Öffentliches RegisterMittel
Software-GesundheitsberichtSchema 0.27.0 · Metriken 2.3.1 · 2026-08-01 16:25 UTC
precice / fmi-runner
Enables co-simulation with FMU models via the coupling library preCICE
precice/fmi-runner erreicht einen Gesundheitsindex von 54 von 100 und liegt damit im Bereich Mittel. Am stärksten schneidet es bei Engineering Quality (82/100) ab, am schwächsten bei Community & Adoption (38/100). Zuletzt vor 14 Tagen aktualisiert. Ein einzelner Mitwirkender trägt den Großteil der jüngsten Arbeit.
54
gesamt / 100Software-Gesundheitsindex
Metriken werden auf einer standardisierten Skala von 1–100 in gewichtete Kategorien gruppiert. Der Gesamtwert beginnt als ihr gewichtetes Mittel, kalibriert auf die Verteilung des öffentlichen Registers, sodass die Stufen Perzentilbedeutung tragen; sobald öffentliche Evidenz die Richtlinie für Hochrisikojurisdiktionen auslöst, wird die Bewertung angepasst und erhält die Obergrenze Gefährdet von 34.
54
Außergewöhnlich93-100Die Spitzengruppe des Registers (≈ obere 5 %); erfüllt im Wesentlichen alle geprüften Kriterien
Exzellent80-92Durchgehend stark; geringfügige Lücken
Gut65-79Gesund; Lücken sind begrenzt und beherrschbar
Mittel50-64Akzeptabel mit deutlichen Lücken; Überprüfung empfohlen
Schwach35-49Wesentliche Schwächen in mehreren Bereichen
Gefährdet20-34Erhebliche Schwächen; eine Übernahme erfordert Vorsicht
Kritisch1-19Schwerwiegende Probleme (aufgegeben, nur ein Maintainer, keine Hygiene)
Bewertungsprofil
Jede Achse ist eine Kategorie. Die Form zählt mehr als der Durchschnitt — ein gesundes Projekt füllt die gesamte Fläche, während ein Profil aus Spitzen und Kratern bedeutet, dass Stärke in einer Dimension Risiken in einer anderen verdeckt.
Der gewichtete Gesamtwert 53 wird auf der veröffentlichten Indexskala auf 54 kalibriert (Register-Kalibrierung 2026-08-02).
Eigentümerschaft
preCICEOrganisation
257 Follower64 öffentliche Reposseit Mai 2015
Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.
Paket-Ökosysteme
| Registry | Paket | Version | Downloads / Monat | Versionen | Zuletzt veröffentlicht | Tags |
|---|---|---|---|---|---|---|
| PyPI | fmiprecice | 0.2.1 | 178 | 1 | vor 864 Tagen | precicefmiodecontrollerpdecoupling |
Metriken nach Kategorie
Vitalität
Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?
41
Entwicklungsaktivität
39Schwach
Wie die Bewertung erfolgt
| 28.8/36 | Push-Aktualität — letzter Push vor 14 Tagen |
| 2.8/36 | Commit-Rhythmus — 4/52 Wochen mit Commits |
| 6.3/18 | Commit-Volumen — 4 Commits im letzten Jahr |
| 1/10 | OpenSSF Scorecard: Maintained — 2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1 |
Verwendete Eingangsdaten
| commits_last_year | 4 |
| human_commit_share | 1 |
| days_since_last_push | 14 |
| active_weeks_last_year | 4 |
Release-Disziplin
44Schwach
Wie die Bewertung erfolgt
| 27/27 | Liefert Releases aus — 3 Releases veröffentlicht |
| 0/36 | Release-Aktualität — letztes Release vor 864 Tagen |
| 12.6/27 | Release-Rhythmus — ein Release etwa alle 122,9 Tage |
| 0/10 | OpenSSF Scorecard: Signed-Releases — keine Daten |
Verwendete Eingangsdaten
| releases_count | 3 |
| latest_release_tag | v0.2.1 |
| releases_from_tags | nein |
| days_since_latest_release | 864 |
| mean_days_between_releases | 122,9 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): OpenSSF Scorecard: Signed-Releases. Die verbleibenden Gewichte wurden renormalisiert.
Community & Verbreitung
Hat das Projekt Nutzer, Downloads, Aufmerksamkeit und ein einladendes Umfeld für Beitragende?
