Modern DevOps approach and software quality issues
Changing customer preferences has opened a new array of possibilities for companies to come up with new and innovative software products. As the products are getting advanced, companies are investing heavily to improve the development process. The growing focus on software development advancements has given rise to the need to combine the development process alongside QA processes so that the quality of the product can be maintained right from the development stage. Hence, the organizations are relying on and investing more in setting up DevOps environment. DevOps is a multidisciplinary approach that combines software development and IT operation, and also interconnects all the other aspects of the development workflow. DevOps is one of the major technological shift witnessed at the software development front recently.
The DevOps methodology is usually performed in smaller segments with each segment handled by a specific team such as technical architecture, software development, testing, and so on. To ensure a seamless workflow, continuous integration and continuous delivery (CI/CD) is performed; with so many processes involved there is a higher probability of software quality issues. To resolve the quality issues in the complex DevOps workflow, the conventional testing process is not a feasible option. Testing should be either performed by test automation tools or advanced AI and ML integrations.
Significance of QA in DevOps
As the DevOps scenario involves continuous integration and continuous delivery process, the ownness of quality lies on both the development and the testing team. Testing is carried throughout the development cycle, simultaneous to the development process. As testing is carried out earlier in the development cycle, the scope of errors is reduced tremendously.
Any misalignment between the QA and DevOps will yield an inefficient result. As the process involves continuous integration and delivery, the errors could appear even at the code integration, which can lead to the wrong process resulting in drastic deviation from the expected outcome. This scenario can be avoided by incorporating QA earlier in the development cycle. QA is an inevitable part of the DevOps process; without it, the final outcome cannot be achieved as expected.
The changing role of testers
As the emphasize on software quality is on the rise, the role of testers is also changing. They are no longer a mere resource who check the product functionality and performance, rather they analyze the user perspective while delivering the product. Testers play a critical link in ensuring the quality is top-notch before release and also speed up the development and deployment cycle. The tester’s role is evolving into a new position known as SET or Software Engineer in Test. SETs are the engineers who build solutions for testing/quality issues.
DevOps practices support efficient operational and deployment for better product and business outcomes. Hence, it becomes important to build a good QA strategy that can align with the DevOps approach. The concept of incorporating QA is to ensure continuous improvement in the delivery process. DevOps helps organizations to streamline the workflows and speed up the delivery process; thereby, helping sustain and succeed in the highly competitive market.
Top test automation tools for DevOps approach
- Allows integration with popular CI/CD tools like Jenkins, Travis, etc.
- Cross-browser test automation on more than 2k browsers and operating systems
- Allows automated screenshot testing with 25 combinations at a time
- Live interactive testing through VM hosted on LambdaTest cloud
Tenjin is a codeless test automation platform used across global banks and financial institutions. Designed specifically to test banking and financial software, Tenjin brings in an innovative approach to cover the functional and non-functional testing aspects of the bank’s core software system. It performs testing for varied banking workflows ranging from transactions, loan originations, credit processing, digital banking, a lot more.
- A codeless, auto adaptive, and agile test automation solution for banks and financial companies
- The robotic-powered engine offers an efficient end-to-end testing solution with resilience, reliability, consistency, and speed
- An innovative platform that offers client-specific services — choose Tenjin Enterprise for complex enterprise applications, Tenjin Digital for digital banking application with 360-degree multi-platform coverage, and Tenjin Connect for API testing
- Agile testing for CI/CD tool integration
- Digital clients can expect effortless cross-platform testing with high coverage
- Intensive testing of functional, UI/UX, performance, compatibility, security, and network testing
Kobiton is the modern test automation tool designed for testing of mobile and IoT (Internet of Things). It is an open-source, scriptless, AI-driven approach that automates all the functional tests along with performance, security, user experience, and compatibility tests.
- Scriptless automation tool
- Works on 100% standards of Appium code
- Smooth CI/ CD integrations
- AI-integrated test solution
- Functional, visual and performance testing on real devices
- Extensive session exploration and reporting
- AI and ML integrated for seamless performance
- Allows code and codeless test creation
- Supports testing of enterprise applications such as SAP, Oracle EBS, and Salesforce
- Allows smooth integration of CI/CD tools
- Easy and efficient test management, issue tracking, and carrying out complete test cycle
- Supports all operating systems
TestProject is a free, Cloud-based test automation platform. It’s a scriptless test automation tool that offers end-to-end testing solution for web, mobile, and API (Application Programming Interface).
- Codeless test automation tool
- Easy integrations with CI/CD workflow
- Built-in integrations for tools like Slack, Jenkins, BrowserStack, etc
- Allows advanced scripting for importing existing Selenium and Appium tests
DevOps is the most preferred methodologies for the agility and robustness it offers. The trend may change in the future with technological advancements. With the change in the software development methodologies, the QA tools will also witness a change. The future QA trends are predicted to be purely AI and ML-based, which have already started making their appearance on a small scale.