Diffblue is recognized as a Representative Provider in this space.
Want your copy of the report? Get it here.
The Modernization Imperative
Technical debt isn’t just slowing innovation—it’s threatening competitive survival. Gartner’s latest research on AI-augmented code modernization tools arrives at a pivotal moment. Software engineering leaders are rushing to solve what previously seemed unsolvable: massive legacy codebases that have resisted modernization for years are now within reach of AI-powered transformation.
According to the research, “44% of enterprises identify the burden of technical debt as the second most common challenge among their top three concerns.” That’s not a future problem. That’s today’s crisis—and for the first time, there’s a realistic path forward.
What Gartner Sees Happening
The strategic planning assumptions in this research are striking. Gartner projects that “By 2029, organizations will complete 90% of software modernization using AI-augmented tools, a significant increase from less than 15% today.”
That’s a massive shift in how modernization work gets done—moving from manual, labor-intensive processes to AI-augmented workflows. For organizations still relying primarily on traditional approaches, the window for competitive advantage is narrowing.
The economic implications are equally significant. Gartner anticipates that “By 2029, GenAI will reduce modernization labor costs by 50% compared with 2025 levels.”
The Testing Imperative in Modernization
What’s particularly relevant for organizations navigating modernization initiatives is Gartner’s emphasis on testing as a critical delivery capability. The research explicitly calls out the ability to “Generate tests” as a core capability of AI-augmented modernization tools, and goes further in the recommendations section, advising leaders to:
“Enhance testing with AI — Deploy AI tools to discover missing test cases, recommend existing ones, and auto-generate unit, integration, or regression tests. Comprehensive AI-driven test coverage is critical for safely refactoring and migrating legacy systems.”
This aligns with what we see in the market every day. Organizations attempting large-scale modernization without adequate test coverage are essentially flying blind. The risk of regression, the uncertainty about actual system behavior, and the inability to safely make changes all stem from the same root cause: insufficient testing.
The Risks Are Real—And Gartner Names Them
One of the most valuable aspects of this research is its honest assessment of risks. Gartner doesn’t shy away from identifying potential pitfalls, including:
“Inaccurate or Hallucinated Transformations: Misinterpreting legacy business logic can produce incorrect refactorings or API signatures. Silent failures — defects or misbehaviors that do not immediately trigger errors — are hazardous.”
This risk category is particularly concerning because it can go undetected until production. The research provides specific examples of how these silent failures manifest—functions returning subtly incorrect results, database migrations dropping columns used in rare reporting scenarios.
The research also identifies “Model Bias, Drift, and Quality Variability” as a technology risk, noting that “Pretrained models can exhibit bias toward specific architectures or technologies, steering teams into suboptimal patterns.”
Our Perspective: Why Deterministic AI Matters
We believe this research validates a fundamental architectural decision Diffblue made years ago: building on reinforcement learning and symbolic execution rather than large language models.
The risks Gartner identifies—hallucinations, quality variability, silent failures—are inherent characteristics of probabilistic AI systems. They’re not bugs to be fixed; they’re features of how LLMs work. For organizations operating in regulated industries, or those where code correctness isn’t optional, these risks create real obstacles to adoption.
Diffblue’s approach generates tests that are mathematically verified to execute the code paths they claim to test. No hallucinations. No invented assertions. No subtle incorrectness that passes code review but fails in production.
When Gartner recommends that organizations “Develop an implementation plan that includes a human in the loop (HiTL) to maintain control, ensure quality, validate complex logic, and retain accountability,” we couldn’t agree more. But we’d add: the degree of human oversight required should be proportional to the reliability of the underlying AI. Deterministic systems require verification; probabilistic systems require vigilance.
The Modernization Use Cases That Matter
Gartner’s research spans the full modernization lifecycle—discover, design, and deliver phases. For testing specifically, the research highlights uses including:
“AI-generated unit, integration and regression test suites. Synthetic test-data creation to cover edge cases and compliance scenarios. Continuous anomaly detection for behavioral regressions.”
These capabilities directly address the challenges we see organizations facing:
- Inherited codebases with zero test coverage after M&A activity
- Compliance mandates requiring documented test coverage
- Legacy systems that teams are afraid to modify without safety nets
- Velocity crises where deployment is blocked by coverage gates
The research’s recommendation to “Run pilot projects — Begin with a representative, low-risk application to validate toolchain integration, measure productivity gains, and refine processes” reflects the reality that modernization is a journey, not an event.
What This Means for Your Modernization Strategy
If your organization is facing any of these scenarios, this research provides valuable context:
You’re dealing with an inherited codebase. Post-merger technical debt with zero test coverage creates existential risk. Understanding how AI tools can rapidly characterize and secure these systems is essential.
You have compliance requirements. Coverage gates aren’t negotiable when auditors come calling. Knowing which AI approaches deliver auditable, deterministic results matters.
Your velocity is suffering. When deployments are blocked and sprint capacity is consumed by testing debt, the cost of inaction compounds daily.
You’re planning a major migration. Cloud transformations, platform migrations, and framework upgrades all require confidence that changes won’t break existing functionality.
Get the Full Research
This blog captures our perspective on Gartner’s research, but the full document contains significantly more detail on capabilities, risks, recommendations, and representative providers, including Diffblue.
We’re offering complimentary access to the complete Gartner research on AI-augmented code modernization tools. If you’re evaluating your modernization strategy, considering AI-powered testing tools, or simply want to understand where this market is heading, this research provides the strategic context you need.
Access the full Gartner research →
And if you’re ready to see how deterministic AI test generation works on your own codebase, Diffblue offers a free trial. No hallucinations. No surprises. Just tests that work.
Gartner, Innovation Insight for AI-Augmented Code Modernization Tools, Arun Batchu, Oleksandr Matvitskyy, Mark O’Neill, Tigran Egiazarov, 17 July 2025
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
