Diffblue

How does Diffblue Testing Agent compare against Claude Code? We put it to the test. Built on the principle of 'Bring Your Own Agent', Diffblue Testing Agent is not another coding tool - it's an orchestration and verification layer that turns your existing AI investment into a managed testing workforce — one that operates at project scale, without developer intervention, and delivers output that is ready to push to your repository. It's designed to orchestrate any coding agent’s behavior to deliver maximum test coverage without developer intervention.... We put it to the test against against Claude Code alone and the results were pretty 🔥🔥🔥 🔗 Access the full report via link in comments section 👇 #DiffblueTestingAgent #ClaudeCode #AgenticWorkflows #MutationTesting #TestCoverage #Modernization

🚨🚨🚨 NEW RESEARCH 🚨🚨🚨 What happens when you compare autonomous AI to a senior developer using AI tools? We ran the benchmark. Across 8 enterprise Java repositories, Diffblue Testing Agent delivered significantly higher test coverage — in a single autonomous run — with zero supervision. Meanwhile, developer-led workflows required constant prompting, rework, and oversight. The takeaway? AI assistance ≠ AI automation. See the numbers for yourself. Read the full full report - 🔗 👇 #DiffblueTestingAgent #AgenticWorkflows #BenchmarkData #AutonomousAI

Most AI-generated tests look fine at first glance. Until you try to run them. They don’t compile. They don’t pass. They don’t improve coverage in any meaningful way. And suddenly, developers are stuck “babysitting” the AI — fixing the very work it was supposed to automate. Diffblue Testing Agent takes a different approach: 🟦 Every test is verified before it reaches your repository. 🟦 If it doesn’t compile → it’s fixed or excluded. 🟦 If it doesn’t pass → it’s fixed or excluded. What you get is production-ready output — not a starting point for more work. That’s the difference between AI assistance… and AI you can actually rely on. #AI #Testing #DevTools #Automation #DiffblueTestingAgent #AgenticWorkflows

Why does AI test generation break down at enterprise scale? After working with Fortune 500 teams, we’ve seen the same five problems again and again: 1. AI coding tools can’t operate across entire codebases 2. Generated tests don’t actually work 3. Output quality varies wildly between developers 4. Teams get locked into a single AI vendor 5. Building in-house customisations costs more than the problem The result? A lot of experimentation… and very little production impact. Diffblue Testing Agent was built to solve these challenges directly — with autonomous execution, built-in verification, and consistent engineering guardrails. Because enterprise AI needs more than good demos. It needs reliability. Peter Schrammel, CTO, shares more in his latest post 👉 https://lnkd.in/eKufNDdy #AgenticAI #EnterpriseAI #SoftwareEngineering #Testing #AI

Today, we’re launching the Diffblue Testing Agent 🚀 The first in a new generation of purpose-built AI agents for enterprise software engineering. AI coding tools like GitHub Copilot and Claude Code have transformed how developers work — but a critical gap remains. Most enterprise codebases still lack the test coverage needed to move fast safely. And while AI can help write code, it hasn’t been able to autonomously generate reliable, scalable regression tests across entire systems… until now. Diffblue Testing Agent changes that. It works with your existing AI coding platform to: ✔️ Autonomously generate high quality unit test coverage across entire codebases ✔️ Use tokens more efficiently while avoiding “model lock-in” ✔️Enforce consistent, enterprise-grade quality — no one-off prompt engineering required ✔️ Operate at scale, without developer intervention This isn’t another coding assistant or IDE plugin. It’s an orchestration and verification layer that turns your AI tools into a managed testing workforce — operating across thousands of files with predictable, production-ready output. In our benchmarks, Diffblue Testing Agent achieved 81% average test coverage across enterprise Java repositories — compared to 32% using AI tools alone. Testing is just the start. This is the foundation for a new class of AI agents: purpose-built, reliable, and designed to handle the high-volume, verifiable tasks that generic AI coding assistants can’t. 🔗 👇 - for blog and news! #AI #AgenticWorkflows #SoftwareEngineering #DevTools #Automation #Testing #EnterpriseAI

"One very important corollary to creating tests using Diffblue Cover is that the tests will not require code review." Our latest technical article explains why: Every test is executed and validated through reinforcement learning before acceptance. They document actual, verified behavior—not assumptions or intentions. From the article: "Cover uses the information in the code and from execution to identify inputs and structures" "The tests will have been chosen to maximise code and branch coverage" "All tests produced by Cover will compile and pass" The out-of-the-box approach means: ✅ No manual tuning needed ✅ No expert intervention required ✅ Fast untended coverage uplift ✅ Immediate regression safety net Discover how reinforcement learning transforms test generation from creative exercise to systematic process. https://hubs.ly/Q042T6yf0

"44% of enterprises identify the burden of technical debt as the second most common challenge among their top three concerns." That's from Gartner's new research on AI-augmented code modernization tools. Here is what is different today: the problems that seemed unsolvable are becoming solvable. These challenges that have blocked modernization for years now have AI-powered solutions. But the research is also refreshingly honest about risks. Gartner specifically calls out: "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 is why the choice of AI approach matters. Diffblue is included as a Representative Provider in this research. We're offering complimentary access to the full document. Link in comments. #EnterpriseAI #Modernization #SoftwareDevelopment #TechDebt #QualityEngineering

This week we hit a milestone that felt worth pausing for: a decade of building something that genuinely matters in software development. . 🎉 What began as pioneering research at the University of Oxford has evolved into a company trusted by some of the world's largest enterprises to secure their most critical Java applications. Our deterministic AI technology has helped engineering teams achieve comprehensive test coverage, enabling safer releases and faster development cycles. Yesterday the team came together to celebrate. Not just the anniversary, but everything it represents: the hard problems we've solved, the customers who trusted us with their most complex codebases, and the engineers who keep pushing what's possible with AI.🤝 We're grateful to everyone who has been part of this journey.

As 2025 draws to a close, the Diffblue team wants to thank our customers, partners, and community for an incredible year. To everyone navigating legacy codebases, chasing coverage targets, and shipping with confidence: we see you. Here's to a restful break and an even stronger 2026. Happy holidays from all of us at Diffblue. 🎄

2025 was the year AI grew up. As the conversation shifted from whether to use AI to modernize legacy systems to how to do it with the confidence that mission-critical code demands, Diffblue hit its stride. Our CEO shares his reflections on industry recognition, new partnerships, platform evolution, and what's next for AI-powered testing at scale. Link to full post here: https://lnkd.in/dFjkZMmE #AI #SoftwareTesting #LegacyModernization #2025wrapped