AI Slop Code Risk Monitor

Finb/Bark

Name: Finb/Bark — Swift Code Quality & Security Review | AI Slop Code Risk Monitor
Creator: AI Slop Code Risk Monitor

Adjusted legacy signal; badge bands: 25 / 50 / 75 (different from dashboard bands).

Bark is an iOS App which allows you to push custom notifications to your iPhone

1.0 Low pattern signal View on GitHub

Open-Source Code Quality, Security and Technical Debt Review

This report summarizes scanner evidence for Finb/Bark, a Swift project with 9,422 GitHub stars. SynthScan examined 17,097 lines of code across 234 source files, recording 16 pattern matches distributed across 5 syntactic categories. The legacy adjusted pattern signal is 1.0 (Low pattern signal); it is not an authorship estimate.

Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.

1.0

Adjusted Signal

1.0

Raw Pattern Score

100%

Time Factor

2026-10-28

Last Push

9.4K

Stars

Swift

Language

17.1K

Lines of Code

234

Files

Pattern Hits

2026-10-31

Scan Date

0.00

HC Hit Rate

Risk Review · Payload v1

Use each vector to guide manual verification of the underlying findings.

Payload schema: 1.0.0 · Engine: 2.0.0 · Scoring: heuristic-density-v1 · Scan date: 2026-10-31.

Cognitive Ownership Risk 3.7/100 · LOW

Can maintainers explain intent, assumptions, and trade-offs? 13 finding(s).

Hallucinated Dependency & Supply Chain Risk 0.0/100 · LOW

Are imports declared and package/API names verified? 0 finding(s).

Agent Over-Engineering Ratio 1.7/100 · LOW

Are wrappers, guards, and repeated structures necessary? 5 finding(s).

PR Review Velocity Impact 5.1/100 · LOW

Which changes need focused human review? 16 finding(s).

What These Metrics Mean

Adjusted Signal: Legacy pattern score normalized per 1,000 scanned lines and multiplied by the repository-level time factor. It is not an estimate of AI authorship.
Raw Pattern Score: Sum of severity- and context-weighted pattern findings before applying the time factor.
Time Factor: A coarse multiplier based on repository last-push metadata. It does not identify when individual code was written.
Pattern Hits: Count of configured pattern findings. Review each location and surrounding code before acting.
HC Hit Rate: High- and critical-severity findings per scanned file. Severity is a configured detector weight, not proof of a vulnerability or authorship.
Lines / Files: Scan coverage denominators used by the legacy normalized score.

Pattern-Signal History

This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.

Severity Breakdown

Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.

CRITICAL 0HIGH 0MEDIUM 1LOW 15

Directory Score Breakdown

This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.

Pattern Findings

The scanner identified 16 distinct pattern matches across 5 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.

Over-Commented Block10 hits · 10 pts

Severity	File	Line	Snippet	Context
LOW	Bark/Intents/PushResponse.swift	1	//	COMMENT
LOW	docs/cert.md	1	当你需要集成Bark到自己的系统或重新实现后端代码时可能需要推送证书	COMMENT
LOW	docs/en-us/cert.md	1	When you need to integrate Bark into your own system or re-implement the backend code, you may need to push certificates	COMMENT
LOW	Common/SharedDefines.swift	1	//	COMMENT
LOW	Common/Operators.swift	1	//	COMMENT
LOW	Common/QRCodeGenerator.swift	1	//	COMMENT
LOW	Model/MessageDeleteTimeRange.swift	1	//	COMMENT
LOW	View/UINavigationItem+Extension.swift	1	//	COMMENT
LOW	View/MessageList/CustomTapTextView.swift	1	//	COMMENT
LOW	…cationServiceExtension/Processor/ImageDownloader.swift	1	//	COMMENT

Excessive Try-Catch Wrapping3 hits · 4 pts

Severity	File	Line	Snippet	Context
LOW	check_unused_translations.py	34	except Exception as e:	CODE
LOW	check_unused_translations.py	83	except Exception as e:	CODE
MEDIUM	…ficationServiceExtension/Processor/CallProcessor.swift	142	print("Error processing CAF file: \(error)")	CODE

Hyper-Verbose Identifiers1 hit · 1 pts

Severity	File	Line	Snippet	Context
LOW	check_unused_translations.py	47	def extract_used_keys_from_file(self, file_path, all_defined_keys):	CODE

Deep Nesting1 hit · 1 pts

Severity	File	Line	Snippet	Context
LOW	check_unused_translations.py	47		CODE

AI Structural Patterns1 hit · 1 pts

Severity	File	Line	Snippet	Context
LOW	check_unused_translations.py	147		CODE