#AI has changed how almost everybody codes. But how useful are LLM-based AI assistants if the majority of the code generated needs human oversight, review and correction? We decided to put GitHub Copilot (Coding assistant) and Diffblue Cover (AI Agent) comparatively through their paces to see which is quicker, more accurate and reliable when testing Java code. 🔥The results are compelling. Find out what we did and read the report: 🔗diffblue.com/resources/diffb… #AI #AIforCode #GitHubCopilot #Java #CodeQuality #AIAgents #AIAssistants

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. 🎄

Just published: "Uplifting Test Coverage Out of the Box with Diffblue Cover" The article reveals why traditional approaches to test coverage fail at enterprise scale and how determinism changes everything. Key insight from our Head of Engineering, Thomas Given-Wilson : "Every test Diffblue Cover produces will compile and pass. There are no hallucinations, no failing tests, no manual fixes required. The reinforcement learning loop validates each test through actual execution before accepting it." Diffblue analyzes bytecode, not source code, discovering actual behavior rather than intended behavior. Read how you can achieve substantial coverage increases out-of-the-box, without configuration or manual intervention. hubs.ly/Q03XZ1km0 #EnterpriseJava #SoftwareEngineering #java #UnitTesting

GitHub Chief Product Officer Mario Rodriguez, speaking at Microsoft Ignite, shared how custom agents are reshaping developer workflows and demonstrated Diffblue Cover as an example of purpose-built AI doing the heavy lifting.

Diffblue is now a GitHub Copilot launch partner. GitHub just announced custom agents for Copilot and they selected Diffblue alongside HashiCorp, Dynatrace, JFrog, MongoDB, and PagerDuty to bring specialized AI capabilities directly into the developer workflow. What this means for enterprise Java testing: - Native distribution across GitHub's 100M developer ecosystem - Integrated into VS Code, CLI, and hubs.ly/Q03WV5yj0 - The deterministic alternative to probabilistic unit test generation methods As AI coding assistants become standard, the need for reliable, verifiable test automation only grows. We're excited to be part of this ecosystem. hubs.ly/Q03WV8mX0

Struggling to reliably test static methods in Java? Mockito’s static mocking capabilities (introduced in 3.4.0 ) finally make it possible to isolate static calls, control nondeterministic behavior, and build cleaner, more deterministic tests. This guide walks through best practices, real-world patterns, and pitfalls to avoid. ⬇️ hubs.ly/Q03WjLzT0 #java #mockito #UnitTesting #CleanCode

Join us next week to see how Diffblue’s next-generation unit test platform makes enterprise test coverage systematic, autonomous, and actually achievable. This latest release includes: Guided Coverage Improvement: transform output codes into action plans with "dcover issues" Test Asset Insights: learns from your existing tests and patterns LLM-Augmented Intelligence: leverage your approved LLMs without new security reviews Building test coverage today means babysitting AI assistants and plateauing at 40%. Diffblue fixes that helping you achieve your coverage gates autonomously. 🚀 Register for Tuesday's demo: hubs.ly/Q03VXB0R0 📊 Get the benchmark study: hubs.ly/Q03VXFNN0 #TestAutomation #DevOps #EnterpriseJava #SoftwareTesting #AITesting

What's blocking AI from transforming enterprise software development? It's trust. When AI coding assistants hover at 50-65% accuracy, developers spend more time verifying suggestions than actually coding. In this article, we explore why professional developers distrust AI tool accuracy, examine why current LLM solutions remain insufficient for enterprise needs, and reveal what it takes to achieve the 95% accuracy threshold needed for genuine productivity gains. Read more: hubs.ly/Q03T0PT50 #AICodeGeneration #SoftwareDevelopment #CodeQuality #DeveloperProductivity #EnterpriseAI #JavaTesting

Why stay stuck at 40% coverage? 🎯 In case you missed it, we just launched Guided Coverage Improvement, transforming how enterprise teams break through coverage plateaus! Before: Hundreds of cryptic output codes (R013, E115, R026...) leaving developers asking "Where do I even start?" After: Intelligent issue aggregation that groups related problems, prioritizes by coverage impact, and delivers ready-to-execute prompts for your existing AI assistant: hubs.ly/Q03SB4dh0

