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Digital Assurance
September 7, 2020
CIO Talk – Hexaware Blogcast: Welcome listeners! This is Sanjog, your host, and the topic for conversation is From Automation to Autonomous Testing
With digitization, we are in a race. The race with our competitors to stay ahead in meeting and exceeding customer expectations. It requires that we keep innovating our products and services using technology, and then take them to the customers as quickly as possible, in some cases, in a matter of a few hours.
For such cases, the traditional approach to testing that is dependent on human intervention simply cannot keep up. Some companies are evaluating a move from test automation to autonomous testing that takes advantage of AI/ML to make testing less dependent on human intervention and self-learning.
So, how does autonomous testing work? Is it ready for the real world? How can organizations transition from test automation to autonomous testing with confidence?
To discuss this, I have with me Tony Mohanty and Nagendra BS. Tony is the Senior Vice President and Global Head of Digital Assurance and Nagendra BS is the Vice President and Head of Practice and Solutions, at Hexaware, a consulting firm focused on transforming IT solutions and solving complex business problems using a combination of human creativity and intellect. Their three-pronged strategy of Automate Everything®, Cloudify Everything®, and Transform Customer Experiences® fast-tracks enterprises into the digital era.
Hello Tony and Nagendra…Thank you for joining us.
Tony & Nagendra says, “Thanks for having me.”
If you wish to directly listen to the Podcast: From Automation to Autonomous Testing, Click here
Today, there are at least a dozen start-ups like Autonomiq, Functionize, Algoshack just to name a few who are building products to enable autonomous testing. They have been able to raise millions of dollars in funding from various PE and VC firms which is also a testimony to the potential rise of Autonomous Testing.
Hope this clarifies why organizations need to shift their focus to Autonomous testing and the opportunity in front of all of us.
As part of intelligent test automation, different elements of autonomous testing are already in place, but not as an end-to-end solution that would make complete testing function independent of human intervention. There are many reasons for this.
One of the biggest reasons is, Testing is not seen as a strategic growth and efficiency enabler in the organizations, and this discourages from any investments to transform the testing function. The other reason is skepticism around the solution being too futuristic and organizations for whatever reasons may not have been able to get the returns on their investments made on test automation.
Also, till recently we did not have the kind of access to AI-enabled technology solutions and platforms like Tensorflow, Kera, Theano and similar platforms which helps in democratizing AI and this was another constraint.
While many of these are ground realities that we have to deal with, another constraint that was holding us back from taking off is also the unavailability of a unified platform to support autonomous testing across all testing types, across all the phases of the testing lifecycle and layers of an application which is what Hexaware has embarked upon.
Let me also share some of the recent conversations with our customers on this topic. At the start of this year, we had hosted many customers across varied industries like airline, banking, insurance, and retail at our campus to whom we presented our vision to move from test automation to autonomous testing. While we had some customers, who bought into this and offered to run some pilots, there were also few customers who were skeptical about this solution.
Some of them are customers whom we have been engaged with for more than 8 to 10 years. While we acknowledge that their reservations are legitimate which are specific to their environments, we are also working with them to address their concerns since we believe this opportunity has greater upside to our customers
As Tony mentioned earlier, one of the proof points from a business potential point of view is the fact that startups focusing on autonomous testing solutions have been able to raise millions of dollars in funding from various Private Equity and VC firms.
While this would give some level of confidence that autonomous testing is real, we are also finding sponsors at the CIO level in our customer organizations who are encouraging to deploy these point solutions.
We recently implemented one of the point solution for a manufacturing services company. Through this solution, we were able to do a seamless conversion of existing manual test assets into corresponding automation scripts with little or no manual intervention.
We have seen test analytics solutions implemented in the industry that use Python-based ML libraries to predict defect patterns of the future releases based on the data of previous releases.
Another example that I can quote is to support testing in Behavior Driven Development or BDD mode of SDLC. We have implemented a solution leveraging Python-based natural language tool kit that can import a Gherkin language feature file and generate corresponding automation scripts without any human intervention.
Similarly, there are many other use cases where we have seen Python-based AI/ML libraries and algorithms like logistic regression, reinforcement learning, clustering being used for solving challenges around automation script maintenance and impact analysis.
Through our experience of implementing different point solutions and elements of autonomous testing, we are now absolutely convinced that AI/ML is real and can make a difference to the way the testing is performed. In fact, AI will not eliminate manual testers completely, but will augment their skills.
We also believe it is critical to have a C-level Sponsorship for the success of this transformation and there needs to be top down approach to adopt the change as it requires collaboration between different IT teams outside of QA involving development, infrastructure, release management teams to make this happen.
Finally, we see a need to have a unified platform that can orchestrate end-to-end testing activities without any human intervention. This platform should be able to integrate seamlessly with existing or third-party automation solutions and if necessary, have its features exposed as services or APIs for external consumption.
As part of our roadmap for ATOP, we have identified more than 50 such use cases across functional and non-functional testing to enable autonomous testing for testing the UI, Service layer and the Data layers of every application.
Nagendra, how should organizations start on the test automation to autonomous testing journey, all along ensuring the quality and accuracy of the results produced, and minimizing risk during the initial implementation as well as when fully operationalized?
The first thing that we would recommend enterprise leaders is to recognize the fact that there is an opportunity to tap by thinking beyond automation. The journey from automation to autonomous would take anywhere between 12 to 24 months depending on their current level of QA and Automation maturity.
An assessment of the current maturity level of autonomous testing and baselining of existing metrics must be done to arrive at a detailed roadmap for implementation.
The roadmap should detail various aspects like tasks that will be done in-house vs leveraging partners, clarity on which are the testing activities that can be completely made autonomous vs activities that will still have to be done manually, what is the approach for building unified orchestration platform, what is the approach for acquiring data across various tools in the eco-system and other relevant details to make both functional and non-functional testing autonomous across all phases of testing lifecycle and all the layers of an application.
From an implementation point of view, we would recommend picking one or two pilot project/programs which are mature enough to take up activities beyond test automation and show the initial proof points before we take up enterprise-wide implementation. There must be an organization change management team created to drive this transformation.
While the QA function would be the owner of this transformation, we would recommend a “skin in the game approach” both for stakeholders within the organization and the partners who would be supporting this transformation.
We also recommend having a well-defined engagement model that would enable organizations to measure partners on the outcomes with attached SLAs/KPIs and at the same time provide necessary ownership to partners so that they can drive the activities. Have a dashboard that provides real-time insights on the expected outcomes, performance against corresponding SLAs/KPIs and accelerate the whole transformation through Organization Change Management (OCM) coaches.
Finally, this transformation will not be successful without the right people on the ground to deliver. Enable workforce transformation through SDETs: Software Development Engineer in Test and take a pragmatic approach for implementing autonomous testing by leveraging existing assets and complement the same with partner capabilities.
Finally, Tony – Transforming from test automation to autonomous testing seems like a significant effort, which may require help from a partner with specialized expertise and experience. Since it is such a new discipline, how should an organization go about selecting the right partner for this effort? If an organization selects Hexaware as a partner, besides just bringing your technology platform, how can your team help in ensuring success, reducing risk, and maximizing business outcome?
With such credentials and experience, we are pretty well positioned to lead our customers all the way in the journey of Autonomous testing.
Once again, thank you, Tony and Nagendra, for sharing your thoughts and insights about how an organization can transition from test automation to autonomous testing to keep up with the pace of business change.
And listeners—I invite you to find related conversations on our website at CIOTalkNetwork.com
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