test the performance of each IoT device under different scenarios.

4. Security Testing

Performing thorough security testing is crucial to ensuring the IoT system is protected. This will help prevent potential threats and cyberattacks. As an IoT tester, you should use strategies, such as vulnerability scanning and penetration testing, to identify and mitigate security risks in the system.

5. Test for Network Configuration Issues

To verify that IoT devices can interact with one another and the cloud without any issues, thoroughly evaluate the network settings. To ascertain how the IoT system will behave under various network configurations, use network simulation tools. By doing this, you’ll be prepared for any sudden changes in the network settings.

Wrapping Up

In conclusion, IoT testing is a critical aspect of the development and deployment of IoT systems. It presents unique challenges that must be addressed to avoid severe consequences such as security breaches and product recalls. A comprehensive approach to testing that includes the use of appropriate tools, methodologies, and best practices is essential to ensure the reliability and security of IoT systems, as well as their ability to deliver the desired outcomes. As IoT systems continue to expand and evolve, the importance of thorough testing will only continue to grow. By embracing a comprehensive approach to IoT testing, organizations can confidently deliver high-quality IoT products that meet the needs of their customers and are safe for use in the market.

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As predicted, this year saw the advent of several novel technologies that can help organisations transform their IT workforces across all major verticals. However, the quality engineering domain has several major service lines that are geared to accommodate and expedite comprehensive technology transformation.

Before we dive deep into two of the most sought-after emerging technologies, let’s briefly discuss digital quality engineering.

Digital Assurance and Quality Engineering: The What and Why?

The long-held definition of quality engineering is: “the discipline of engineering concerned with the principles and practise of product and service quality assurance and control.” (Source: Wikipedia) The field of digital quality engineering extends beyond software testing. 

Stress detection through stress testing is an essential component of quality assurance. Initially, manual quality assurance was used to support the mainframe era, which was followed by the introduction of automation through the World Wide Web (WWW), Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and so on. Businesses have evolved into an “Intelligent Digital Mesh” (IDM), and the Cloud Computing revolution has ushered in the “digital quality assurance” method.

The three components of speed, quality, and intelligence combine in digital QA to establish a unified approach to accelerated Continuous Quality Improvement (CQI). As organisations scramble to attain a digital makeover, digital quality checkpoints shine through with enhanced risk mitigation and holistic customer-centric solutions. Many aspects of digital QA, such as cognitive QA, IoT testing, cloud testing, chatbot testing, and voice bot testing, aid in digital transformation.

As companies mature in addressing this new IT environment, Digital QA will evolve to address the end-to-end automation of test ecosystems, improve the velocity of quality delivery, and enable Agile and DevOps frameworks and AI (AI). One such strategy that incorporates AI and ML (Machine Learning) into automation is autonomous testing.

Best Read: Testing a bank application: A Success Story

Autonomous Testing: The Next Big Thing in Automation

Many sectors, including software, strive for flawless user experiences. However, to deliver swiftly, organisations must reduce delivery cycles from months or weeks to hours or less. This is where automation comes in. Before automation, test cycles were frequently lengthy. Handwritten tests have a set of stages and checks that require involvement. 

When the first wave of test automation arrived, it completely transformed the test execution process. Automation allowed tests to run more quickly and constantly. However, the disadvantages of manual triggers and maintenance issues quickly became apparent. The CI/CD methods further blurred the line between developers and testers. At this moment, the automation sector appears to be at a standstill.

Challenges of Automation Testing

Some experts argue that automation testing, when faced with fast-paced new-age practices, failed to fully deliver on its promises. Some of the main challenges with automation are listed below:

Challenge to build

A high degree of skill and domain expertise is essential to creating high-quality automation scripts. These include proficiency in programming languages, mastery of the tools, and thoroughness with client requirements. Manual creation of tests remains the major bottleneck for automation.

Costly debugging and maintenance

It would not be incorrect to say that automation is a maintenance nightmare in and of itself. It incurs high costs and is time-consuming. Sometimes a minor update to the GUI would result in re-recording an entire script. The fact that debugging a script is equally troublesome adds to the difficulty.

