The promise of Test Driven Development (or TDD) remains unfulfilled. Like many other forms of aspirational development, the practice has fallen victim to countless buzzword cycles. What if the answer is already in our toolbox? This week, host Andrew Zigler sits down with Animesh Mishra, Senior Solutions Engineer at Diffblue, to unpack the gap between TDD's theoretical appeal and its practical challenges. Animesh draws from his extensive experience to explain how deterministic AI can address the key challenges of building trust in AI for testing. These aren’t LLMs of today, but foundational machine learning models that can evaluate all possible branches of a piece of code to write test coverage for it. Imagine writing two years worth of tests for a legacy codebase… in two hours… with no errors! If you enjoyed this conversation about the gaps between theory and execution in engineering culture, be sure to check out last week's chat with David Mytton about shift left adoption by engineering teams. Check out: Translating DevEx to the Board Beyond the DORA Frameworks Introducing AI-Powered Code Review with gitStream Follow the hosts: Follow Ben Follow Andrew Follow today's guest(s): www.diffblue.com X: diffbluehq LinkedIn: Diffblue Animesh Mishra OFFERS Start Free Trial: Get started with LinearB's AI productivity platform for free. Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era. LEARN ABOUT LINEARB AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production. AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance. AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil. MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.
