Matthew-Hope Eland
Wizard at Leading EDJE
About
An AI Specialist and Wizard at Leading EDJE who is known to teach software engineering, AI, and data science concepts in the most ridiculous ways possible. Matt has used machine learning to settle debates over whether Die Hard is a Christmas movie, reinforcement learning to drive the behavior of digital squirrels, data analytics to suggest improvements to his favorite TV show, and AI agents to play board games and create an AI agent with the personality of a dog. Matt is the author of "Data Science in .NET with Polyglot Notebooks" and "Refactoring with C#" as well as several LinkedIn Learning courses. Matt helps organize the Central Ohio .NET Developer Group, runs several blogs and a YouTube channel, has a Master’s of Science in Data Analytics, and is a current Microsoft MVP in AI and .NET.
Ensuring Software Quality in the world of AI Developers
Description
Like it or not, AI agents can now turn a loosely written paragraph of requirements into a pull request that looks production-ready in minutes. That’s impressive — and horrifying. When code is being generated faster than humans can fully internalize it, QA becomes the last line of defense between “seems fine” and a 2 a.m. incident caused by a misunderstood requirement or a bad database migration. In this session, we’ll explore how quality practices must evolve in a world where teams treat AI agents like new junior developers. We’ll talk about strengthening test plans so they validate intent instead of just implementation, expanding automated coverage to catch AI-specific failure modes, and partnering closely with developers whose familiarity with the generated code may be thinner than in years past. We’ll look at redefining code and feature review processes, improving requirement clarity to reduce ambiguity before it becomes defects, documenting our new vibe coded enterprise systems, and adding guardrails so AI-authored changes can’t slip past quality gates unchecked. By the end, you’ll have a clear understanding of the new risks AI introduces — and practical strategies to help your team move fast without letting AI-generated pull requests quietly YOLO their way into prod.