Stability concerns 

Considering the most widely used Selenium automation tool, most scripts rely on XPath. Any change in it, and the scripts become useless. Also, the scripts don’t consider that the infrastructure is inherently unstable, so tests “fail” when they time out or crash.

Automation vs. Autonomous Testing

We read out both to test in automation, considering all forms of testing as verification of interaction. What if we could automate dictation entirely? To do this, autonomous testing makes use of AI and ML.

Autonomous testing is the automated creation and maintenance of tests and the analysis of results using an application blueprint. Using machine learning algorithms, these tools cut through redundant data in testing and spot irregularities.

Benefits of Autonomous Testing

• Early bug detection through impact and root-cause analysis lowers the risk of failure.
• Autonomy in test infrastructure facilitates faster test cycles. 
• Ability to self-heal the scripts while encountering unexpected application changes.
• Reduction in maintenance costs through automatic identification of scenarios and test data
• Better allocation of resource productivity by eliminating human dependencies and skillset requirements 
• Increased productivity with faster and improved release cycles

The transition from testing automation to fully autonomous testing is in and of itself a challenge. Most businesses’ current automation approaches can be summarized as follows:

• Assisted Automation: Using an automation tool, QA creates and executes tests, but manually manages and maintains scripts.
• Partial Automation: This tool helps create test cases and execute them, and the tester is responsible for test monitoring.
• Accelerated Automation and Unassisted Automation: The tool manages every component of the test, reducing the tester’s responsibility to assess the impact of a change.
• Autonomous Testing: The automation tool is redefined as autonomous and learns from failed tests to make the best judgments. Human-machine collaboration via seamless AI-powered systems is critical to embracing total autonomy. 

Chaos Testing: A Method for TiP 

While autonomous testing seeks to reduce test cycle times, chaos testing employs “Testing in Production” (TiP) to increase the fault tolerance of a built system.

What is TiP? 

Testing in production is based on the premise that only real-time input can successfully supplement the quality assurance process. Every version release is, in theory, a production test with high value-to-cost risk.

Principles and Steps in Chaos Testing 

Chaos testing assesses the resilience of a system to unexpected events by inducing failure. Experts explain this by stating that it is preferable to break a system intentionally at the optimal time than to have it crash unexpectedly. With the rising importance of “mean time to repair” (MTTR), it is vital to minimise the recovery time of a business.

Chaos testing is based on the following established plans and experiments

• Create a hypothesis: Here, we define the steady state, which is the system response to unstable variables. Metrics such as the total throughput, error rates, and system latency help make assumptions about its status. 
• Design and run experiments: Identify, design, and run all probable failure scenarios in a controlled manner. Doing this ensures you have a backup plan for even the worst-case scenario. if the backup strategy is unclear, the system recovery pathways are documented.
• Analyse the impact: Compare and contrast the outcomes with the initial hypothesis. The error difference in the metrics aids in deciding whether to improve or limit the blast radius and reveals additional issues. 

Benefits of Chaos Testing 

• Prepare for the unforeseen: By simulating all potential failures, you get the information necessary to fortify the system against surprises. 
• Uncover the unknowns: It illustrates the system failure reaction and facilitates the development of associated recovery routes by utilizing several experiment-specific indicators. 
• Reduce system downtime: With a good grasp of the common failures encountered by a system, we can reduce the MTTR by making continuous fault injections and regularly targeted improvements. 
• Improve customer satisfaction: Chaos testing empowers organizations to gain confidence in their systems’ resilience and increase customer satisfaction.

