The BugBash Podcast

Large Language Models can generate code in a flash, but that code is notoriously unreliable. Traditional unit tests often can’t put enough guardrails in place to ensure correctness… even if they’re written by the LLM itself.
This is where property-based testing (PBT) becomes essential.
Today, we're joined by David R. MacIver, creator of the PBT library Hypothesis, and now an Antithesis employee! We discuss how to build robust feedback loops that are needed to make AI-generated code trustworthy.
We'll cover why standard AI coding benchmarks are flawed, how Hypothesis makes PBT approachable, and the challenge of getting developers to think in "invariants." David also shares his perspective on the future of AI in software engineering.
If you want to build a reliability backstop for your code, vibed or otherwise, stick around.

Software testing research is exploding, but in practice, most companies' testing approaches seem stuck in the past. Where does that gap come from?
It often boils down to the distance between academic promises and the practical needs of developers who need usable tools and fast results.
In this episode, David talks with Rohan Padhye, head of the PASTA research group at Carnegie Mellon University, who has lived on both sides of that divide. They explore how fuzz testing crossed that chasm—from industry curiosity to academic focus and back again—and what it will take for other techniques to do the same.
Rohan shares insights on designing testable software, building a robust testing culture, and what truly makes a "good" property for finding bugs.

Integrating non-deterministic, non-durable elements like AI agents into our workflows tends to lead to a lot of do-overs. But restarting AI processes can be costly, burning through tokens and losing valuable progress. Wouldn’t it be easier if there was always a clear checkpoint to restart a task from?

Today I talk with Qian Li, co-founder of the DBOS durable execution engine, about reliability, ergonomics, and actually understanding your software. We discuss the long history of checkpointing, mental models, and how using durable execution allows systems to resume right where they left off after a crash. It makes your software resilient by default.

Learn how this architectural pattern can impact an AI-assisted or any complex system that could use a little improvement in how developers work with it.

How do you test for bugs that only appear when a user clicks frantically, or when asynchronous data loads in an unexpected order? Standard UI tests often miss the subtle stuff that happens all the time in the stateful, dynamic applications.
In this episode, Paul Ryan and I sit down with Oskar Wickström, creator of the QuickStrom framework, among other things, to explore how to apply generative testing to the complex world of user interfaces. 
Oskar argues that you don't need to be a formal methods genius to get real value out of the approach. Even simple properties can uncover deep bugs, like ensuring a loading spinner eventually disappears or that the screen never goes blank. 
If you've been intrigued by property-based testing but intimidated by the thought of writing complex formal models for UIs, stick around.

AI coding assistants promise incredible speed, but what happens when you run straight into a wall of buggy code and technical debt?
In this episode, Clare Sudbery, a software engineer with over 25 years of experience, discusses a crucial paradox for modern developers. 
The secret to harnessing AI's power isn't to move faster, but to slow down. Clare explains why deliberate, rigorous practices like Test-Driven Development (TDD) are the essential "guardrails" needed to guide AI tools toward reliable, high-quality software. You'll learn why "more, smaller steps" is the key to tackling technical debt and how throwing your code away might be the most productive thing you do all week.