What should I check when triaging crashes?

The essentials: group identical failures, rank by occurrence and affected users, read the worst one's trace and breadcrumbs, and tie it to a build. Run them consistently so issues are caught while still small. Each item depends on real data — failures captured, grouped, and tied to builds — rather than impressions.

What does the checklist depend on?

Trustworthy data. Capturing failures automatically with full context, grouping them by impact, and tying each to its build is what makes every item verifiable rather than aspirational.

A Crash Triage Checklist

Q: How do I keep this checklist lightweight?

Keep it to the essentials and lean on automatic capture to do the work. A checklist you will actually run beats an exhaustive one you abandon, and grouping means the same quick pass scales from ten failures to ten thousand.

Quick answer: A Crash Triage Checklist covers what to check when triaging crashes: group identical failures, rank by occurrence and affected users, read the worst one's trace and breadcrumbs, and tie it to a build. The thread running through all of it is the same — capture failures automatically with full context, group them by impact, and tie each to its build — which is what makes every item on the checklist actionable rather than aspirational.

A good checklist turns a vague intention into a repeatable routine. This one covers what to check when triaging crashes: group identical failures, rank by occurrence and affected users, read the worst one's trace and breadcrumbs, and tie it to a build. It is deliberately lightweight, because a checklist you will actually run beats an exhaustive one you abandon. Here is how to work through it and the data each item depends on.

The checklist

A Crash Triage Checklist comes down to a handful of essentials: group identical failures, rank by occurrence and affected users, read the worst one's trace and breadcrumbs, and tie it to a build. None of them are heavy; each is a small, concrete check that catches a category of problem before it reaches your players. The value is in running them consistently when triaging crashes, so issues are caught while they are still small.

What makes the checklist work is that each item rests on real data rather than impressions. You cannot honestly check your crash-free rate or your top signatures from a quiet inbox; you need the failures captured, grouped, and tied to builds, which is what turns each line from a hope into a verifiable fact.

Why the report you get is never the whole story

When a player does take the time to tell you something broke, the message is almost always thin: “it crashed,” maybe a screenshot, rarely a version number, and almost never the exact steps. You are left reconstructing the scene of an accident from a single blurry photo. The information you actually need to fix the bug — the stack trace, the device, the build, the state the game was in — is precisely what a human report leaves out.

That is why working from manual reports alone keeps you slow. Every ticket becomes a back-and-forth interrogation, and half the time the player has moved on before you get an answer. Automatic capture removes the interrogation entirely, because the context travels with the failure the instant it happens.

Why “it works on my machine” is a trap

Your development machine is the single least representative device your game will ever run on. It is the one configuration guaranteed to work, because you built and tested the game on it. Your players live out on the long tail of GPUs, drivers, operating-system versions, resolutions, and background software, and that long tail is exactly where the failures you never reproduce are hiding.

This is why local testing, however thorough, has a hard ceiling. You cannot own every device, and you cannot imagine every combination. Field data closes that gap by letting the failures come to you with the configuration attached, so a crash that only happens on one driver version stops being a mystery and becomes a one-line filter.

Turning a pile of crashes into a ranked worklist

Raw crash data is overwhelming if every occurrence is its own line. The trick is grouping: identical failures, fingerprinted by their stack trace, collapse into one issue with a count. Suddenly the question “what should I fix first?” answers itself, because the bug hitting the most players sits at the top with the biggest number next to it.

That ordering is what makes a small team effective. You are never going to fix everything, but you do not have to. Fixing the top few signatures usually removes the large majority of real-world failures, and prioritising by frequency means your limited hours always go to the bug that matters most right now.

Making it a habit

The foundation under every item is automatic capture: each failure recorded with its stack trace, the device and OS, the build, and the breadcrumb trail, grouped so the worst is on top and tied to its build so regressions are obvious. With that in place, working the checklist when triaging crashes is a quick, honest pass rather than guesswork.

Keep it light and repeatable. A solo developer or a two-person studio can run this checklist when triaging crashes in minutes, and the same routine scales as your audience grows because grouping does the heavy lifting. Run it consistently and the problems it catches never get the chance to compound.

This is where a tool like Bugnet earns its place. Its SDK captures every failure automatically with the full stack trace plus device, OS, memory, build, and game-state context, folds identical failures into one grouped issue with an occurrence count, and ties each to the build it happened on. The result is that the abstract idea above stops being theory and becomes a ranked list you work down — the worst problem first, verified fixed when its signature disappears from the next release.

The crashes you never hear about are the ones costing you most. Visibility is what turns them into a list you can actually work down.