Chaos Test Tools 

The origin of chaos testing dates to when Netflix transitioned to the cloud via AWS. The unexpected AWS outage caused the Netflix platform to be unavailable for several hours, inducing them to implement the concept of chaos engineering. Today, many tools are available to perform Chaos testing, such as: 

• Chaos Monkey: The first open-source tool via which Netflix assembles an entire suite of fault injection tools called Simian Army. It attacks by discontinuing the VM instances. 
• Chaos Blade: Built for Alibaba, to support wide range of platforms and has dozens of attack modes.
• Chaos Mesh: This tool possesses a fully featured web UI encompassing 17 unique attacks.
• Litmus: This is a Kubernetes-native tool that utilizes Litmus probes to monitor the system’s health statistics. 
• AWS-FIS: This works only with AWS services through a fault injection simulator. 
• Gremlin: Offered as a SaaS technology, Gremlin can be integrated with CI/CD to test entire systems. 
• ChaosNative: It is a SaaS platform that hosts LitmusChaos to perform end-to-end chaos testing. 

Chaos testing is thus a proactive approach to testing, which can begin with small trials in controlled environments. It can then be effectively implemented and scaled up through proper rollback planning and appropriate tools. 

Indium’s Approach to Autonomy in Testing 

Through the uphoriX Test-Accelerator, Indium has taken a step towards autonomy by providing low code automation, intelligent scripting, Script once NFT, a native test data generator, and swift compatibility. Indium considers smart test automation as an emerging vertical that ‘addresses one of the fundamental aspects of cloud-native initiatives – the velocity of code deployment’ and a prerequisite to the ongoing digital transformation.

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An IoT botnet is a group of devices, appliances and computers connected to the internet that have been coordinated to launch a cyber-attack. These attacks could be very fatal.

Almost every device that can be connected to the internet is an IoT device. Our reliance on the internet is increasing every day.

This increased reliance on the internet comes with an increased chance of these IoT devices being hacked, and used by nefarious people as part of a botnet for cyber attacks.

According to the reports from Gartner, by the end of this year, there will be 20 billion IoT devices in use.

This means that about 3 IoT devices exist for every human. By 2025, the number of IoT devices in use is expected to grow to 75 billion.

With the high demand for IoT analytics services, this number is only going to grow. If the cybersecurity of these devices isn’t taken seriously, there will be more hacks and botnet attacks.

In the past, some sci-fi movies have depicted everyday appliances acting on their own and turning against humans. Unfortunately, this is now a reality. There have been some botnet attacks in the past. Here are some infamous IoT hacks and attacks you should know about.

Keep reading until the end, as I have also mentioned how to stay protected from these attacks.

The Jeep SUV Attack

In 2015, some researchers from IBM discovered that they could access the internal software of a Jeep SUV and take advantage of a vulnerability in the firmware update feature of the software.

These researchers were able to gain control of the vehicle, turn the steering, accelerate it, decelerate it and move the car to any direction they wanted.

There is a need to ensure that vehicles are safer, as more driverless electric cars are made available for public purchase.

Some electric vehicles like Tesla offer improved features through software updates and car manufacturers need to protect users of these cars from cyberattacks.

TRENDnet Webcam Hack

The SecurView cameras sold by TRENDnet were supposed to be good security cameras that could also be used as baby monitors and for other purposes.

They were also expected to be secure and safe from cyberattacks.

Later, it was discovered that the SecurView cameras could be hacked by anyone who could find the IP address of the devices.

Some hackers were able to capture audio recordings from the cameras.

Later it was discovered that TRENDnet was transmitting the login of users over the internet without encrypting them. So, anyone that could intercept the package would get the users login details easily.

If a supposedly secured device that was used in several homes was prone to cyberattacks, it means that nothing is 100% safe.

Some supposedly safe devices could be revealing your personal data without you knowing. To prevent things like this from occurring, you should run a penetration test. 

You should also install a VPN on your home router. A VPN will encrypt your internet communications. This will make it extremely difficult for hackers to access or read them.

The Finland Thermostat Attacks

In 2016, some internet-connected thermostats in Finland came under attack from some hackers. These hackers gained access to the environmental control systems of two apartment buildings and set the thermostat to freeze the residents.

When authorities tried to fight off the attack by rebooting the environmental systems, the systems became stuck in a continuous loop that lasted for almost two weeks.

Freezing cool Isn’t it?

