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 any Tom, Dick, and Harry who wants to develop software. Making coding more accessible and faster sounds like a great thing. ‘Expert’ coding buddies whipping up function, classes, or even whole app in the blink of an eye seems like a dream scenario.
If only that was true.
While AI-generated code speeds things up, it can also turn your codebase into a complete mess. Unfortunately, copy-pasting code and moving on without refactoring or reviewing is becoming more common. This trend has been observed in the analysis of 211 million lines of open-source code, including major projects like VSCode, where copy-pasting has surged while code refactoring has plummeted.
The AI 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 obvious appeal to be able to create applications in minutes, but there are still significant risks when relying on AI-assisted programming tools:
- Code redundancy increases – Developers can often use AI-generated code without understanding or optimizing it.
- Refactoring takes a back seat – Codebases become bloated as engineers add AI-generated code snippets without restructuring existing logic.
- Bugs are hard to spot – Bugs introduced by AI can be difficult to detect because developers trust the generated code to be correct.
- Vulnerabilities are introduced – AI may inadvertently generate insecure code, increasing security risk and exposing applications to cyber threats.
There’s no doubt that AI tools for coding will improve over time with larger context windows, tried and tested prompting, and better training data. But right now, they cannot and should not replace sound engineering principles.
The Case for More Testing, Not Less
Writing software isn’t just about producing code (and tons of it, quickly). It’s about maintaining it, optimizing it, and ensuring it functions as expected in real-world scenarios. Testing has always been a necessary evil, which when given the option to reduce or stop it, it must be appealing. However, there are reasons why testing needs to still happen. Acting as quality guardrails, software testing should not be overlooked. Yes, AI can produce code ridiculously quickly. Some of it more than adequate, but to soley rely on it, with blind faith, is where things can go awry:
AI doesn’t guarantee correctness
AI-generated code might be syntactically correct, but it can remain logically flawed. As a result, you would still need comprehensive testing to catch these subtle errors. This might be fine for a basic app with minimal lines of code, but when you consider multiple integrated systems, the job at hand has just got increasingly more difficult.
According to GitClear’s 2025 AI Code Quality Report, the trend of copy and pasting code doesn’t seem to be slowing down, quite the opposite. Serious concerns are raised about the structural integrity of modern applications if this persists. Adequate testing should always be implemented, regardless of who or what is writing the code, but as refactoring and code improvements decrease, organizations run the risk of deploying software riddled with errors.
Increasing complexity requires more validation
Yes, AI is helping devs produce code at a startling rate, but the influx of new code is making applications and systems more complex and harder to maintain. As reported by GitClear, code blocks are increasingly being duplicated, replicating errors and inconsistencies across integrations. Forgetting about poor code for a second; software is growing in complexity anyway, but now flawed code is exacerbating the situation. Testing is the only reliable way to ensure the new code doesn’t negatively impact regression testing (which can be hard enough) or create bottlenecks, delaying releases even more.
Compliance risks spike with AI code adoption
AI-generated code adds a layer of unpredictability for enterprise applications that must meet strict security and compliance standards. Backing this up are some interesting stats from Google’s 2024 DORA report – for every 25% increase in AI adoption, software delivery stability drops 7.2%. This poses severe risks for regulated industries like healthcare, finance, aerospace and defense. Ignoring rigorous testing is not just risky, it’s a regulatory nightmare waiting to happen.
So, has AI finally destroyed the need for software testing?
Nope. Not by a long shot.
Testing has become even more critical. The only effective way to counter the drop in code quality is through smarter, more intelligent AI-augmented testing.
AI-Augmented Software Testing: A Smarter Approach
Traditional testing already 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.
Keysight Eggplant Test brings AI into the testing process itself—with model-based testing, computer vision, and low-code scriptless design—so you can match the speed and complexity of the software you’re shipping.
Map every user path–without the manual effort
Tired of spending hours trying to predict every way a user might interact with your app? With AI-augmented model-driven testing, you don’t have to. This approach automatically explores your application and uncovers all possible user journeys—including ones you might not even think of. Eggplant Test learns how your software behaves, then dynamically generates the most relevant tests to maximize coverage. It’s like having an intelligent co-tester that never misses a beat—ideal for fast-moving projects and unpredictable AI-generated code.
Test like a real user, no matter the platform
Testing modern UIs—especially ones shaped by AI—can be a nightmare when you’re stuck with rigid, code-based tools. Eggplant Test flips that on its head. It uses computer vision to interact with your app the same way a user would: visually, through the screen. And because it connects through methods like RDP, VNC, and mobile gateways, it can test virtually anything—whether it’s on a locked-down device, legacy system, or remote desktop. So, you can finally get consistent, full-stack test coverage without battling tech limitations.
Let anyone in your team automate with confidence
Automation shouldn’t be limited to the folks who can write code. With scriptless automation, everyone on your team—from testers to product owners—can jump in. Eggplant Test makes it easy with a no-code interface and natural language scripting via SenseTalk. That means faster ramp-up, fewer bottlenecks, and better collaboration across departments. When your whole team can contribute to testing, quality doesn’t just improve—it scales.
AI Won’t Save Quality—But Smart Testing Will
AI isn’t here to fix your bugs or safeguard your release. In fact, with AI-generated code, the risks can multiply—bugs, security gaps, unpredictable behaviors. That’s why testing is more critical than ever.
Eggplant Test helps you stay ahead with intelligent automation that catches issues early, validates constantly evolving code, and keeps quality front and center.
Bottom line: don’t just trust the AI—test it.
To get you on your way, read our popular, The Ultimate AI Testing Playbook today.