When you ask most software testers or QA managers about the real impact of AI today, many are saying they haven’t seen a significant impact yet.
No matter how big the buzz gets around generative AI, we’re still in the very early stages of adoption in the software quality/software testing industry. There has been a lot of noise created around the promise of AI in testing, and you should expect that noise to grow louder. However, when you ask most software testers or QA managers about the real impact of AI today, many are saying they haven’t seen a significant impact yet – at least not in the form of dollars and cents.
And, amidst all the buzz and excitement that many feel about the potential for generative AI in our industry, big uncertainties and anxieties continue to loom. It seems like only yesterday that we finally put the debate to rest around, “No, test automation will not replace every manual tester’s job,” and now we’re already mired in debating whether AI is going to take all of our jobs, including those held by automation engineers who previously felt irreplaceable.
It’s clear that testers’ reception of AI-driven changes in test automation tooling and processes will continue to reflect both skepticism and promise for the foreseeable future. Let’s delve deeper into the source of this doubt.
Good reasons for skepticism…and concern
There’s valid skepticism of AI stemming from two notable concerns. First, testers have been promised “revolutionary” AI solutions that will solve all their problems, and autonomously complete all of their testing with only the push of a button. Sound familiar? Many vendors have made the same claims about automated testing for more than a decade. Yet, many of these promises haven’t quite delivered, leaving practitioners understandably doubtful. Often, the reality of AI in testing hasn’t matched the hype, making it challenging to see its practical value in everyday testing scenarios. AI will never be a silver bullet solution for all testing challenges, and every testing and QA team knows this.
Second, it’s not hard to understand why many testers today are apprehensive about AI and the potential threat it poses to their job. Manual testers have been down this road before, but now automation engineers are also seeing demos, reading research, and hearing the rumors of AI completing tasks at unprecedented speeds that could render “technical expertise” redundant. It’s unsettling for anyone to feel like their relevance is in jeopardy. And, while the reality is that generative AI is going to disrupt many industries, there’s another reality that bears stating and repeating until it’s accepted by all as just as true: AI should be seen as a tool that significantly enhances the tester’s role rather than replaces it.
Generative AI’s best use case is not to “replace” testers
Generative AI, not unlike test automation in many regards, is a powerful tool to be leveraged by testers. It has the ability to significantly accelerate the completion of tasks that provide no extra value or ROI when performed by a human, so that humans have more time (or, in some cases, any time at all) to do the tasks that are best suited for humans and are performed worse by machines. Lastly, the most innovative solutions to complex problems in testing will always intersect at the intelligent use of modern technologies and the indispensable human touch.
As both these notions swirl, let’s talk about where exactly AI is having a positive impact and delivering immediate value for testers right now.
Test automation: A strong AI candidate
The increasing demands placed on development and testing teams to deliver high quality software at unprecedented speeds continue to fuel a drive toward identifying areas for workflow improvement. Test automation plays a pivotal role in this pursuit.
Testing broadly is highly pattern-based at scale. Once you identify a set of regression test cases, you perform the same actions over and over. Any pattern-based work where you are executing repetitive actions consistently and can be performed by a robot is a natural fit for AI.
Imagine a test case written in natural language within Jira, where many testers spend their time. With a simple click, AI can fix grammar (think, Grammarly for testing), make the test case legible, and break it into manageable steps, enabling new testers who have never seen your application or test case before to execute it. After review and any needed adjustments, the test can be automated with another click, removing the need for scripting or recording. You’ve eliminated the task of creating an automation test! Even better, all this works seamlessly within a tester’s existing workflow, and there is no need to learn new processes or tools. This realization was my own personal “a-ha” moment of seeing AI’s potential in testing, and exploring where it can further assist (not replace!) the talented, valued, users I speak with all the time and strive to support.
Another notable advancement for AI in testing is in reducing test flakiness, effectively addressing the instability of automated tests when there’s changes in the application under test or environment. AI is helping ensure that tests remain intact and effective in these scenarios. Through “self-healing” tests, there are teams today maintaining large test sets effectively, even as software systems undergo continuous, iterative development and updates.
Additionally, AI-powered visual testing allows testers to quickly compare images and identify discrepancies following code changes. With AI, testers can automate this manual, time-consuming task, and eliminate issues that are easy to go unnoticed when only a human eye is hunting for them.
While testers are beginning to utilize these AI-powered capabilities, no company can claim to have legitimately “perfected” an AI solution. Despite this, testers are experiencing incremental benefits, as AI accelerates the execution of tedious tasks, highlighting its increasing role in expediting workflows and driving efficiency. While “larger” problems across the software development lifecycle (SDLC) might not be magically solved through AI, cutting down the time it takes to complete repetitive, often mundane tasks is certainly a noteworthy step forward in the right direction.
So, what’s next?
While nobody (outside of the companies building them) knows “what’s next?” when it comes to how these tools will evolve from a capability standpoint, there is an emerging sentiment in the testing industry that is gaining steam. Testers and other QA professionals who see this uncertainty as a great time to evolve alongside AI will be rewarded for doing so. Testers can grow skill sets in areas immune to AI disruption. Areas like:
- Understanding user behavior within their domain
- Maintaining a focus on customers’ problems and needs
- Embracing exploratory testing and critical thinking
- Developing strengths in adjacent areas like UI design
- Fostering seamless collaboration with development and product teams
- Initiating conversations with engineering, product teams, customer support, and other business leaders
AI’s inability to fully comprehend user needs and foster cross-functional alignment aren’t knocks against it. They’re simply areas that AI, or any other automation engine, weren’t designed to do. And again, just like we know with test automation, understanding which tasks aren’t suited for automation is just as important as knowing which tasks are.
Balancing the nuanced interplay between AI-driven efficiency gains and the human touch necessary for comprehensive understanding and collaboration will likely always be an important challenge worth solving, but it’s also a tremendous opportunity for those who view it as such.
The AI journey is just beginning
We’re seeing firsthand how testers are identifying new opportunities to redirect their focus toward areas they’ve wanted to spend more time on for years. This renewed focus may also help testers show heightened contributions to the rest of the business. Going forward, clear communication with and streamlined collaboration between testers and product and engineering teams will be paramount. I also believe that it will be essential for testers to learn how to use AI tools effectively as the efficiencies they provide will often be too compelling to ignore.
https://www.devprojournal.com/software-development-trends/software-testing/lets-be-honest-where-is-ai-impacting-software-testing/a>