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Generative AI in Software Testing 

What is generative AI in software testing? 

Generative AI in software testing is an advanced approach that augments human testers to make the testing process faster and more efficient while improving the quality of the software test results. 

To better understand the concept of AI and testing, let’s talk analogies. What else comes to mind when comparing the idea of generative AI to other advances in human performance?

Did you say, Cher? Amazing, because that’s what I was thinking too.

In 1998, Cher leveraged a novel way to alter her voice in her song “Believe.” She used auto-tune technology, and Lil Wayne and many others soon followed. Auto-tune is an excellent analogy for how generative AI enhances the work of software testers. As auto-tune leverages technology to measure and improve pitch in vocal recording, generative AI in quality assurance enhances productivity, speed, and accuracy. 

Apple Image of Garage Band with Tim Ryan singing Cher's 1998 hit
Using Apple’s GarageBand voice filter to enhance an already silky smooth voice.

Taking the analogy a step further, think about other ways technology augments our ability to improve performance. Examples include Power Steering, the Jackhammer, Microsoft Excel, and Google Docs.

Or, take Grammarly’s editing tool. It identifies issues and helps improve writing accuracy and editing speed. Kind of like “AI QA” for writing.

Adobe image revealing generative AI used in Photoshop.
Image: Adobe

How does generative AI improve software testing efficiencies?   

Analysts estimate software testing costs companies ~$45B per year, with spending expected to grow 5% annually. With new AI testing tools, companies can expand their software testing coverage and gain even more confidence in the quality of their product releases. 

Generative AI Testing Tools

Future Of Generative AI in Testing

When it comes to the future, Testlio is backing Generative AI tools since Testlio’s initial experiments showed that: 

  • Testing managers using generative AI-assisted tools could refactor test cases 15-30% faster than traditional methods.
  • Bug reports created by skilled QA testers using generative AI tools exhibited a 40%+ decrease in errors compared to reports generated through traditional methods.

The impact of these findings is significant for the software testing industry and a catalyst for change in software testing services.