The Mandalay Bay Casino Aquarium Attack

This casino attack is weird and stranger than other casino attacks that you’ve ever seen or heard of.

A lot of casinos are highly secure with a lot of security staff on the ground. Some hackers decided to think differently and they gained access to the casino through a thermometer located inside an aquarium in the casino’s lobby.

This is some Ocean’s 12 stuff.

After the hackers gained access to the casino’s network, they found the casino’s high-roller database and extracted it through the thermostat.

This compromised the personal data of several people and affected the casino.

The Office Printer Attack

It is when your office network printer is hacked by an outsider.

A lot of people fear using the office printer for several reasons. One, you risk allowing a coworker to see some private documents that are not meant to be seen by him/her.

Also, you might be queried for using more paper and colored ink/toner than you should. But there is something worse than all that.

Printers? Seriously?

Some printers that are password protected could even reveal personnel passwords to hackers. As you can see, this is more fatal than other printer-related dangers.

Yes, these outsiders could gain access to the printer memory and view office contracts, personnel information, client information, deal sheets, and other sensitive, private corporate data.

How to Stay Protected?

Here are the most sensitive areas that organizations need to pay attention to in the future:

More and more IoT devices are used every year. These devices have found various applications in the healthcare, automobile, food, fashion and entertainment industries.

More companies are developing and rolling out IoT devices for consumers. From the workplace, people can now use their smartphones to access their refrigerators, ovens, indoor and outdoor lights, heaters, TV sets and more.

All these make IoT testing more important than ever.

The Need for IoT Testing

IoT testing is more than physical interaction. It also involves emotional and sensory interactions.

Digital devices now respond to voice, motion and touch controls rather than keystrokes alone. Human experience testing is a fundamental aspect of IoT testing.

IoT testing involves testing all IoT devices for different combinations of operating systems and software.

There will be a need to use simulators and emulators to test such products thoroughly. Test-Labs will be needed to test these IoT devices well.

IoT testing should be all-encompassing. Every aspect concerning the production of an IoT device should be tested and verified before they are released to the public for consumption.

If the testing is not thorough enough, there could be a lot of irreversible damage.

The post 5 Infamous IoT Attacks – How To Stay Protected? appeared first on Indium.

It could be in the form of

• Wearable gadgets such as lifestyle and healthcare devices that help capture data about different health parameters, displaying them and even notifying healthcare professionals when needed.
• Industrial and Community conservation and safety devices that detect movement and switch lights on or off, cut off supply, manage traffic signals among many other functions to improve the use of energy.

Such devices have transformed the way industry works. Cisco believes that the economic value added by the “Internet of Everything” will be nearly $19 trillion by 2020, while a McKinsey Global Institute research report suggests it will be $6.2 trillion by 2025.

In any case, it is a very large number, and dwarfs past technology trends [e.g. $150 billion for Fintech by 2021 as per a PWC report; Cybersecurity, at nearly $1 Tn, as reported in PR News Wire.]

The Stumbling Blocks

However, this journey to achieving the lofty goals is fraught with challenges:

1. First is the lack of uniform standards and protocols, which are also difficult to achieve because each device is one of its kind. This causes different devices to use different network technologies and operate on various networks. This is becoming more of a problem since devices are starting to interact and execute pre-determined decisions and pre-programmed inbuilt logic. Compatibility and behavior become suspect and need to be tested for consistency.
2. Traditional frameworks are not yet capable of capturing, routing, analyzing data, and providing insights for meaningful business decisions. This also makes managing the data difficult, needing huge storage, strong data management, and analytical skills.
3. Privacy and security concerns will become a major concern in the IoT ecosystem. There have been several instances of hackers taking over unsecured webcams to spy on the owners. For homes to remain a haven, IoT developers have a responsibility of ensuring their products and solutions are immune to access by the undesirable intrusion.
4. The huge amount of data that sensors generate every millisecond introduces several complexities in managing it. The 4 Vs of Big Data: Volume, Velocity, Variability, and Veracity will have to be dealt with efficiently.
5. Different resources and technologies need to be integrated as IoT straddles different types of devices. Some of the technologies typically used in IoT include:

• RFID (Radio Frequency Code) tags and EPC (Electronic Product Code)
• NFC (Near Field Communication) for facilitating two-way interactions between the devices.
• Bluetooth to enable short-range communications mostly in wearable technologies.
• Z-Wave, a low power RF communication technology for home automation, lamp controlling etc.
• WiFi for transferring files, data, and messages seamlessly.