38
Popularität & Verbreitung
27Gefährdet
Wie die Bewertung erfolgt
| 18.6/60 | Stars — 15 Stars |
| 6.5/25 | Forks — 7 Forks |
| 1.7/15 | Watcher — 3 Watcher |
Verwendete Eingangsdaten
| forks | 7 |
| stars | 15 |
| watchers | 3 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Community-Gesundheit
50Mittel
Wie die Bewertung erfolgt
| 22.5/22.5 | README |
| 22.5/22.5 | Lizenz — anerkannte Lizenz (LGPL-3.0) |
| 0/18 | CONTRIBUTING-Leitfaden |
| 0/13.5 | Verhaltenskodex |
| 0/7.2 | Issue-Vorlage |
| 0/6.3 | PR-Vorlage |
Verwendete Eingangsdaten
| has_readme | ja |
| has_license | ja |
| readme_badges | — |
| has_contributing | nein |
| has_issue_template | nein |
| has_code_of_conduct | nein |
| readme_badge_services | — |
| has_pull_request_template | nein |
Ökosystem-Verbreitung (Downloads)
38Schwach
Wie die Bewertung erfolgt
| 30/80 | Downloads pro Monat — 178 Downloads/Monat über pypi |
| 0/20 | Abhängige in der Registry — von diesem Ökosystem nicht ausgewiesen |
Verwendete Eingangsdaten
| packages | fmiprecice |
| dependents | — |
| ecosystems | pypi |
| total_downloads | — |
| monthly_downloads | 178 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Abhängige in der Registry. Die verbleibenden Gewichte wurden renormalisiert.
Nachhaltigkeit & Governance
Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?
55
Maintainer-Resilienz (Bus-Faktor)
34Gefährdet
Wie die Bewertung erfolgt
| 9/54 | Bus-Faktor — 1 Beitragende decken die Hälfte aller Commits ab |
| 8.7/22.5 | Commit-Verteilung — wichtigste beitragende Person verfasste 62 % der Commits |
| 6.8/13.5 | Breite der Beitragenden — 5 Beitragende |
| 10/10 | OpenSSF Scorecard: Contributors — project has 5 contributing companies or organizations |
Verwendete Eingangsdaten
| bus_factor | 1 |
| contributors_sampled | 5 |
| top_contributor_share | 0,615 |
Wie die Bewertung erfolgt
| 25.2/42 | Issue-Lösungsquote — 60 % der Issues geschlossen |
| 27.7/30 | PR-Annahme — 12/13 entschiedene PRs gemergt |
| 0/13 | Newcomer PR acceptance — kein PR eines Erstbeitragenden in 30 Tagen entschieden |
| 1.5/15 | OpenSSF Scorecard: Code-Review — Found 4/21 approved changesets -- score normalized to 1 |
Verwendete Eingangsdaten
| merged_prs | 12 |
| open_issues | 4 |
| closed_issues | 6 |
| prs_merged_7d | — |
| prs_decided_7d | — |
| prs_merged_30d | — |
| prs_decided_30d | — |
| issue_closed_ratio | 0,6 |
| closed_unmerged_prs | 1 |
| first_time_authors_30d | — |
| first_time_prs_merged_30d | — |
| first_time_prs_decided_30d | — |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): newcomer_pr_acceptance. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
| 30/30 | Organisatorische Trägerschaft — im Besitz einer Organisation |
| 0/20 | Verifizierte Domain |
| 17.3/25 | Reichweite des Inhabers — 257 Follower von precice |
| 25/25 | Kontohistorie — 64 öffentliche Repos, Kontoalter ca. 11 Jahre |
Verwendete Eingangsdaten
| followers | 257 |
| owner_type | Organization |
| is_verified | — |
| owner_login | precice |
| public_repos | 64 |
| account_age_days | 4.089 |
Paketpflege
53Mittel
Wie die Bewertung erfolgt
| 25/25 | Veröffentlicht & auflösbar — 1 Paket(e) auf pypi |
| 4/35 | Veröffentlichungsaktualität — letzte Veröffentlichung vor 864 Tagen |
| 4/20 | Versionshistorie — 1 veröffentlichte Versionen |
| 20/20 | Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen |
Verwendete Eingangsdaten
| packages | fmiprecice |
| ecosystems | pypi |
| any_deprecated | nein |
| min_days_since_publish | 864 |
Engineering-Qualität
Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?
82
Engineering-Praktiken
80Exzellent
Wie die Bewertung erfolgt
| 24/24 | CI-Workflows — 5 Workflow(s) |
| 24/24 | Tests vorhanden |
| 16/16 | Linter-Konfiguration |
| 9.6/9.6 | Pre-Commit-Hooks |
| 0/6.4 | .editorconfig |
| 6/20 | OpenSSF Scorecard: CI-Tests — 4 out of 11 merged PRs checked by a CI test -- score normalized to 3 |
Verwendete Eingangsdaten
| has_ci | ja |
| has_tests | ja |
| has_editorconfig | nein |
| has_linter_config | ja |
| has_precommit_config | ja |
Dokumentation
85Exzellent
Wie die Bewertung erfolgt
| 30/30 | README |
| 25/25 | Dokumentationsverzeichnis |
| 0/15 | Dokumentations-/Homepage-Site |
| 10/10 | Repository-Beschreibung |
| 10/10 | Topics — 5 Topics |
| 10/10 | Wiki |
Verwendete Eingangsdaten
| topics | co-simulation, coupling, fmi, precice, fmpy |
| has_wiki | ja |
| homepage | — |
| has_readme | ja |
| has_docs_dir | ja |
| has_description | ja |
Sicherheit
Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?