Diffblue Cover now addresses more of the key pain points Java enterprises face in unit test generation through three integrated capabilities.! Achieve your coverage goals in 90 days, leverage your existing test infrastructure, and work seamlessly with your approved enterprise LLMs. All with 20x productivity advantage over AI coding assistants: 👉 See how it all works together: hubs.ly/Q03R-zn50 #SoftwareEngineering #java #UnitTesting

We benchmark every major LLM against our autonomous agent. Why? Because understanding architectural limits matters more than following hype cycles. But we absolutely respect what LLMs do well. They excel at understanding intent, suggesting patterns, adapting to coding styles. Recently, our benchmarks exposed what enterprises are learning the hard way: on closed-source code, even Claude's impressive 83% open-source coverage drops to 49%. Qodo generates 17.6× fewer tests in the same timeframe. Even Copilot with GPT-5 tops out at 74% coverage while requiring constant developer oversight. LLMs shine at creative problem-solving and contextual understanding. Deterministic agents excel at systematic execution and guaranteed results. The results? Tests that compile 100% of the time versus 58-88%. Coverage that scales up to 29× better annually. Unattended execution for hours versus continuous prompting. Sometimes the best AI strategy is knowing which tool fits which problem. Read the complete analysis: hubs.ly/Q03QZxgY0 #SoftwareEngineering #EnterpriseJava

Unit tests check the logic. Integration tests check the connections. Both are important. Knowing when to use each one helps you build more reliable Java applications. Explore tools, examples, and testing tips: diffblue.com/resources/unit-… #Java #UnitTesting #IntegrationTesting #DevTools #DiffblueCover

Need more control over your Java test generation? Cover Annotations in Diffblue Cover let you guide mocking, inputs, and factory methods. Get smarter, more relevant tests with less manual effort. See how it works: diffblue.com/resources/diffb… #Java #UnitTesting #AI #DevTools #DiffblueCover

A US Aerospace & Defense company used Diffblue Cover to modernize a 35-year-old Java app. 550k lines of code covered by unit tests in 1 month. Estimated 2.5 years of manual effort saved. Smarter coverage. No extra headcount. On-time delivery. Full case study: diffblue.com/case-studies/us… #AI #Java #UnitTesting #DevTools #DiffblueCover #ApplicationModernization

Reinforcement learning isn't just for games or chatbots. At Diffblue, it's how our AI learns to write Java unit tests. No labels. No prompts. Just feedback loops that improve coverage and catch regressions. xAI's Grok 4 shows where RL is going. We're already putting it to work. #AI #ReinforcementLearning #Java #UnitTesting #DiffblueCover

Test writing is complex. The space is huge, feedback is fuzzy, and quality is hard to measure. At Diffblue, we use Reinforcement Learning to solve it. The JVM is our environment. Coverage is our reward. And our AI learns to write better Java tests—automatically. 👉 diffblue.com/developer-produ… #AI #ReinforcementLearning #Java #DevOps #UnitTesting

Struggling with unmaintainable legacy Java code? Agentic AI could be your answer. Discover how Diffblue’s agentic AI automatically creates maintainable unit tests, simplifying complex codebases: diffblue.com/resources/tamin… #LegacyJava #AgenticAI #Java #SoftwareEngineering

Great code quality starts at day one, not at deployment. See how Diffblue Cover makes continuous testing seamless, efficient, and effective: diffblue.com/resources/code-… #Java #ContinuousTesting #CI #CodeQuality

Choosing the right Java unit test generator can significantly impact your dev team's productivity and software quality. In our latest blog, we outline the top 12 automated unit testing tools to help you decide which is the best fit for your Java projects. Get insights on coverage, maintainability, and ease of use: 👉 diffblue.com/resources/java-… #Java #UnitTesting #SoftwareTesting #DevTools

Traditional coding couldn't solve image recognition. AI did—by learning patterns, not hardcoding rules. We do the same for Java testing. Diffblue Cover uses AI to understand your code and write unit tests automatically. No scripts. No maintenance. Just results. Learn more at diffblue.com/ #AI #ReinforcementLearning #Java #UnitTesting #DeveloperProductivity #DevOps

CI in theory? Write code ➡️ run tests ➡️ catch bugs early ➡️ deploy with confidence. The DevOps dream. CI in reality? Too few unit tests early in the pipeline = bugs found late = time lost. Diffblue helps fix that. Our AI writes Java unit tests automatically, giving you fast, reliable test coverage right from the first commit. Request a demo to boost your CI pipeline today: diffblue.com/schedule-a-demo…