1. Sustenance is yet another challenge as many device makers are startups that may or may not be able to sustain in the long run, or may get taken over.7.
2. Failure to map or incomplete mapping of relevant systems to IoT deployment contributes to device failure.

The Rollout does not typically consider the practical use of the IoT devices or how they will play into the daily life of the users.

The focus is on the flash rather than the substance – it will take time for the useful business processes to be identified and put into place.

During a Cisco survey of 1,800 IT leaders in the US and UK, a strong message that emerged is that one needs to be ready for the failure of IoT initiatives.

As many as 60 percents of IoT projects could not scale up after the proof of concept phase. The survey also revealed that nearly 75 percent of IoT initiatives are business failures.

Some of the factors that this study identified include

• The initiatives take too long
• The enterprise has limited expertise
• Data quality is suspect
• Poor integration across teams
• Budget over-runs

Testing to Clear the Path

The causes could be rooted in poor project management, architectural development without foresight, or during the rollout.

While some are dependent on business decisions, the technology aspects can and should be tested in a timely manner as well as periodically on an ongoing basis.

Without a doubt, the IoT solution needs a comprehensive testing plan that includes

• Requirement validation
• Pilot Testing to verify business value
• Usability testing
• Security
• Connectivity
• Performance
• Compatibility Testing
• Regulatory Testing, especially for healthcare, financial and wherever applicable
• Upgrade testing
• Periodic audits of systems, hardware, and sensor nodes

While a start-up may ambitiously decide to integrate testing with development, an external testing partner with a cross-domain, technology, and protocols experience can help improve the chances of success of the initiative.

Involving the testing partner right at the project planning stage can help anticipate and incorporate course correction at every stage to minimise the cost and time of development.

It also frees up resources for research and development, while the testing team provides the inputs needed to make it a high-quality product.

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Considering the practical hiccups in generating big data from the thing (device) in a testing environment, it is crucial to evaluate data simulation and virtualization methods.

Stubs can be considered as an option during early stages, whereas, data recorders can assist as an alternative at the later stages.

Beyond test planning and data simulation, metrics-driven exhaustive test execution is performed to achieve a stable system. 

QA team can split IoT test area into two layers i.e. device interaction layer and user interaction layer. However, QA has to be performed across both the layers. It is actually easier to identify techniques and the types of testing that can be adapted to each layer to enhance the QA strategy.

The device interaction layer

This layer acts as the connectivity between the software and the hardware components of a real-time ‘IoT environment interact’.

A typical example will be a Bluetooth device transmitting real-time data to a mobile device application. Sometimes, a lot of interaction testing will be done on the functional side of QA.

However, other types of testing could also be required. IoT testing will cover the spectrum of other required elements listed below, in addition to typical software testing:

Conformance with standards

These are mostly device performance traits that are precise to the devices and sensors. These attributes must be validated against the standards of the device and its communications protocol.

Hardware vendors perform most of these tests, but there could be a certain domain or use-case specific requirements such as the use of such devices in an environment that was not tested.

Interoperability: The ability of different devices to support the required functionality among themselves, other external devices and implementations.

Security: With billions of sensors in the making, it’s crucial to tackle data privacy and the security concerns across the IoT ecosystem.

The following are the different types of security testing requirements:

• Identity and authentication
• Data protection
• Data encryption
• Storage data security in local
• Remote clouds

The user interaction layer:

This layer is the touch point between the thing (IoT Device) and the user. The success of the overall system depends on the seamless user experience.

