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Software-GesundheitsberichtSchema 0.27.0 · Metriken 1.13.0 · 2026-08-01 16:25 UTC

precice / aste

Artificial Solver Testing Environment

Python · C++GPL-3.0★ 11 Sterne⑂ 15 Forksseit Okt. 2015Auf GitHub ansehen ↗

precice/aste erreicht einen Gesundheitsindex von 64 von 100 und liegt damit im Bereich Mittel. Am stärksten schneidet es bei Engineering Quality (86/100) ab, am schwächsten bei AI Readiness (43/100). Zuletzt vor 5 Tagen aktualisiert. 2 Mitwirkende tragen den Großteil der jüngsten Arbeit.

64
gesamt / 100
Mittel

Software-Gesundheitsindex

Metriken werden auf einer Skala von 1–100 in gewichtete Kategorien gruppiert. Der Gesamtwert beginnt als ihr Mittel; sobald öffentliche Evidenz die Richtlinie für Hochrisikojurisdiktionen auslöst, wird die Bewertung angepasst und erhält die Obergrenze 49 (Gefährdet). AI Readiness liegt außerhalb.

64
Exzellent85-100Vorbildlich; erfüllt im Wesentlichen alle geprüften Kriterien
Gut70-84Gesund; geringfügige Lücken
Mittel50-69Akzeptabel mit deutlichen Lücken; Überprüfung empfohlen
Gefährdet30-49Erhebliche Schwächen; eine Übernahme erfordert Vorsicht
Kritisch1-29Schwerwiegende Probleme (aufgegeben, nur ein Maintainer, keine Hygiene)
VitalitätCommunity &VerbreitungNachhaltigkeit &GovernanceEngineering-QualitätSicherheitAI Readiness

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.

Eigentümerschaft

preCICEOrganisation
257 Follower64 öffentliche Reposseit Mai 2015

Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.

Metriken nach Kategorie

Vitalität

Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?

54Mittel · 22 % des Gesamtindex
Wie die Bewertung erfolgt
36/36Push-Aktualität — letzter Push vor 5 Tagen
5.5/36Commit-Rhythmus — 8/52 Wochen mit Commits
11.3/18Commit-Volumen — 17 Commits im letzten Jahr
2/10OpenSSF Scorecard: Maintained — 3 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 2
Verwendete Eingangsdaten
commits_last_year17
human_commit_share0,95
days_since_last_push5
active_weeks_last_year8
Wie die Bewertung erfolgt
27/27Liefert Releases aus — 4 Releases veröffentlicht
7.2/36Release-Aktualität — letztes Release vor 632 Tagen
12.6/27Release-Rhythmus — ein Release etwa alle 254,7 Tage
0/10OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count4
latest_release_tagv3.3.0
releases_from_tagsnein
days_since_latest_release632
mean_days_between_releases254,7
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?

58Mittel · 18 % des Gesamtindex
Wie die Bewertung erfolgt
16.2/60Stars — 11 Stars
9.6/25Forks — 15 Forks
2.7/15Watcher — 4 Watcher
Verwendete Eingangsdaten
forks15
stars11
watchers4
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Wie die Bewertung erfolgt
22.5/22.5README
22.5/22.5Lizenz — anerkannte Lizenz (GPL-3.0)
18/18CONTRIBUTING-Leitfaden
13.5/13.5Verhaltenskodex
0/7.2Issue-Vorlage
6.3/6.3PR-Vorlage
Verwendete Eingangsdaten
has_readmeja
has_licenseja
has_contributingja
has_issue_templatenein
has_code_of_conductja
has_pull_request_templateja

Nachhaltigkeit & Governance

Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?

68Mittel · 24 % des Gesamtindex
Wie die Bewertung erfolgt
25.2/54Bus-Faktor — 2 Beitragende decken die Hälfte aller Commits ab
14.3/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 36 % der Commits
13.5/13.5Breite der Beitragenden — 12 Beitragende
10/10OpenSSF Scorecard: Contributors — project has 6 contributing companies or organizations
Verwendete Eingangsdaten
bus_factor2
contributors_sampled12
top_contributor_share0,363
Wie die Bewertung erfolgt
31.4/46.8Issue-Lösungsquote — 67 % der Issues geschlossen
34.9/38.3PR-Annahme — 166/182 entschiedene PRs gemergt
3/15OpenSSF Scorecard: Code-Review — Found 5/21 approved changesets -- score normalized to 2
Verwendete Eingangsdaten
merged_prs166
open_issues22
closed_issues45
issue_closed_ratio0,672
closed_unmerged_prs16
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
17.3/25Reichweite des Inhabers — 257 Follower von precice
25/25Kontohistorie — 64 öffentliche Repos, Kontoalter ca. 11 Jahre
Verwendete Eingangsdaten
followers257
owner_typeOrganization
is_verified
owner_loginprecice
public_repos64
account_age_days4.089

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

86Exzellent · 20 % des Gesamtindex
Wie die Bewertung erfolgt
24/24CI-Workflows — 5 Workflow(s)
24/24Tests vorhanden
16/16Linter-Konfiguration
9.6/9.6Pre-Commit-Hooks
0/6.4.editorconfig
10/20OpenSSF Scorecard: CI-Tests — 12 out of 21 merged PRs checked by a CI test -- score normalized to 5
Verwendete Eingangsdaten
has_cija
has_testsja
has_editorconfignein
has_linter_configja
has_precommit_configja

Dokumentation

90Exzellent
Wie die Bewertung erfolgt
30/30README
25/25Dokumentationsverzeichnis
15/15Dokumentations-/Homepage-Site — https://precice.org/tooling-aste.html
10/10Repository-Beschreibung
0/10Topics
10/10Wiki
Verwendete Eingangsdaten
topics
has_wikija
homepagehttps://precice.org/tooling-aste.html
has_readmeja
has_docs_dirja
has_descriptionja

Sicherheit

Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?

