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garris/BackstopJS

Catch CSS curve balls.

60
Hotness score
1
Reliability score
7,156
Stars
almost 12 years
Age
0
Published reviews
0
Questions

Scores

Hotness and reliability

The two headline scores, measured daily, with 30, 90, and 180 day views.

Hotness score

Jul 19, 2026

Current60

Previous1

Weekly average

100500
Full metrics details

Derived only from GitHub GraphQL starredAt events after daily star totals are reconciled.

180 observed daily rows. Missing days are not fabricated.

Hotness formula

40% Hot today + 40% Hot this week + 20% Breakout.

  • 40% Hot today: 1
  • 40% Hot this week: 6
  • 20% Breakout: 25
  • Stars gained: 1d: 0
  • Stars gained: 7d: 1
  • Stars gained: 14d: 7156
  • Stars gained: 30d: 7156
  • Stars gained: 90d: 7156

Per-day formula: 0.40 × Hot today + 0.40 × Hot week + 0.20 × Breakout.

  • Same-day multiplier = stars 1d ÷ max(1, stars 7d ÷ 7)
  • Weekly multiplier = stars 7d ÷ max(1, stars 30d ÷ 30 × 7)
  • Fortnight multiplier = stars 14d ÷ max(1, stars 90d ÷ 90 × 14)
  • Hot today = clamp(70 × log-scale(stars 1d, 1000) + 30 × breakout-scale(same-day, 4))
  • Hot week = clamp(75 × log-scale(stars 7d, 5000) + 25 × breakout-scale(weekly, 3))
  • Breakout = clamp(35 × breakout-scale(same-day, 4) + 40 × breakout-scale(weekly, 3) + 25 × breakout-scale(fortnight, 2.5))
DateStarsStars 1dStars 7dStars 14dStars 30dStars 90dSame-day multiplierWeekly multiplierFortnight multiplierHot todayHot weekBreakoutStored score

Reliability score

Jul 19, 2026

Current1

Previous1

Weekly average

100500
Full metrics details

Derived from the displayed continuity, closure, shipping, liveness, and support-burden components.

180 observed daily rows. Missing days are not fabricated.

Reliability formula

30% Continuity + 30% Closure + 20% Shipping + 10% Liveness + 10% Support burden.

  • 30% Continuity: 1
  • 30% Closure: 0
  • 20% Shipping: 0
  • 10% Liveness: 1
  • 10% Support burden: 0

Per-day formula: 0.30 × Continuity + 0.30 × Closure + 0.20 × Shipping + 0.10 × Liveness + 0.10 × Support burden.

  • Continuity = 35% active ratio 90d + 20% active ratio 30d + 15% PR efficiency 90d + 10% PR efficiency 30d + 10% liveness + 10% observed-history coverage
  • Closure = 55% PR merge efficiency 90d + 45% issue close efficiency 90d
  • Shipping = 100 × (20% × release ratio 30d + 30% × release ratio 90d + 50% × release ratio 180d); ratios are releases ÷ 2, 6, and 12, capped at 1
  • Support burden = 100 − 2 × open issues per 1,000 stars
  • Liveness = 100 × exp(−pushed days ago ÷ 120)

Stars, issues, pull requests, and releases are daily observations. Pushed-days and license are repository snapshot-derived inputs and are not independent historical GitHub events.

DateIssues openedIssues closedPRs openedPRs mergedReleasesStarsOpen issuesPushed days agoContinuityClosureShippingLivenessSupport burdenLicenseObserved daysMissing daysNeeds healingStored score

Reliability breakdown

What drives the reliability score

Component scores measured daily from GitHub activity (2026-07-18).

Continuity30% of headline1

Activity on 0 of 90 tracked days in the last 90 and 0 of 30 in the last 30.

Activity pulse
Closure30% of headline0

Merged 0 of 0 PRs opened and closed 0 of 0 issues opened over the last 90 tracked days — PR flow carries 55% of this component, issue flow 45%.

PRs merged (90d)PRs opened (90d)Issues closed (90d)Issues opened (90d)
Shipping20% of headline0

0 releases in the last 180 days, 0 in the last 90 and 0 in the last 30 — a steady cadence scores highest.

Releases
Liveness10% of headline1

Last push 681 days ago — freshness decays as pushes age (roughly halves every 83 days without a push).

Repository
Support burden10% of headline0

Open-issue load isn't tracked for this repo yet.

Open issuesStargazers
Adoption confidence1

Modeled — how confidently teams are adopting this repo. Stargazers

Maintenance quality2

Modeled from PR/issue responsiveness and upkeep signals. Activity pulse

Risk score100

Modeled — lower is better; adoption and continuity risk. Repository

Stays active (30d)0%

Modeled chance the repo stays active over 30 days. Activity pulse

Stays active (90d)3%

Modeled chance the repo stays active over 90 days. Activity pulse

Release rhythm (180d)0

Regularity of releases over the last 180 days. Releases

Maintainer bus risk (90d)94%

Modeled — lower is better; concentration of commits in few maintainers. Contributors graph

Topics

Explore related topics

Jump into the topic listings this repository belongs to.

JavascriptUi TestingBrowser Automation

Measured history

Project metrics

Weekly GitHub totals with 30, 90, and 180 day views.

Issues opened

Feb 22, 2026

Current0

Previous0

Weekly total

110
Full metrics details

GitHub API observation. Historical values render only when the source provides a real observation for that day.

36 observed daily rows. Missing days are not fabricated.

DateValue

Issues closed

Feb 22, 2026

Current0

Previous0

Weekly total

110
Full metrics details

GitHub Search issue totals queried for exact UTC days; rolling values are sums of proven daily counts.

36 observed daily rows. Missing days are not fabricated.

DateValue

Pull requests opened

Feb 22, 2026

Current0

Previous0

Weekly total

110
Full metrics details

GitHub API observation. Historical values render only when the source provides a real observation for that day.

36 observed daily rows. Missing days are not fabricated.

DateValue

Pull requests closed

Feb 22, 2026

Current0

Previous0

Weekly total

110
Full metrics details

GitHub Search pull-request totals queried for exact UTC days; rolling values are sums of proven daily counts.

36 observed daily rows. Missing days are not fabricated.

DateValue

Pull requests merged

Feb 22, 2026

Current0

Previous0

Weekly total

110
Full metrics details

GitHub API observation. Historical values render only when the source provides a real observation for that day.

36 observed daily rows. Missing days are not fabricated.

DateValue

About Backstopjs

BackstopJS is a visual regression testing tool that captures webpage screenshots and compares them against approved baselines to detect layout and styling changes. It runs scripted scenarios across viewports and reports pixel differences for review. The project is commonly used in frontend QA, design system maintenance, and release pipelines where CSS or component updates may introduce unintended UI regressions.

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