Open source tools and technologies have become a significant part of IoT testing. This because they fuel the quick testing and integration of IoT platforms with various vendors. It is important to note that open source adoption is at the heart of DevOps provided that the majority of DevOps technologies and tools in the market are open source. This decreases the total cost of testing infrastructure and ownership.
DevOps’s Open Source Ecosystem Assisting in IoT Testing
Software developer in test (SDET), risk-based testing and shift left testing are few of the most sought-after IoT testing strategies. However, DevOps is one of the most effective strategies when you want to generate outstanding test efficiency metrics and coverage along with tangible cost saving due to its support of open source technologies and tools. Other than this, intelligent automation and DevOps is being used by companies to manage IoT at scale and attain quick innovation. DevOps also talks regarding the requirement of infrastructure automation, network, testing, deployment, covering build and IoT test automation.
DevOps can be used to test several IoT application endpoints and product pipelines of an IoT platform. Although testing a device-to-cloud use case, the separate functionality of every component in the IoT platform, including APIs, sensors, mobile network protocols, cloud, the web also requires to be tested. This is where various technologies and tools of DevOps can assist.
The Significance of DevOps Technologies and Tools in Automation Scenarios and IoT Testing
Let’s understand the way common technologies and tools of DevOps help in IoT software testing.
- Mobile functional test automation, web, and UI can be done with DevOps tools such as Appium, Selenium, Robotium etc., with multiple operating systems and variants.
- For functional API automation, JMeter can be used.
- For network automation, Python is widely used. Interoperability with various protocols like CoAP, MQTT, Zigbee can be automated using Ruby or Python scripts. API virtualization, service or sensors with Python can be utilized to simulate complete scenarios for performance and load testing of IoT apps.
- Test environment automation utilizing container technology like virtualization tools or Docker such as Vagrant assist in six times more server app instances in comparison to traditional virtual machines, saving big infrastructure costs.
- Infrastructure as Code assists in test environment automation via deployment and configuration automation. DevOps deployment and configuration tools like Puppet, Chef, Ansible can be utilized for this purpose.
- Continuous integration utilizing tools like Bamboo and Jenkins guarantees that the newest piece of the code is being tested and in turn, help to recognize defects early in the testing cycle. A test management tool has this as an in-built feature. The continuous delivery and integration pipeline created with these DevOps tools also help in creating the base of continuous testing of microservices via integration and contract testing. This leads to quicker deployment and provisioning of an IoT test environment. This means speedy recovery from test failures that lead to directly enhancing test cycle time.
DevOps for IoT Regression Testing
Regression testing is considered as one of the most cost-incurring activities. This is because testing is required to be done iteratively with each recently added feature. Setting up an automated workflow to perform complete sensor-to-cloud regression tests for IoT utilizing the tools above DevOps tools controls the cost of reworks and testing efforts. Therefore, incorporating DevOps in IoT software testing plan gives enhanced coverage in term of regression testing, performance testing, load testing, compatibility testing, and functional testing. After building DevOps-led test automation engine, it can be easily scaled by implying the same test plan across various verticals.
