AI is making it easier than ever to generate code, but harder to maintain quality, well-engineered software.
So, does AI-generated code actually reduce the need for testing, or does it demand more rigorous validation than ever before? The explosion of AI tools that can generate code has opened the door to anyone eager to develop software.
Making coding more accessible and faster sounds like a great thing. ‘Expert’ coding buddies whipping up functions, classes, or even entire apps in the blink of an eye seems like a dream scenario.
If only that were true, argued Mike Wager, a senior software testing expert at Keysight Technologies.
While AI-generated code speeds things up, it can also turn your codebase into a complete mess, Wager explained.
“Unfortunately, copy-pasting code and moving on without refactoring or reviewing has become all too common.”
According to an analysis of 211 million lines of open-source code, including major projects like VSCode, copy-pasting has surged while code refactoring has plummeted.
Code quality ‘crisis’
More or faster code doesn’t mean better code. Far from it. The output often creates more technical debt and bugs, while making it increasingly harder to maintain. There is an obvious appeal to creating applications in minutes, but there are still significant risks when relying on AI-assisted programming tools.
“AI is undoubtedly speeding up software development, but at what cost? We’re seeing more redundancy, more bugs, and less attention paid to optimising or refactoring code,” said Wager.
“The temptation to generate and move on without a second thought leads to bloated codebases that are harder to maintain and prone to errors.”
Wager highlighted a growing problem where developers, relying on AI-generated code, often fail to understand or optimise what’s being produced.
This trend introduces multiple risks: from bugs that are hard to detect to vulnerabilities that increase security risks.
“AI-generated code can be syntactically correct, but still logically flawed. Comprehensive testing is required to catch these errors,” he noted.
“To blindly trust AI, without validation, is where things go awry.”
– Mike Wager
Writing software isn’t just about producing code quickly; it’s about maintaining it, optimising it, and ensuring it functions as expected in real-world scenarios.
Testing has always been necessary, and when given the option to reduce or skip it, the appeal is understandable. However, Wager stressed that testing should never be overlooked.
“Yes, AI can produce code quickly, and some of it is more than adequate,” he pointed out, “but to blindly trust it, without validation, is where things go awry. AI doesn’t guarantee correctness.”
Wager also warned about the rising complexity in software.
“AI tools are helping developers produce code at an astonishing rate, but that influx of new code is making applications and systems more complex—and harder to maintain.”
He added: “The risk is that more code doesn’t necessarily equal more reliable or higher-quality software. Quite the opposite: it often leads to software that’s more fragile and difficult to test.”
According to GitClear’s 2025 AI Code Quality Report, the trend of copy-pasting code isn’t slowing down. In fact, it’s escalating. This raises serious concerns about the structural integrity of modern applications.
“Without proper testing, organisations run the risk of deploying software riddled with errors,” Wager cautioned.
Compliance risks
AI-generated code adds a layer of unpredictability for enterprise applications that must meet strict security and compliance standards.
Wager pointed to Google’s 2024 DORA report, which reveals that for every 25% increase in AI adoption, software delivery stability drops by 7.2%.
“This is a serious concern for regulated industries like healthcare, finance, aerospace, and defence,” he stressed. “Neglecting rigorous testing isn’t just risky; it’s a regulatory nightmare waiting to happen.”
So, has AI finally destroyed the need for software testing? No, not by a long shot, Wager stated.
“Testing has become even more critical,” he said. “AI tools may be helping developers produce more code faster, but they’re not guaranteeing better code. Smarter, more intelligent AI-augmented testing is the only way to address the drop in code quality.”
Wager pointed out that traditional testing has struggled to keep up with complex, fast-evolving apps, and then AI-generated code came along and raised the stakes even higher.
“It’s unpredictable, constantly changing, and often a black box. That’s why we need smarter testing strategies, he said.
“With AI-generated code, risks can multiply, such as bugs, security gaps, unpredictable behaviors.”
– Mike Wager
Singling out Keysight’s Eggplant Test platform, Wager said it brings AI into the testing process itself with model-based testing, computer vision, and low-code, scriptless design, helping organisations keep pace with the speed and complexity of software.
“AI-augmented model-driven testing automatically explores your application and uncovers all possible user journeys, including those you might not think of. It learns how your software behaves and dynamically generates the most relevant tests to maximise coverage,” he explained.
“It is like having an intelligent co-tester that never misses a beat—ideal for fast-moving projects and unpredictable AI-generated code.”
For testing modern UIs shaped by AI, Wager highlighted the power of computer vision.
“Instead of relying on rigid, code-based tools, Eggplant Test interacts with your app visually, just like a user would. And it connects seamlessly to test anything, whether it’s on a locked-down device, a legacy system, or a remote desktop.”
“AI isn’t here to fix bugs or safeguard a release,” he continued. “In fact, with AI-generated code, the risks can multiply, such as bugs, security gaps, unpredictable behaviors. That’s why testing is more critical than ever.”
“The bottom line is, don’t just trust the AI, test it,” Wager concluded.
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Looking for more news on regulations and compliance requirements driving developments in software quality engineering at financial firms? Visit our dedicated Regulation & Compliance page here.
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