What is the difference between severity and a bug priority score?

Severity measures how badly the bug breaks the product in isolation. A priority score combines severity with how many users hit it, how confident you are in the estimate, and how much effort the fix takes. A severe bug that affects one user on a deprecated path can score lower than a moderate bug on your signup flow.

Which scoring model should I use for bugs, RICE or WSJF?

Use RICE (Reach x Impact x Confidence / Effort) when you want a simple, defensible ranking that most teams can adopt in a day. Use WSJF (cost of delay / job size) when time-criticality matters heavily, such as security or compliance deadlines. Both produce a number you divide by effort, so pick the one whose inputs your team can estimate honestly.

How often should bug scores be recalculated?

Re-score at each triage cycle, weekly for active backlogs. Reach and impact drift as usage changes, and confidence rises once a bug is reproduced. A score set months ago and never revisited is usually the reason a stale bug outranks a fresh, more damaging one.

How to Prioritize Bugs With a Scoring Model, Step by Step

Prioritize bugs with a scoring model rather than a gut call: score each bug on reach (how many users hit it), impact (how badly it hurts them), confidence (how sure you are of those estimates), and effort (how much work the fix is), then rank by the resulting number. This is the RICE method — popularized for feature prioritization — adapted for bugs. It converts "this feels urgent" into a comparable score every stakeholder can see, so the highest-value fix rises to the top instead of the loudest request.

Why severity alone can't prioritize a backlog

Severity answers one question: how badly does this bug break the product? That's necessary but not sufficient. A crash on a settings page almost nobody visits is high-severity and low-priority. A layout glitch that blocks the "Buy" button on mobile is moderate-severity and top-priority. Ranking by severity alone puts the wrong bug first.

A scoring model fixes this by multiplying severity by the other factors that actually decide business value — how many people are affected, how confident your estimates are, and what the fix costs. If you still conflate the two dimensions, start with the difference between severity and priority, then layer a score on top.

How to score a bug: the RICE formula adapted for bugs

RICE gives you a single comparable number per bug:

Score = (Reach × Impact × Confidence) ÷ Effort

Reach. Estimate how many users or sessions hit the bug in a fixed window — say, per month. Use real numbers where you have them: affected accounts, error counts, or the share of traffic on the broken flow. A checkout bug reaches far more people than one on an admin export screen.
Impact. Rate how much the bug hurts each affected user on a fixed scale (for example 3 = blocks the task, 2 = painful workaround, 1 = minor annoyance). This is your severity, expressed as a number you can multiply.
Confidence. Discount the score by how sure you are. Use a percentage: 100% when the bug is reproduced with evidence, 50% when reach is a guess, lower when it's a single unconfirmed report. Confidence is what stops a loud anecdote from dominating a ranking.
Effort. Estimate the fix cost in person-days (or story points). Dividing by effort is what makes a one-hour fix for a common bug outrank a two-week rewrite for a rare one.

Multiply the top three, divide by effort, and sort descending. The bugs at the top are the ones that return the most user value per unit of work.

How to set consistent scoring scales

A scoring model only works if two people scoring the same bug land close to the same number. That requires fixed, written scales — not vibes.

Write the scale down. Define each impact level with a concrete example: "3 = user cannot complete the core task; 2 = task possible but with a manual workaround; 1 = cosmetic." Ambiguous scales produce inconsistent scores.
Anchor reach to data, not adjectives. "A lot of users" is not a number. Tie reach to affected sessions, error volume, or percentage of traffic on the affected route so it's auditable later.
Cap the range. Keep each factor on a small bounded scale (1–3 or 1–5). Unbounded numbers let one enthusiastic estimate swamp everything else.
Calibrate on a few known bugs. Score five bugs everyone already agrees on, compare, and adjust the scale definitions until the ranking matches team intuition. Do this once and you'll trust the model for the rest.

How to prioritize with WSJF when deadlines dominate

RICE weights broad reach heavily. When time-criticality is the deciding factor — a security bug with a disclosure deadline, a compliance fix, a regression blocking a launch — Weighted Shortest Job First (WSJF) fits better:

Score = Cost of Delay ÷ Job Size

Cost of delay sums business value, time-criticality, and risk-or-opportunity reduction; job size is your effort estimate. Because both models divide value by effort, you can run RICE as your default and switch a bug to WSJF only when a hard deadline changes the calculus. Don't mix the two in one ranked list — the numbers aren't comparable.

How to keep bug scores honest over time

The most common failure isn't a bad formula — it's a stale one. A score is a snapshot of what you knew when you set it.

Re-score at every triage cycle. Reach and impact drift as usage shifts and features ship. A bug on a page you've since deprecated should fall; a bug on a newly popular flow should climb.
Raise confidence when you reproduce. An unconfirmed report might sit at 40% confidence. Once it's reproduced with a screenshot, console log, and network trace, push it toward 100% — and watch it jump the queue on merit, not volume.
Log the inputs, not just the score. Store the reach, impact, confidence, and effort numbers on the ticket. A bare score can't be challenged; the inputs can, which keeps the model trusted.

Confidence is where good evidence pays off directly. The faster a report is reproduced with real state attached, the sooner its score reflects reality. Capturing that evidence at report time — the steps, console, network, and environment in one click — is exactly what Klavity Snap is built to do, which is why reproduced, high-confidence bugs stop losing the queue to loud-but-unverified ones.

A worked example

Two bugs compete for the next slot:

Bug A — checkout button misaligned on iOS Safari. Reach 8,000 sessions/month, Impact 3 (blocks purchase), Confidence 100% (reproduced), Effort 0.5 days. Score = (8000 × 3 × 1.0) ÷ 0.5 = 48,000.
Bug B — CSV export drops a column on a rarely used admin report. Reach 40 sessions/month, Impact 2, Confidence 0.8, Effort 2 days. Score = (40 × 2 × 0.8) ÷ 2 = 32.

Bug A outranks Bug B by three orders of magnitude — despite both being real, reproducible defects. Without a model, Bug B might have jumped the line simply because an internal admin filed it loudly. The score makes the trade-off explicit and defensible, and anyone can audit the four inputs that produced it.

Put it into practice

Start small: pick RICE, write your scales down, calibrate on five known bugs, and score your next triage batch. Store the inputs on each ticket and re-score weekly. Within a cycle or two you'll have a ranked backlog that reflects user value rather than volume — and a number you can point to when someone asks why their bug isn't first. For the upstream steps that feed the model, see how to triage a bug backlog without losing your mind.