In this blog post
Revolutionizing QA with AI and Automation
Before a product is in the hands of the customer, it goes through many steps in production. Hence it is imperative to continuously keep track of what is going on and where/if any issues are to be resolved in the processes carried out by the Development, QA, Security, and IT operations teams. It is being increasingly acknowledged that the vicious cycle of unexpected production issues and firefighting can be arrested with AI and Automation-led QA. Even with a robust QA process, it is near impossible to accurately emulate and test all pathways that actual users will take with the application in real-time – unless there is an AI script generator.
GAVS conducted a webinar on this topic that covered a range of discussion points from comparing traditional QA and new-age AI-based QA; understanding coverage, scalability, and resilience of both these types of QA; the possibility of functional testing to performance, load, security testing – all in one QA platform; auto-remediation of issues by the power team – AI & Automation; integration of AI testing and DevSecOps; and the holy grail of fully autonomous, self-healing scripts!
This blog captures some of the takeaways from the webinar on ‘Revolutionizing QA with AI and Automation.’ The link to the entire webinar is available at the end of the blog.
Balaji Uppili, Chief Customer Success Officer at GAVS Technologies, moderated the session. The speakers were Juliana Koshy from GAVS and Jigar Pithwa from Appvance. Juliana Koshy is the AVP of Customer Success at GAVS with over 17 years of experience, currently leading digital transformation initiatives where QA plays a significant role. Jigar Pithwa is a Senior Sales Engineer at Appvance with over ten years of experience working across various industrial domains, currently focusing on driving business outcomes with quality assurance.
Quality Assurance – The General Perspective
When it comes to digital transformation, most enterprises have a vision centered around high levels of customer experience, business outcomes, service reliability, and service outcomes. This vision can be realized only through revolutionary changes in infrastructure, application development, quality assurance, security processes, etc. Although organizations understand that the quality of the outcomes and end-user experience is a direct derivative of the quality of enterprise applications, quality assurance is often looked at as an addendum and not as an integral part of transformation initiatives. The focus is more on faster time to market and hence businesses deploy an agile development framework to deliver iteratively in shorter release cycles. QA is routinely relegated to the back seat.
The Emergence of AI in QA
Existing testing methodologies such as Test-Driven Development (TDD), Behavior-Driven Development (BDD), or Acceptance Test-Driven Development (ATDD) are based on legacy manual testing approaches. With the number of application issues that crop up in production, it is evident that there needs to be a radically new approach to quality assurance for complete test coverage. Another aspect of equal importance is test automation. The major driving forces for test automation are the need for agility in how testing is done and the demands for faster time to market. For the QA team to keep pace with the agile mode of development, traditional testing is not enough, thus making AI and Test Automation necessities for modern application development.
The focus is now on the role of AI in QA. The aim of implementing AI is to eliminate test coverage overlap, to comprehensively cover all application usage pathways, and to ultimately move from defect detection to defect prevention. Some of the benefits of AI in testing are:
- Shifting from test coverage to application coverage as testing is done end-to-end
- Reusability of code for different types of testing, including performance, load, security, bulk, and API
- Reduction in the cost of testing
- AI-driven script creation reducing manual effort, time, and error
Will AI Replace Human Intervention in Testing?
The simple answer is NO. While AI is seen as a mechanism to reduce cost, it is also dependent on where the company is in its digital transformation journey. However, it will be nearly impossible to replace human intervention in testing. As AI helps test every aspect of the application, it can be used in building quality code that helps in application coverage that is typically ten times more effective than test coverage. Nonetheless, a quality tester’s role is imperative in understanding the complexity of the application and steering the AI-driven testing process. So, with AI in the mix, a quality tester’s role may evolve into becoming a quality assurance strategist role that leverages AI and ML in quality testing.
Future of QA
With the growing use of AI, it can be inferred that AI will help a functional tester become an AI tester, where the focus is on improving the quality of the code. Incidentally, GAVS implemented AI in QA for an e-commerce platform that helped reduce the release cycle from twenty days to one day! With all aspects of an organization undergoing transformation through the intervention of AI and Automation, QA cannot be left behind.
This blog offers only a high-level gist of the webinar. You can watch the entire discussion, including the poll questions and the experts’ take on audience questions here.
GAVS periodically organizes insightful webinars with GAVS’ tech leaders, the leadership team, and industry thought leaders to explore current and emerging trends. To watch any of our webinar recordings, please visit https://www.gavstech.com/videos/.