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Calcrivo

Alert Noise Score Calculator

Score how noisy your alerting is from the ratio of false-positive and auto-resolved alerts to total alerts fired.

Inputs

alerts

Total alert notifications sent over the measurement period.

alerts

Alerts that fired but did not correspond to a real, actionable problem.

alerts

Alerts that resolved on their own before anyone took action (may indicate overly sensitive thresholds).

Alert Noise Score

40.0%

Rating

High noise — significant alert fatigue risk

Actionable Alert Rate

60.0%

Noise Alerts

200alerts

Step by step

  1. Noise alerts: false positives + auto-resolved

    80 + 120

    = 200 alerts

  2. Noise score: noise / total × 100

    200 / 500 × 100

    = 40.0%

  3. Rating

    based on noise score thresholds

    = High noise — significant alert fatigue risk

How it works

Noise score = (false_positives + auto_resolved) / total_alerts × 100. Both categories represent alerts that didn't require the human response an alert implies: false positives never reflected a real problem, and auto-resolved alerts fixed themselves before anyone acted. A high noise score is the leading driver of alert fatigue — responders start ignoring or delaying response to a channel that's frequently wrong or self-resolving.

Formula

noise_score = (non_actionable_alerts / total_alerts) × 100

non_actionable_alerts
Alerts that required no action (false positives, auto-resolved)
total_alerts
Total alerts fired in the period

Frequently Asked Questions

Is a 0% noise score achievable or even desirable?

It's rarely achievable in practice — some legitimate transient issues will always auto-resolve before action is needed. Most well-tuned alerting systems land in the 5-15% range rather than exactly zero.

What's the difference between a false positive and an auto-resolved alert?

A false positive never corresponded to a real problem at all (a monitoring bug, a bad threshold, a flapping check), while an auto-resolved alert reflected a real but transient condition that fixed itself — both are noise from a responder's perspective, but they call for different fixes.

How do I reduce auto-resolved alert volume?

Increase the `for:` duration so transient conditions don't cross the firing threshold, or add a secondary condition requiring sustained severity, so only conditions that would matter to a human actually notify one.

How do I reduce false positives specifically?

Audit alert rules against actual incident postmortems — if an alert rule has fired many times with zero corresponding incidents, either its threshold is miscalibrated or it's not actually measuring something that matters.

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