Enterprises are increasingly relying on an expansive landscape of applications, and technology processes, regardless of their industry, or domain of operations. Whether we look at a software company where application development is the core business value proposition or a global manufacturer who leverages a proprietary supply chain management system for mission-critical tasks, technology is the backbone of any organization today.
This involves a complicated framework for application design, development, and delivery rapidly changing with every new cycle of modernization and change. Traditionally, the application testing process relied on manual efforts, ensuring that the code in place led to the desired outcome when fed with a specific input/trigger. This is a highly iterative process, with innumerable functions and features to test. Here, the challenge of testing is compounded by the increasingly complex nature of applications — modern software releases are not just about barebones functionalities or basic enterprise integration.
As a result, test automation is becoming an area of interest and rapid investments, as it promises to offload much of the manual dependencies involved in manual testing. By 2025, the test automation market is expected to cross the $100-billion threshold, as enterprises look at partnering with service providers who can streamline testing scenarios.
From siloed processes to AI-led intelligence
The history of testing is as old as the initial onset of the digital era. Testing has a direct impact on company reputation and market positioning — if a customer faces poor functionalities, response time or user experience from an application, the ownership would not be on testers, it is the software company/brand/provider who would come under scrutiny and have to immediately address the lacunae.. For internal applications, testing errors can lead to process gaps, bringing down overall business performance and impacting the quality of experience for the workforce. These were some of the common issues characterizing traditional testing practices or Testing 1.0.
From Testing 1.0, we have moved across second and third-generation advancements, and are fast approaching Testing 4.0, defined by AI and ML. This evolution can be summed up as follows:
- Testing 1.0 – Testing comprised manual, decentralized processes, with a different team/stakeholder taking charge of a specific aspect of the application. User experience was an oft-neglected area, addressed only ‘reactively’ as and when performance issues were detected. Decentralized testing placed a narrow focus on functional requirements, and if they were adequately fulfilled. Release cycles were still marked by protracted timelines and delays, with minimal incremental changes.
- Testing 2.0 – This was the era of agile software development with release cycles rapidly shrinking, thereby, increasing the need for regression tests. This stage witnessed the rise of testing Centers of Excellence, a trend spearheaded by large-scale global organizations whose testing efforts were growing at an unprecedented pace. The focus was on resource optimization – but the human efforts formed the primary driver. Several organizations are still stuck at testing 2.0, given that nearly a quarter of enterprises are automating only 1-10% of their testing efforts.
- Testing 3.0 – This marks a paradigm shift in testing processes, with the introduction of specific models and patterns to guide test processes, factoring in the limitations of human testers. With an expansive ground to cover, automation is beginning to go beyond regression tests, bringing in efficiency gains in other areas like load testing, performance testing, and data-based integrations. Most organizations are looking to automate over half of their testing efforts in the next 12 months, indicating a serious interest in embracing Testing 3.0, as agile application delivery becomes the norm worldwide.
- Testing 4.0 – This is the next epoch in testing evolution, with sophisticated AI and machine learning (ML) taking charge of quality assurance (QA) processes. Today, many organizations have a dedicated QA team where testing responsibilities are divided between QA and developers. This implies that a significant chunk of application development talent is being routed to iterative tasks, without generating value. Testing 4.0 takes this process to an entirely new level, where machines are responsible for programming machines. Test cases would be created by AI techniques, auto-improving over time thanks to the ML technology. Selfhealing is bringing down the maintenance effort drastically. It is autonomous and self testing ecosystem that takes the speed, accuracy and scale to different level. Enterprises will leapfrog into an experiential ecosystem where developers are repositioned as “the guardians of UX,” instead of constantly having to shepherd the delivery process.
It should be noted that the evolution of testing has not taken place in a vacuum — it is a direct outcome as well as a driver of the ongoing digital transformation, placing the user/customer at the center of the application design, development, and delivery framework.
To conclude, we can say that the impact of AI-led testing automation is broader than apparent at first glance. As the world becomes increasingly more digitalized and enterprises as a whole (not just standalone applications) witness repeated cycles of modernization, testing will become an ongoing process and AI will play a vital role, transforming the sheer speed, accuracy, and scale of testing activities. This reassures the need for 360-degree digital assurance that cuts across horizontals and verticals to drive seamless transformation at every level.