50
Sicherheitslage
38Schwach
Wie die Bewertung erfolgt
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 0/7.5 | Branch-Protection — keine Daten |
| 0.8/2.5 | CI-Tests — 4 out of 11 merged PRs checked by a CI test -- score normalized to 3 |
| 0/2.5 | CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected |
| 0.8/7.5 | Code-Review — Found 4/21 approved changesets -- score normalized to 1 |
| 2.5/2.5 | Contributors — project has 5 contributing companies or organizations |
| 10/10 | Dangerous-Workflow — no dangerous workflow patterns detected |
| 0/7.5 | Dependency-Update-Tool — no update tool detected |
| 0/5 | Fuzzing — project is not fuzzed |
| 2.5/2.5 | Lizenz — license file detected |
| 0.8/7.5 | Maintained — 2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1 |
| 0/5 | Packaging — keine Daten |
| 0/5 | Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
| 0/5 | SAST — SAST tool is not run on all commits -- score normalized to 0 |
| 0/5 | Security-Policy — security policy file not detected |
| 0/7.5 | Signed-Releases — keine Daten |
| 0/7.5 | Token-Permissions — detected GitHub workflow tokens with excessive permissions |
| 7.5/7.5 | Vulnerabilities — 0 existing vulnerabilities detected |
Verwendete Eingangsdaten
| source | openssf_scorecard |
| checks_evaluated | 15 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 3 |
| scorecard_aggregate | 3,8 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): branch_protection, packaging, signed_releases. Die verbleibenden Gewichte wurden renormalisiert.
Abhängigkeits-Advisories
100Außergewöhnlich
Wie die Bewertung erfolgt
| 35/35 | Direkte Abhängigkeiten ohne bekannte Advisories — keine direkte Abhängigkeit trägt ein bekanntes Advisory |
| 25/25 | Indirekte Abhängigkeiten ohne bekannte Advisories — keine indirekte Abhängigkeit trägt ein bekanntes Advisory |
| 0/40 | Keine offenen Advisories — kein Advisory trägt ein Veröffentlichungsdatum |
Verwendete Eingangsdaten
| source | osv |
| advisories | 0 |
| affected_packages | 0 |
| assessed_packages | 20 |
| unassessed_packages | 0 |
| affected_by_severity | none |
| direct_affected_packages | 0 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Keine offenen Advisories. Die verbleibenden Gewichte wurden renormalisiert. Abgeglichen wurde die Laufzeit-Abhängigkeitshülle von pypi:fmiprecice@0.2.1 — das, was die Installation des veröffentlichten Pakets nach sich zieht — mit 20 Paketen. Erreichbarkeit wird nicht analysiert.
AI Readiness
Wie gut ist das Repository dafür ausgestattet, mit KI-Coding-Agenten entwickelt und gepflegt zu werden? Trägt ein bewusst kleines Gewicht (4 %): Agenten-Tooling ist ein echtes Pflegesignal, doch ein Repository ohne jedes Signal kann weiterhin 100/100 erreichen.
42
AI-Agentenkontext & Leitlinien
9Kritisch
Wie die Bewertung erfolgt
| 0/45 | Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln |
| 0/15 | Maschinenlesbare Doku (llms.txt) |
| 9.4/40 | Lesbare Commit-Historie — 10 von 57 menschlichen Commits benennen ihre Absicht (strukturierter Betreff oder erläuternder Text) |
Verwendete Eingangsdaten
| has_llms_txt | nein |
| legible_history_share | 0,175 |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
Wie die Bewertung erfolgt
| 0/18 | Bootstrap mit einem Befehl |
| 22/22 | Automatisierte Tests |
| 11/11 | Lint-/Format-Konfiguration |
| 11/11 | Statische Typprüfung — C (statisch typisiert) |
| 0/10 | Reproduzierbare Umgebung |
| 0/10 | Belegte Agentenpraxis — keine von Agenten verfassten Commits unter den letzten 57 |
| 0/8 | Automatisierte Wartung — keine automatisierten Abhängigkeits-Updates beobachtet |
| 0/10 | OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
Verwendete Eingangsdaten
| has_nix | nein |
| has_tests | ja |
| lockfiles | — |
| has_dockerfile | nein |
| typed_language | ja |
| bootstrap_files | — |
| has_devcontainer | nein |
| has_linter_config | ja |
| typecheck_configs | — |
| agent_commit_share | 0 |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0 |
AI-Code-Lesbarkeit für Modelle
100Außergewöhnlich
Wie die Bewertung erfolgt
| 45/45 | Typprüfbarer Code — C (statisch typisiert) |
| 55/55 | Handhabbare Dateigrößen — 0/32 Quelldateien über 60 KB |
Verwendete Eingangsdaten
| primary_language | C |
| largest_source_bytes | 40.349 |
| source_files_sampled | 32 |
| oversized_source_files | 0 |
Eckdaten
15GitHub-Sterne
5Mitwirkende
4Commits, letzte 12 Monate
14Tage seit letztem Push
3Releases
1Bus-Faktor
4offene Issues
PyPIPaket-Ökosysteme
Warnungen zur Datenerhebung
- Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
Weitere Details
Stern- und Fork-Verlauf 0 ★ / 7 ⇿
0Sterne
7Forks
3Releases
Wann jeder Stern und Fork hinzugefügt wurde, von GitHub erfasst und nach Tagen gruppiert. Das kumulierte Wachstum steht direkt über den täglichen Zugängen, aus denen es besteht, sodass beide gegeneinander lesbar sind: stetiger organischer Zuwachs sieht ganz anders aus als ein abrupter, kurzlebiger Ausschlag. Wo dieser Unterschied messbar ist, wird er als Wachstumsauthentizität ausgewiesen.