49Gefährdet · 16 % des Gesamtindex

Sicherheitslage

49Gefährdet
Wie die Bewertung erfolgt
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — keine Daten
1.2/2.5CI-Tests — 12 out of 21 merged PRs checked by a CI test -- score normalized to 5
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
1.5/7.5Code-Review — Found 5/21 approved changesets -- score normalized to 2
2.5/2.5Contributors — project has 6 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5Lizenz — license file detected
1.5/7.5Maintained — 3 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 2
0/5Packaging — keine Daten
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — keine Daten
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Verwendete Eingangsdaten
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate4,9
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): branch_protection, packaging, signed_releases. Die verbleibenden Gewichte wurden renormalisiert.

AI Readiness

Wie gut ist das Repository dafür ausgestattet, mit KI-Coding-Agenten entwickelt und gepflegt zu werden? Ein unabhängiges, experimentelles Badge — Gewicht 0,0, es wird eigenständig ausgewiesen und verändert den Gesamt-Gesundheitswert nicht.

43Gefährdet · 0 % des Gesamtindex
Wie die Bewertung erfolgt
0/45Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln
0/15Maschinenlesbare Doku (llms.txt)
18.5/40Lesbare Commit-Historie — 33 von 95 menschlichen Commits benennen ihre Absicht (strukturierter Betreff oder erläuternder Text)
Verwendete Eingangsdaten
has_llms_txtnein
legible_history_share0,347
agent_instruction_files
agent_instruction_max_bytes
Wie die Bewertung erfolgt
18/18Bootstrap mit einem Befehl — tools/mesh-tools/makefile
22/22Automatisierte Tests
11/11Lint-/Format-Konfiguration
0/11Statische Typprüfung
0/10Reproduzierbare Umgebung
0/10Belegte Agentenpraxis — keine von Agenten verfassten Commits unter den letzten 100
8/8Automatisierte Wartung — 5 der letzten 100 Commits sind automatisierte Abhängigkeits-Updates
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Verwendete Eingangsdaten
has_nixnein
has_testsja
lockfiles
has_dockerfilenein
typed_languagenein
bootstrap_filestools/mesh-tools/makefile
has_devcontainernein
has_linter_configja
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0,05
Wie die Bewertung erfolgt
0/45Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration
53.5/55Handhabbare Dateigrößen — 1/37 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languagePython
largest_source_bytes919.975
source_files_sampled37
oversized_source_files1
Wie die Bewertung erfolgt
0/40API-Schema (OpenAPI/GraphQL/proto)
0/20MCP-Server
40/40Lauffähige Beispiele — examples
Verwendete Eingangsdaten
example_dirsexamples
has_mcp_signalnein
api_schema_files

Eckdaten

11GitHub-Sterne
12Mitwirkende
17Commits, letzte 12 Monate
5Tage seit letztem Push
4Releases
2Bus-Faktor
22offene Issues
PyPIPaket-Ökosysteme

Warnungen zur Datenerhebung

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • No resolved dependencies carried a version and a supported ecosystem

Weitere Details

Stern- und Fork-Verlauf 0 ★ / 15 ⇿
0Sterne
15Forks
4Releases

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.

03581013151522017-072022-012026-06
Major 1Minor 3Patch 0

Jeder Punkt umfasst 9 Tage.

OpenSSF Scorecard 4.9 / 10
4.9Gesamtwert

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).Scorecard v5.5.0 · 2026-08-01 16:25 UTC

10Binary-Artifactsno binaries found in the repo
k. A.Branch-Protectioninternal 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
5CI-Tests12 out of 21 merged PRs checked by a CI test -- score normalized to 5
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
2Code-ReviewFound 5/21 approved changesets -- score normalized to 2
10Contributorsproject has 6 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
2Maintained3 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 2
k. A.Packagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
k. A.Signed-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Alle Abhängigkeiten 9

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 0 direkte und 9 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
PyPIgmshindirekt
PyPIjinja2indirekt
PyPImatplotlibindirekt
PyPImeshioindirekt
PyPInumpyindirekt
PyPIpolarsindirekt
PyPIprecice-profilingindirekt
PyPIscipyindirekt
PyPIsympyindirekt
Abhängigkeits-Advisories nicht bewertet

Der Advisory-Abgleich konnte für diesen Bericht nicht ausgeführt werden: No resolved dependencies carried a version and a supported ecosystem

JSON-Rohbericht maschinenlesbar
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                "key": "pr_acceptance",
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                "key": "openssf_scorecard_code_review",
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                "key": "owner_reach",
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        ],
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        "key": "security",
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                "key": "fuzzing",
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                "key": "vulnerabilities",
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  "license_spdx": "GPL-3.0"
}

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Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v1.13.0, Schema v0.27.0 — vollständige Methodik · Metriken-Wiki.

Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte Statistiken.