First post of a four-part series. Download the full eGuide here.
Introduction
For the first time in decades, we are at a technical inflection point for the discipline of software quality assurance. Beyond the application of AI and generative AI for the creation and maintenance of functional tests, AI First Software Quality Platforms will radically change the day-to-day tasks of software testers.
Let’s Face It – Things Will Change
It’s quite amazing how infrequently we have seen sea changes in software testing. Sure, there have been ebbs and flows of outsourcing and insourcing as well as waves of decentralization and centralization both within the testing teams and in combination with development teams. However, as AI First testing platforms have been able to deliver on the promise of significantly greater autonomous automation, we have never been faced with a greater opportunity to advance the mission of delivering significantly better software.
As with any inflection point with the evolution of technology, your organization’s results will vary. Why? Because there is one universal constant – a tool alone will not magically manifest change. Just as driving a Tesla does not automatically make you a better driver.
The Successful Transition to an AI First Testing Platform
Let’s highlight three things that your team must understand to evolve.
- AI offers transformation only if you let it
- Success can’t be measured with old process metrics
- Map your evolution based on milestones and trust
AI Offers Transformation Only if You Let It
What gets in the way of successfully adopting an AI First testing platform are the expectations that you can keep the same processes and tasks and expect an AI testing tool to make everything better.
Today testing teams are predominately validating bottom-up user stories. This acute focus on validating bottom-up requirements gets exacerbated by the time-boxes introduced by agile or more iterative development methodologies. This leads to the mentality of “we are so busy that we cannot change.”
An AI First testing platform distinctly shifts the bottom-up tasks associated with code changes with a top-down approach focused on safeguarding the integrity of the user experience while protecting the business from the potential impacts of application failures.
The organization must manage this fundamental shift as more responsibility for user story validation will shift to developers. This will allow testing teams to focus more holistically on assessing the impact of change versus overarching business goals.
AI Success Can’t be Measured with Old Process Metrics
In terms of software, a business risk is any application shortcoming that impairs the end user’s (or customer’s) expected experience and ultimately erodes confidence in the business. A software business risk can manifest itself as a headline news event such as a reservation system outage that strands holiday travelers—damaging brand equity. Or it could be a series of user-experience hiccups that eventually drive customers to a competitor—directly impacting revenue or a subscriber base.
Why do we mention this? Because our efforts associated with software testing today do not directly address business risk. Our efforts today are focused on validating assumptive text isolated as a user story. Think about the number of times a reported defect is rejected because the development team does not believe the user story is violated. Additionally, it has been reported that only 18% of defects reported by testers get addressed.
A shift to an AI First testing platform opens the opportunity to have a direct business impact. It allows the organization to express its objectives and metrics in direct alignment with the goals of the business. Furthermore, it forces a fundamental shift to align testing with business risk.
Embracing AI First Software Quality Platforms marks a pivotal moment in the evolution of software testing. As organizations transition to these advanced systems, it’s crucial to understand that success requires more than adopting new tools—it demands a comprehensive shift in mindset and processes. By recognizing the transformative potential of AI, redefining success metrics to focus on business risk, and strategically mapping milestones, teams can unlock unparalleled improvements in software quality. The future of software testing lies in leveraging AI to not only enhance testing efficiency but also to ensure that software truly aligns with and supports overarching business goals.
First post of a four-part series. Download the full eGuide here.
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