Repository Analysis

tisfeng/Easydict

一个简洁优雅的词典翻译 macOS App。开箱即用,支持离线 OCR 识别,支持有道词典,🍎 苹果系统词典,🍎 苹果系统翻译,OpenAI,Gemini,DeepL,Google,Bing,腾讯,百度,阿里,小牛,彩云和火山翻译。A concise and elegant Dictionary and Translator macOS App for looking up words and translating text.

4.9 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of tisfeng/Easydict, a Swift project with 13,844 GitHub stars. SynthScan v2.0 examined 141,302 lines of code across 963 source files, recording 641 pattern matches distributed across 13 syntactic categories. The overall adjusted score of 4.9 places this repository in the Likely human-written band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

4.9
Adjusted Score
4.9
Raw Score
100%
Time Factor
2026-07-13
Last Push
13.8K
Stars
Swift
Language
141.3K
Lines of Code
963
Files
641
Pattern Hits
2026-07-14
Scan Date
0.00
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 1HIGH 0MEDIUM 9LOW 631

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 641 distinct pattern matches across 13 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. 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 diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Over-Commented Block585 hits · 578 pts
SeverityFileLineSnippetContext
LOW.swiftlint.yml1#COMMENT
LOWEasydict/App/AppDelegate.h1//COMMENT
LOWEasydict/App/entry.h1//COMMENT
LOWEasydict/App/AppDelegate+EZURLScheme.m1//COMMENT
LOWEasydict/App/Easydict-Bridging-Header.h1//COMMENT
LOWEasydict/App/Easydict-Bridging-Header.h21#import "EZOCRResult.h"COMMENT
LOWEasydict/App/entry.m1//COMMENT
LOWEasydict/Swift/Model/AppTriggerConfig.swift1//COMMENT
LOWEasydict/Swift/Model/DetectManager.swift1//COMMENT
LOWEasydict/Swift/View/TapHandlerView.swift1//COMMENT
LOW…nfigurationView/CodexCLIServiceConfigurationView.swift1//COMMENT
LOW…ingView/Tabs/ServiceConfigurationView/SliderCell.swift1//COMMENT
LOW…igurationView/ClaudeCodeServiceConfigurationView.swift1//COMMENT
LOW…t/View/SettingView/Tabs/Model/QueryRecordManager.swift1//COMMENT
LOWEasydict/Swift/View/Window/HostWindowManager.swift1//COMMENT
LOW…ature/Shortcut/Model/ShortcutConfictAlertMessage.swift1//COMMENT
LOW…Feature/Screenshot/Screenshot/NSScreen+Extention.swift1//COMMENT
LOW…t/Feature/Screenshot/Screencapture/Screencapture.swift1//COMMENT
LOWEasydict/Swift/Feature/Markdown/MarkdownLabel.swift1//COMMENT
LOW…dict/Swift/Feature/Markdown/MarkdownToggleButton.swift1//COMMENT
LOWEasydict/Swift/Feature/Markdown/MarkdownRenderer.swift1//COMMENT
LOWEasydict/Swift/Feature/HTTPServer/Vapor/configure.swift1//COMMENT
LOW…t/Swift/Feature/HTTPServer/Vapor/DictionaryEntry.swift1//COMMENT
LOWEasydict/Swift/Service/Apple/AppleLanguageMapper.swift61 return languageCOMMENT
LOW…pple/AppleLanguageDetector/AppleLanguageDetector.swift1//COMMENT
LOW…pple/AppleLanguageDetector/AppleLanguageDetector.swift21/// - Hierarchical post-processing: Chinese/English verification → Mixed content → Short text correctionsCOMMENT
LOW…pple/AppleLanguageDetector/AppleLanguageDetector.swift41///COMMENT
LOW…pple/AppleLanguageDetector/AppleLanguageDetector.swift81 /// - Note: These probabilities may be an empty dictionary for certain edge casesCOMMENT
LOW…pple/AppleLanguageDetector/AppleLanguageDetector.swift341 if text.trimmingCharacters(in: .whitespacesAndNewlines).isNumeric {COMMENT
LOW…pple/AppleLanguageDetector/AppleLanguageDetector.swift401 /// - Non-Latin scripts (Chinese, Japanese, Korean, Arabic, Cyrillic, etc.)COMMENT
LOW…pple/AppleLanguageDetector/AppleLanguageDetector.swift481 /// Handle corrections for short text and obvious misdetectionsCOMMENT
LOW…ift/Service/Apple/AppleOCREngine/OCRLineAnalyzer.swift1//COMMENT
LOW…ift/Service/Apple/AppleOCREngine/OCRLineAnalyzer.swift21 // MARK: LifecycleCOMMENT
LOW…eOCREngine/VNRecognizedTextObservation+Extension.swift1//COMMENT
LOW…eOCREngine/VNRecognizedTextObservation+Extension.swift161COMMENT
LOW…wift/Service/Apple/AppleOCREngine/AppleOCREngine.swift1//COMMENT
LOW…wift/Service/Apple/AppleOCREngine/AppleOCREngine.swift21/// the initial image to delegating the final text processing to `OCRTextProcessor`.COMMENT
