Autonomous testing is an emerging testing practice in which tests are created, driven, and managed by AI/ML, completely removing the need for human intervention.
Essentially, autonomous testing is a higher level of automation for automation testing. This is a recent shift in the testing industry as AI technologies evolve to be more advanced, opening a promising era of human-machine integration testing.
Currently, autonomous testing is only in its infancy. Even the leading brands who have implemented AI features to their automated software testing processes still require a certain level of human intervention.
To reach complete Autonomy (equivalent to level 5 in the diagram below), we need considerably more research and development.
5 Benefits Of Autonomous Testing
1. Accelerated testing speed with considerably lower resources required
With autonomous testing, the system can function as an independent entity, taking full control over the process of end-to-end testing thanks to intelligent algorithms. The autonomous testing tool can locate and inject the necessary data, analyze it before performing all of the testing activities.
In addition, autonomous testing can also increase the depth and scope of tests to improve software quality. Tests which are often avoided due to their complexity can now be run unattended, and we can effortlessly execute thousands of such tests each test run, achieving a level of test coverage that is impossible if we only use manual tests.
This saves tremendous time for QA teams, empowering them to speed up their development life cycle for faster product release, shrinking time to market.
Thanks to that reduced human involvement, companies can achieve higher productivity per developer/automation engineer. Resources can then be allocated towards more critical areas of the business.
2. Increased system versatility
The system can easily adapt itself to the changes in the product thanks to continuous learning algorithms embedded in its core. Over time, the Autonomous Testing system will be able to generate better and better test scenarios.
A truly autonomous testing system is also ever-improving. It can continuously learn from historical test data to evolve and adapt its model to the organization’s specific needs. This can be helpful for organizations with applications that frequently undergo changes and modifications.
When changes happen, the autonomous testing system can also automatically repair test scenarios and test scripts without any human intervention. The cost of test maintenance will consequently be reduced dramatically over time.
3. Open up automated testing bottlenecks
For many software development teams and organizations, it is often admitted that automated testing presents its own unique challenges. For one, it is difficult to implement a test automation framework and architecture, find the right automated testing tools, or identify which tests are optimal for automation within the infrastructure. That’s not to mention the occasional need for manual testing, which is tedious and time-consuming.
Autonomous testing leverages AI technology to address all of those issues by cutting through the noise and focusing only on what’s important.
4. Lower level of skills and expertise required
To create complex test cases, QA teams need to possess some coding skills as well as a good understanding of business requirements to develop test scripts that align with business objectives.
As such, companies tend to be dependent on these skilled individuals, and when the team’s level of expertise can not keep up with the requirements, bottlenecks may occur. With autonomous testing, there is less need for immediate technical skills and more room for learning and training over time.
5. Faster feedback
Automated testing and CI/CD pipelines have already imprinted massive impact on assessing code quality in the past years.
The addition and advancement of autonomous testing only takes this 1 step higher. With the added speed and productivity in designing and maintaining automated test suites, there will be an immediate feedback loop that creates a strong ripple effect on the entire development team, opening up an era of expanded testing coverage and increased software delivery velocity.
Software Testing Platforms Are Heading Towards Autonomous Testing
It is easy to see that autonomous testing is an ambitious and futuristic endeavor that is guaranteed to disrupt the testing landscape. There will be emerging terminologies, concepts, and discussions. For now, there is no true autonomous testing platform on the market, but many software testing solutions are heading towards that future.
As a notable example, the Katalon Platform is a centralized automated testing platform that allows testers to perform functional and visual testing on many devices and operating systems in 1 single place.
Originally developed as an intelligent and all-in-one test automation platform, the Katalon Platform is packed with various AI features such as similar failure analysis self-healing and smart wait to ease up common bottlenecks in test design and maintenance.
Nevertheless, human approval to act upon those features is still required, and therefore it is not yet a true autonomous platform in the market, but progress is being made. Those who aren’t already on their journey to autonomous testing won’t be able to keep up with the rapid development in the industry.