Major 0Minor 2Patch 1
Jeder Punkt umfasst 3 Tage.
OpenSSF Scorecard 3.8 / 10
3.8Gesamtwert
Unabhängige, werkzeugneutrale Sicherheitsbewertung durch das quelloffene OpenSSF Scorecard. Jede Prüfung honoriert eine Sicherheits-Praxis, nicht das Werkzeug eines bestimmten Anbieters. Prüfungen, die Scorecard nicht ermitteln konnte, sind mit k. A. markiert und vom Sicherheitswert ausgeschlossen (nie als null gezählt).
| 10 | Binary-Artifacts | no binaries found in the repo |
| k. A. | Branch-Protection | internal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md |
| 3 | CI-Tests | 4 out of 11 merged PRs checked by a CI test -- score normalized to 3 |
| 0 | CII-Best-Practices | no effort to earn an OpenSSF best practices badge detected |
| 1 | Code-Review | Found 4/21 approved changesets -- score normalized to 1 |
| 10 | Contributors | project has 5 contributing companies or organizations |
| 10 | Dangerous-Workflow | no dangerous workflow patterns detected |
| 0 | Dependency-Update-Tool | no update tool detected |
| 0 | Fuzzing | project is not fuzzed |
| 10 | License | license file detected |
| 1 | Maintained | 2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1 |
| k. A. | Packaging | packaging workflow not detected |
| 0 | Pinned-Dependencies | dependency not pinned by hash detected -- score normalized to 0 |
| 0 | SAST | SAST tool is not run on all commits -- score normalized to 0 |
| 0 | Security-Policy | security policy file not detected |
| k. A. | Signed-Releases | no releases found |
| 0 | Token-Permissions | detected GitHub workflow tokens with excessive permissions |
| 10 | Vulnerabilities | 0 existing vulnerabilities detected |
Direkte Abhängigkeiten 3
| Registry | Paket | Versionsvorgabe | Manifest |
|---|---|---|---|
| PyPI | fmpy | — | pyproject.toml |
| PyPI | numpy | — | pyproject.toml |
| PyPI | pyprecice | >=3.0 | pyproject.toml |
Alle Abhängigkeiten 3
Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 3 direkte und 0 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.
| Registry | Paket | Version | Beziehung |
|---|---|---|---|
| PyPI | fmpy | — | direkt |
| PyPI | numpy | — | direkt |
| PyPI | pyprecice | — | direkt |
Abhängigkeits-Advisories 0
Die Installation von pypi:fmiprecice@0.2.1 zieht 20 Pakete nach sich, direkt und transitiv: 0 tragen bekannte Advisories, davon 0 direkte Abhängigkeiten.
Keine bekannten Advisories betreffen die bewerteten Abhängigkeiten.
Ein Advisory bedeutet, dass die im Abhängigkeitsgraphen erfasste Version in den betroffenen Bereich eines Advisories fällt. Erreichbarkeit wird nicht analysiert, und der Graph enthält Entwicklungs- und Test-Pins — ein Fund kann das Werkzeug betreffen und nicht die ausgelieferte Software.
JSON-Rohbericht maschinenlesbar
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"full_name": "precice/fmi-runner",
"license_state": "standard",
"license_spdx": "LGPL-3.0"
}Bewertungen sind Signale, keine Garantien. Sie spiegeln öffentlich sichtbare Praxis auf GitHub wider — kein Code-Audit und keine Sicherheitsgarantie.
Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v2.3.1, Schema v0.27.0 — vollständige Methodik · Metriken-Wiki.
Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte Statistiken — PyPI.