Key testing areas in this layer include:

Network capability and device level tests: The specific aspects of network communication such as connectivity are validated by simulating different network modes in addition to device-level validation such as energy consumption tests, etc.

Usability and user experience: Usability and user experience are important in terms of the real-time usability; it involves both human and machine interaction and also the real-time experience that the IoT system provides.

For example, contactless payments compared with a physical card-based payment.

The IoT services and back-end IoT environment:

While integration testing of the interfaces is a key, there is a complex data layer that comes into play.

For example, a classic IoT system boxes a complex analytical engine to ensure an exceptional user experience.

This creates a QA environment to assist validation of such interface by addressing the growing data volume, velocity, and variety challenges.

The front-end validation environment can be done by assembling data recorders and simulators.

The service and data layer validations will involve complex simulation such as the generation of millions of sensor hits, machine-learning algorithms and the time-boxed traffic. There are a few methods to create such an ecosystem;

For example, leveraging sandboxes of development services or creating mock environments using virtualization tools.

However, numerous implementation synergies are required to establish a working set of environments for through services and back-end validation platform.

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The new research report on “Internet of Things (IoT) Testing Market by Testing Type Service Type, Application Type, and Region – Global Forecast to 2021”, published by MarketsandMarkets, the market size estimated to grow from USD 302.9 Million in 2016 to USD 1,378.5 Million by 2021, at a Compound Annual Growth Rate (CAGR) of 35.4%. – PR Newswire

To get the attention of IoT, QA vendors must view software testing more than devices and sensors.

They should view testing from a more technical perspective as IoT comes with the enormous volumes, variety and velocity of data produced across a clever and connected ecosystem.

There are multiple definitions for IOT, but we define it as a network of devices with sensors or embedded technology, connected to the internal or external environment for taking intelligent decisions

There are three core components of IOT –

• Things – The real world devices with sensors and embedded technology that are expected to communicate
• Communication – Networking Component – Helps the things to communicate with other things or the external environment
• Computing – This is done either by mobile devices, desktop or server, based on the amount of data that need to be processed and analyzed

IoT’s Key Components

QA Challenges?

The top challenges and solutions that QA managers can count on are:

The hardware/software union: Devices, sensors, applications and network from the IOT ecosystem. Though, there is a change from absolute QA of applications in a distinct environment to dynamic environment.

With several truckload of sensors and devices providing a combination of experience with intelligent software, sheer functionality validation is not enough in complex IOT environment.

A functioning system is not sufficient 

To endorse an IoT system, it is not just enough to only make sure a working device software. This necessitates a very robust testing validation process.

The use cases can be exceptionally complex in real time; the different variety of real-time scenarios can become a dare for QA.

Interactions between huge numbers of sensors:

It is very challenging to build an environment to test a instantaneous IoT implementation.

It involves validating the correctness as well as scalability and reliability assessment of the environment.

The system is built on several analytics engines and requires a significant experience in simulation to build a test environment.

Whereas, the hardware and the protocols are usually tested well by device makers. Understanding application’s intelligence and the device’s real-time complexity is an extremely new art for the IOT application developers and QA experts.

Types of IOT Testing

A meek IoT ecosystem steals a number of software testing tactics from regular QA jargons to test IoT apps.

It is essential to emphasize on all 3 core modules of the IoT system to delight the functional testing and connectivity testing as critical elements of whole IoT ecosystem.

The following kinds of QA have to be performed through the IoT ecosystem:

Performance testing services to check the core computation competences of the embedded software system and cover the swiftness of the communication network model.

Security testing services to cover autonomy, spying and privacy.

Compatibility testing with the possible combination of device version, protocol version, mobile devices and mobile OS version.

Exploratory testing to perform tests from the user’s viewpoint and beyond pre-defined test procedures.

The IoT environment puts forth an innumerable QA challenges. Significantly, the QA service provider should look at these tasks as opportunities to build appropriate QA solutions.

Let’s discuss IOT Testing in our upcoming blog.

Please feel free to post your suggestions.

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