Old Meets New: Bringing Crash Analysis into 2025
Let’s face it – while the rest of software development has evolved at warp speed, crash dump analysis feels like it’s been preserved in digital amber for decades. We’ve got self-driving cars and pocket-sized supercomputers, yet here we are, still pecking away at command prompts like it’s the dawn of the internet. Why is debugging the only area where we cling to tools that look like they belong in a computer history museum?
Picture this: You, a professional software engineer in 2025, hunched over a terminal, manually typing arcane commands like !analyze -v and .ecxr, squinting at hexadecimal memory addresses, and mentally translating stack traces. All while your friends in other industries are delegating their work to AI assistants that can write entire documents, create art, or automate complex workflows.
Something’s wrong with this picture, right?
What if I told you we can throw that ancient workflow into the dustbin of computing history? That’s exactly what I’ve done. And I’m not talking about slightly better syntax highlighting or prettier UI for WinDBG. I’m talking about a fundamental transformation where you simply have a conversation with your debugger.
When Inspiration Strikes
During a debugging session at work, I had one of those lightning bolt moments. What if – and stick with me here – we could apply the same AI-assisted “vibe coding” approach to crash dump analysis?
Picture this: instead of manually slogging through memory dumps and command outputs, you simply ask, “Hey, why did this application crash?” and get an intelligent, contextual answer that actually helps you solve the problem.
The idea was too compelling not to pursue. So I built it.
See It In Action: AI-Powered Crash Analysis
Before diving into the technical details, let me show you what this looks like in practice. I have prepared a demo application to showcase two different use cases:
Video 1: Crash Analysis and Automated Bugfix
In this video, I show how Copilot can analyze a crash dump, identify the bug and auto-fix the issue.
As you can see in the video, instead of manually running WinDBG commands and interpreting the cryptic output, I’m having a natural conversation with GitHub Copilot. The AI quickly identifies that the application crashed, explains which specific conditions led to the crash, and suggests a fix.
Video 2: Automated Crash Dump Analysis of multiple crash dump files
This video demonstrates a different capability: analyzing multiple crash dump files at once. It shows how the tool can quickly identify which dumps belong to your application and which don’t.
Worth noting, it takes just a few seconds until you get your first useful answer. I’ve played around with this for many hours and let me tell you one thing: You can really go deep. If you ask the right questions, the AI runs WinDBG/CDB commands that I haven’t seen in all these years of debugging, and that is simply amazing.
How can this help the industry?
I believe this is one of the really good examples of how AI can boost productivity. Analyzing crash dumps is a very tedious task. It begins with quickly checking and identifying whether crashes are the same or different, and often requires very advanced knowledge when a crash is challenging – really challenging.
Copilot can help here tremendously; it knows how to:
- Interpret assembly code (without you having to remember what EAX stands for)
- Check memory contents (so you don’t have to count hex bytes on your fingers)
- Traverse structures with symbols (goodbye to manual pointer arithmetic!)
- And so much more
This is a game changer – not just for en
8 Comments
Zebfross
Considering AI is trained on the average human experience, I have a hard time believing it would be able to make any significant difference in this area. The best experience I’ve had debugging at this level was using Microsoft’s time travel debugger which allows stepping forward and back.
cadamsdotcom
Author built an MCP server for windbg: https://github.com/svnscha/mcp-windbg
Knows plenty of arcane commands in addition to the common ones, which is really cool & lets it do amazing things for you, the user.
To the author: most of your audience knows what MCP is, may I suggest adding a tl;dr to help people quickly understand what you've done?
indigodaddy
My word, that's one of the most beautiful sites I've ever encountered on mobile.
revskill
[flagged]
lowleveldesign
I do a lot of Windows troubleshooting and still thinking about incorporating AI in my work. The posted project looks interesting and it's impressive how fast it was created. Since it's using MCP it should be possible to bind it with local models. I wonder how performant and effective it would be. When working in the debugger, you should be careful with what you send to the external servers (for example, Copilot). Process memory may contain unencrypted passwords, usernames, domain configuration, IP addresses, etc. Also, I don’t think that vibe-debugging will work without knowing what eax registry is or how to navigate stack/heap. It will solve some obvious problems, such as most exceptions, but for anything more demanding (bugs in application logic, race conditions, etc.), you will still need to get your hands dirty.
I am actually more interested in improving the debugger interface. For example, AI assistant could help me create breakpoint commands that nicely print function parameters when you only partly know the function signature and do not have symbols. I used Claude/Gemini for such tasks and they were pretty good at it.
As a side note, I recall Kevin Gosse also implemented a WinDbg extension [1][2] which used OpenAI API to interpret the debugger command output.
[1] https://x.com/KooKiz/status/1641565024765214720
[2] https://github.com/kevingosse/windbg-extensions
danielovichdk
Claiming to use WinDBG for debugging a crash dump and the only commands I can find in the MCP code are these ? I am not trying to be a dick here, but how does this really work under the covers ? Is the MCP learning windbg ? Is there a model that knows windbg ? I am asking becuase I have no idea.
You cannot debug a crash dump only with these 4 commands, all the time.
anougaret
this is pretty cool but ultimately it won't be enough to debug real bugs that are nested deep within business logic or happening because of long chains of events across multiple services/layers of the stack
imo what AI needs to debug is either:
– train with RL to use breakpoints + debugger or to do print debugging, but that'll suck because chains of action are super freaking long and also we know how it goes with AI memory currently, it's not great
– a sort of omniscient debugger always on that can inform the AI of all that the program/services did (sentry-like observability but on steroids). And then the AI would just search within that and find the root cause
none of the two approaches are going to be easy to make happen but imo if we all spend 10+ hours every week debugging that's worth the shot
that's why currently I'm working on approach 2. I made a time travel debugger/observability engine for JS/Python and I'm currently working on plugging it into AI context the most efficiently possible so it debugs even super long sequences of actions in dev & prod hopefully one day
it's super WIP and not self-hostable yet but if you want to check it out: https://ariana.dev/
Tepix
Sounds really neat!
How does it compare to using the Ghidra MCP server?