How do I decide between capturing everything and filtering?

Look at the real impact: let grouping fold identical failures into signatures rather than dropping errors you might need. Capturing failures with full context, grouping them by how many players each hits, and tying them to builds turns the judgement call into a reading of the numbers.

What if I make the wrong call?

Working from data limits the downside. Because failures are tied to builds and grouped by impact, you'll see quickly whether the choice worked — a spike, a falling count — and can adjust, rather than discovering it weeks later in your reviews.

Should You Capture Every Error or Filter?

Quick answer: On capturing everything versus filtering: capture broadly but group aggressively, so you see everything without drowning in noise. The way to make the call with confidence rather than instinct is to let grouping fold identical failures into signatures rather than dropping errors you might need. That depends on capturing failures with full context, grouping them by impact, and tying each to its build — the data that turns a judgement call into a clear decision.

“Should You Capture Every Error or Filter?” is the kind of question where the honest answer is “it depends,” but it depends on things you can actually measure. On capturing everything versus filtering, the rule of thumb is: capture broadly but group aggressively, so you see everything without drowning in noise. Made from a gut feeling, the choice is a coin flip; made from real failure data, it is straightforward. This guide covers how to decide, and how to make the call with evidence — let grouping fold identical failures into signatures rather than dropping errors you might need.

The honest answer

On capturing everything versus filtering, the honest answer is: capture broadly but group aggressively, so you see everything without drowning in noise. The reason it feels hard is that, without data, you are weighing risks you cannot see — and instinct is biased by the fact that everything works on your own machine. Once you can see the real impact of the failures involved, the choice usually makes itself.

It is rarely a permanent, all-or-nothing decision either. The right call this time depends on the specifics — how many players are affected, how severe it is, what changed in the last build — which is exactly the kind of thing real data tells you and a hunch does not.

What good context actually looks like

The difference between a bug you fix in five minutes and one you chase for a week is almost always context. A bare error message tells you something went wrong; a useful report tells you where, on what, after what sequence of actions, in which build. Stack trace, device model, OS version, available memory, and the breadcrumb trail of recent events are the fields that turn guessing into reading.

When that context is captured automatically and consistently, reproduction stops being the bottleneck. You can often see the cause directly in the trace, and when you cannot, the breadcrumbs show you the exact path to walk to reproduce it yourself.

The silent majority who never report anything

For every player who files a report, a large number simply hit the problem, sigh, and close the game. They do not owe you a bug report, and most will not write one. The failures that churn the most players are therefore the ones least likely to ever reach your inbox, which is a deeply unfair feedback loop: the worse the bug, the quieter it tends to be.

The only way out of that loop is to stop depending on goodwill. When every crash is recorded automatically, the silent majority become data. You finally see the failure that is quietly costing you installs, ranked by how often it actually happens rather than by who happened to be patient enough to complain.

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.

Deciding with data

To make the call with confidence, let grouping fold identical failures into signatures rather than dropping errors you might need. The foundation is failures captured with full context, grouped so you can see how many players each one hits, and tied to builds so you can see what changed and when. With that, the decision stops being a debate about opinions and becomes a reading of the numbers.

This is what lets a small team act decisively under pressure. Whether the answer is one option, the other, or both in sequence, it is grounded in what is actually happening to your players rather than in whoever argues hardest. And because failures stay tied to builds, you can confirm afterwards that the choice was right.

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.

Most of the failures hurting your game are silent. The first job is making them visible; the fixes get a lot easier after that.