Are we at the edge of a new era in software development—an era driven by Generative AI? Will AI fundamentally change the way software is created? As GenAI begins to generate code autonomously, with no developers in the loop, how will we test all this code?
That's a lot of bold questions, and if I have learned anything about Jason Arbon over the years, bold is an excellent description of him. To that end, Jason suggests a landscape where AI is set to generate 10 times more code at 10 times the speed, with a 100-fold increase in the software that will need to be tested. The truth is, that our traditional human-based testing approaches simply won’t scale to meet this challenge.
Just like human-created code, AI-generated code is not immune to bugs. As GenAI continues to evolve, the sheer volume of code it produces will surpass anything we’ve seen before. Think about it: if AI can generate 10 times more code, that’s not just a productivity boost—it’s a tidal wave of new code that will need to be tested for reliability, functionality, and security. This surge is not just a matter of speed; it’s a "complexity crisis". Modern software systems, like Amazon.com, are far too intricate to be tested by human hands alone. According to Jason, AI-generated code will require AI-based testing. Not just because it’s faster, but because it’s the only solution capable of scaling to match this growth.
The current approach to software testing struggles to keep pace with traditional development cycles. In the future, with the explosion of AI-generated code, human-based testing methods will fall short unless we somehow hire a tenfold increase in software testers (I'm skeptical of that happening). Manual testing will absolutely not be able to keep up, and automated testing as we know it today won’t be able to keep up with the increasing volume and complexity of AI-generated systems.
What’s more, while GenAI can generate unit tests, it can’t test larger, more complex systems. Sure, it can handle individual components, but it stumbles when it comes to testing entire systems, especially those with many interdependencies. Complex applications, like enterprise-level platforms or global e-commerce sites, don’t fit neatly into a context window for GenAI to analyze. This is where Jason says the need for AI-based testing becomes critical.
The future isn’t just about AI generating code—it’s about AI testing that AI-generated code. According to Jason, AI-based testing is the key to addressing the 100X increase in software complexity and volume. Only AI has the ability to scale testing efforts to match the speed and output of Generative AI.
AI-first testing systems should be designed to:
Automate complex testing scenarios that would be impossible for traditional methods to cover efficiently.
Understand and learn from system behaviors, analyzing patterns and predicting potential failures in ways that humans or current automated tools cannot.
Adapt and evolve, much like the AI that generates code, enabling continuous testing in real-time, as software systems grow and change.
As Jason points out, AI is not a fad or a trend, it’s the only way forward. As we move into an era where Generative AI produces vast amounts of code at breakneck speed, AI-based testing will be the way that we help ensure that the software we create tomorrow will be reliable, functional, and secure.
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