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Will AI power the next wave of testing transformation? Here’s what 2,600 DevOps professionals had to say

Test automation

Will AI power the next wave of testing transformation? Here’s what 2,600 DevOps professionals had to say

Year after year, DevOps industry reports and practitioners in the field point to testing as the biggest bottleneck in the software development lifecycle. Take GitLab’s 2021 Global DevSecOps Survey – for the third year in a row, the majority of respondents pointed to testing as the biggest cause of delays.  

That’s a lot of finger-pointing. If we had to guess, testers are tired of it. 

But as businesses become increasingly digitized, the burden on testing will only continue to grow. Research has shown that you can’t succeed at DevOps without a successful test automation practice. But even mature DevOps organizations struggle to scale test automation as quickly as the increase in development scope and speed requires. Testing teams need technology that allows them to optimize their testing processes, giving them time back to focus on strategy while increasing quality and speed.  

There’s hope for testers and their dev and ops counterparts, according to a just-published report. Tricentis and TechStrong surveyed 2,600 DevOps professionals to understand their perspectives on AI-augmented technology, and guess where they’re most excited to apply it? Drumroll please… 

Across all DevOps maturity levels and every global region, respondents overwhelmingly ranked testing as both the most valuable application of AI technology today and the most promising for the future – more than any other stage in the DevOps lifecycle.  

Get the report | AI-Augmented DevOps: The Next Frontier 

Nearly 70% of respondents said this technology could be “extremely” or “very” valuable in testing. When we asked how AI could address specific testing challenges, respondents said it could help them: 

  1. Reduce test case maintenance (49%)  
  2. Focus testing on the highest risk areas (44%) 
  3. Identify the root cause of failed tests (43%) 
  4. Accelerate the creation of automated test cases (37%) 

A powerful antidote to the testing bottleneck 

We also asked what types of testing AI could help them the most with. Functional testing came in first, with 65% of respondents selecting this option, followed by UI, unit, and performance testing. 

This makes sense, as respondents believe AI DevOps can eliminate the burdens of the most time-consuming and labor-intensive tasks. In the comment box, many respondents wrote in that functional regression testing in particular could greatly benefit from AI. And it is indeed a time-consuming task. To really be effective, regression testing needs to be done every single time that a change is made to the existing code – and for organizations shifting to DevOps, that means that what once needed to be completed quarterly, monthly, or bimonthly suddenly needs to be completed daily or hourly.  

Even if your regression suite is mostly automated, there’s still test script maintenance to consider – for a test case library that can quickly grow into the thousands as new functionality gets added. It’s easy to see why respondents were optimistic about technology that could save them time on both test case creation and maintenance, as well as narrow test scopes by focusing testing on what’s really at risk.

These results strongly suggest that as AI adoption grows, testers could catch a break from being the delayed-release scapegoat. A few years from now, fingers could be pointed in an entirely different direction. 

Broader applications across the DevOps pipeline 

While testing ranked first, the respondents to our survey were optimistic about AI-infused DevOps across every stage of the DevOps lifecycle, from planning to monitoring. Almost 90% of respondents said they’re aware of the significant potential benefits of AI-augmented DevOps to mitigate a variety of business and technical challenges. When we asked about which specific challenges were most likely to benefit from AI, here’s how they ranked: 

  • Improving customer experience (48%) 
  • Closing the skills gap of junior employees 47(%) 
  • Reducing costs (45%) 
  • Increasing developer efficiency (43%) 
  • Accelerating innovation (40%) 
  • Increasing code quality (32%) 

Companies at every stage of maturity are primed to begin incorporating AI technology into their software planning, development, deployment, and operations practices. To learn more about how your peers are incorporating this emerging technology into their DevOps practices, read the full report. To hear what our AI experts at Tricentis thought about the findings and what they mean for DevOps teams, register for our upcoming webinar