ForensicDbg 的 AI 崩潰除錯評測

ForensicDbg has been built from the ground up to be the best post-mortem debugger for Windows developers, no matter what workflows they use. Our core debugging engine has made crash analysis significantly more accurate and convenient for our testers, but how can you really make that kind of claim for coding agents? More importantly, how can you prove it?

With a Claude Code driven crash dump evaluation competition of course!

Let me run you through the competitors we chose, the crash dump gauntlet we created, the metrics we are evaluating, and some thoughts on how results shake out as of today.

The Competitors

First we have ForensicDbg. Our home grown hero will be accessed by Claude through its built-in MCP server.

Everyone knows our next contender will be WinDbg, but you might be surprised that we are NOT accessing it through its new MCP server. We spent time evaluating it and found it was difficult to set up, incredibly limited in how it could be accessed, and didn’t give meaningfully better results than our next competitor.

No… if you want to utilize WinDbg through a modern MCP server, the best way is still mcp-windbg, created by svnscha and released under the MIT License. It bridges AI models with CDB and does an amazing job, even after the latest WinDbg release gave us an “official” option.

That being said, AI tools like Claude are actually pretty good at utilizing CDB’s powerful command line interface directly as well, so that is what we measured for our next entrant. Think of this as the classic debugging champion being utilized in the traditional way.

LLDB is our next competitor and is a heavy hitter in the MacOS and iOS worlds, but that doesn’t make it a slouch when it comes to Windows debugging. It also has its own built-in, first party MCP server. Unfortunately Claude repeatedly couldn’t get it to work, so we skipped it and used the tool directly.

Oddly, the final choice is nothing at all. It would be really embarrassing if raw AI crash dump analysis works great already, so we need to test to make sure separate debugging tools are even necessary.

The Gauntlet

These tests are run using Claude Code 5.5 with the default Sonnet Medium settings. We have assembled 10 crash dumps and each is analyzed 10 times per tool, clearing context between each run. 100 analysis sessions per tool. 500 runs total.

The crash dumps fall into two categories. First we gathered 6 different real-world crash dumps that cover a broad range of crashes and then generated 4 synthetic crash dumps covering additional common crash cases.

Some are minidumps and some are full. Some have PDBs, EXEs, and even local source code while others have nothing but a dump file. Some are simple crashes with obvious solutions while others are complex memory errors, corrupted heaps, and stomps happening way after the initial issue occurred.

The Metrics

Analysis quality is the most important metric since nothing matters if it can’t accurately work out what the problem even is. Does it understand the issue? Is it specific? Was its output consistent during repeated runs?

Next, we care about the actionability of the suggested fix. Does Claude clearly identify a specific, reasonable solution to the identified problem? This metric is especially important to you if you expect your AI agents to automatically repair the problem.

Now comes the cost. We are quickly leaving the world of acceptable infinite token burn, so cost matters more and more every day. Claude has a great costUSD value we can check after each test to see the total spend so that is what we will use. Simple.

Time is money, so time is the next metric we track. This isn’t as important as some of the above metrics but that doesn’t mean it is useless. The difference between your agent taking 8 hours to process the daily crashes vs 14 can be vital to staying on top of incoming workloads.

The Results

Now that we have gone over all of that, let’s look at our latest results!

Well, clearly we showed that you should never be debugging crash dumps without a dedicated tool your AI can access. Using nothing scored the worst in every possible metric.

Comparing the mcp-windbg and CDB runs shows that a well designed MCP server can assist your AI with issue comprehension all on its own. They both are using CDB to analyze the crash, but mcp-windbg’s addition of the server improves analysis and actionability without changing the underlying tool.

We included LLDB in this test so that we could have a larger variety of options, but this wasn’t its day. Maybe it could be valuable if you were very price sensitive, but then, is it really that much better than just having your agent use CDB directly for half the cost?

ForensicDbg is the only competitor to hit a level of success that I would expect from my tools in terms of analysis and actionability. Its enriched crash data forms a valuable foundation for any AI tool to work from, and the results show themselves clearly while still remaining right around the middle of the pack for cost and time.

AI models get more powerful every day, but these results show that a parallel path of equally significant improvements is possible by making the correct tools available to your agents. ForensicDbg is creating a better debugging foundation for everyone to build off of, and will continue to improve from here.

Check out our free beta if you would like to try debugging with ForensicDbg and help contribute to its continued development.
https://www.forensicdbg.com/beta

If you would like to check out our raw testing results, you can find them here.
https://static.forensicdbg.com/docs/comparison-gauntlet-2026-10-19.pdf