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openjdk/jmc

https://openjdk.org/projects/jmc OpenJDK Mission Control, a production time profiling and diagnostics tools suite.

1
Hotness score
25
Reliability score
984
Stars
about 7 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

Current1

Previous8

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: 1
  • 20% Breakout: 1
  • Stars gained: 1d: 0
  • Stars gained: 7d: 0
  • Stars gained: 14d: 1
  • Stars gained: 30d: 3
  • Stars gained: 90d: 16

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

Current25

Previous25

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: 19
  • 30% Closure: 0
  • 20% Shipping: 0
  • 10% Liveness: 99
  • 10% Support burden: 100

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 headline19

Activity on 7 of 90 tracked days in the last 90 and 2 of 30 in the last 30.

Activity pulse
Closure30% of headline0

Merged 0 of 7 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 headline99

Last push 3 days ago — freshness decays as pushes age (roughly halves every 83 days without a push).

Repository
Support burden10% of headline100

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

Open issuesStargazers
Adoption confidence24

Modeled — how confidently teams are adopting this repo. Stargazers

Maintenance quality27

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

Risk score45

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

Stays active (30d)13%

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

Stays active (90d)36%

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)76%

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

Measured history

Project metrics

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

Weekly star gains

Jul 19, 2026

Current0

Previous1

Weekly star gains

52-1
Full metrics details

GitHub GraphQL current stargazer cohort, reconstructed from starredAt events and totalCount retrieved in one stable pagination walk. GitHub does not expose historical unstar events, so this is not an exact ledger of past star counts.

180 observed daily rows. Missing days are not fabricated.

DateValue

Cumulative stars

Jul 19, 2026

Current984

Previous984

Cumulative total

9844920
Full metrics details

Running total of the GitHub GraphQL stargazer count over time (not weekly deltas), reconstructed from starredAt events and totalCount in one stable pagination walk. GitHub does not expose historical unstar events, so this is not an exact ledger of past star counts.

180 observed daily rows. Missing days are not fabricated.

DateValue

Issues opened

Jul 12, 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.

178 observed daily rows. Missing days are not fabricated.

DateValue

Issues closed

Jul 12, 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.

178 observed daily rows. Missing days are not fabricated.

DateValue

Pull requests opened

Jul 12, 2026

Current0

Previous0

Weekly total

630
Full metrics details

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

178 observed daily rows. Missing days are not fabricated.

DateValue

Pull requests closed

Jul 12, 2026

Current1

Previous0

Weekly total

740
Full metrics details

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

178 observed daily rows. Missing days are not fabricated.

DateValue

Pull requests merged

Jul 12, 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.

178 observed daily rows. Missing days are not fabricated.

DateValue

Pull request close ratio (daily)

Jul 17, 2026

Current0.00

Previous0.00

Daily ratio — a gap means nothing was opened that day

How this works: each day's value is pull requests closed that day ÷ pull requests opened that day. Days when nothing was opened render as gaps — never a rolling average or a made-up zero. Values above 1 mean the project closed more pull requests than arrived that day.

4.002.000.00
Full metrics details

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

21 observed daily rows. Missing days are not fabricated.

Per-day formula: pull requests closed that day ÷ pull requests opened that day; blank when nothing was opened (a gap, not a zero).

DatePRs openedPRs closedStored score

About Jmc

OpenJDK Mission Control (JMC) is a diagnostics and profiling suite built for real-world Java operations. The project focuses on production-safe observability, giving teams a way to inspect JVM behavior, investigate performance issues, and understand runtime characteristics without relying on intrusive debugging workflows. As part of the OpenJDK ecosystem, JMC is relevant for organizations that run Java services at scale and need consistent tooling for performance analysis, troubleshooting, and ongoing JVM health monitoring. For platform teams, SREs, and Java developers, openjdk/jmc represents a mature foundation for production-time insight into Java workloads.

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