LOW…wift/Service/Apple/AppleOCREngine/AppleOCREngine.swift181COMMENT
LOW…t/Service/Apple/AppleOCREngine/OCRTextNormalizer.swift1//COMMENT
LOW…t/Service/Apple/AppleOCREngine/OCRTextNormalizer.swift41COMMENT
LOW…t/Service/Apple/AppleOCREngine/OCRTextNormalizer.swift101 // Update metrics language when setCOMMENT
LOW…t/Service/Apple/AppleOCREngine/OCRTextNormalizer.swift121 /// - **Code patterns**: `array.map()` → `〈PROTECTED_4〉`COMMENT
LOW…t/Service/Apple/AppleOCREngine/OCRTextNormalizer.swift141 /// "〈PROTECTED_1〉": "test@example.com",COMMENT
LOW…t/Service/Apple/AppleOCREngine/OCRTextNormalizer.swift241 /// Input text: "访问 〈PROTECTED_0〉,邮箱 〈PROTECTED_1〉。价格 $〈PROTECTED_2〉,代码 〈PROTECTED_3〉。"COMMENT
LOW…t/Service/Apple/AppleOCREngine/OCRTextNormalizer.swift321 ///COMMENT
LOW…t/Service/Apple/AppleOCREngine/OCRTextNormalizer.swift461 result.replace(western, with: chinese)COMMENT
LOW…t/Service/Apple/AppleOCREngine/OCRTextNormalizer.swift481 /// 6..<21 (test@example.com - email pattern)COMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRSectionMerger.swift1//COMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRSectionMerger.swift21class OCRSectionMerger {COMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRSectionMerger.swift181 /// a new paragraph, or handle hyphenated words.COMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRSectionMerger.swift201 paragraphObservations: paragraphObservations,COMMENT
LOW…wift/Service/Apple/AppleOCREngine/OCRDashHandler.swift1//COMMENT
LOW…wift/Service/Apple/AppleOCREngine/OCRDashHandler.swift21///COMMENT
LOW…t/Service/Apple/AppleOCREngine/OCRPoetryDetector.swift181 if let last = text.last, let scalar = last.unicodeScalars.first {COMMENT
LOW…Swift/Service/Apple/AppleOCREngine/OCRBandMerger.swift1//COMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRTextProcessor.swift1//COMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRTextProcessor.swift21/// 1. **Language Detection**: Determines the language of the recognized text.COMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRMergeAnalyzer.swift1//COMMENT
LOW…vice/Apple/AppleOCREngine/Model/OCRMergeStrategy.swift1//COMMENT
LOW…rvice/Apple/AppleOCREngine/Model/ConfidenceLevel.swift1//COMMENT
525 more matches not shown…
Verbosity Indicators13 hits · 28 pts
SeverityFileLineSnippetContext
LOW…ft/Service/Apple/AppleOCREngine/OCRTextProcessor.swift82 // Step 1: Detect and process bandsCOMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRTextProcessor.swift89 // Step 2: Process all bands and sectionsCOMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRTextProcessor.swift97 // Step 3: Store results and calculate confidenceCOMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRTextProcessor.swift104 // Step 4: Merge bands and finalize resultCOMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRTextProcessor.swift332 // Step 1: Group observations into horizontal bandsCOMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRTextProcessor.swift336 // Step 2: Analyze each band group to determine its column structureCOMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRTextProcessor.swift345 // Step 3: Sort each section's observations for reading orderCOMMENT
LOWEasydict/Swift/Service/Volcano/VolcanoSigning.swift42 // Step 1: Create a canonical requestCOMMENT
LOWEasydict/Swift/Service/Volcano/VolcanoSigning.swift54 // Step 2: Create string to signCOMMENT
LOWEasydict/Swift/Service/Volcano/VolcanoSigning.swift66 // Step 3: Calculate the signatureCOMMENT
LOWEasydict/Swift/Service/Volcano/VolcanoSigning.swift73 // Step 4: Add the signature to the requestCOMMENT
LOW…ict/Swift/Utility/ChineseGenre/MetadataExtractor.swift50 // Step 1: Extract formatted text with markersCOMMENT
LOW…ict/Swift/Utility/ChineseGenre/MetadataExtractor.swift53 // Step 2: Extract clean contentCOMMENT
Structural Annotation Overuse13 hits · 28 pts
SeverityFileLineSnippetContext
LOW…ft/Service/Apple/AppleOCREngine/OCRTextProcessor.swift82 // Step 1: Detect and process bandsCOMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRTextProcessor.swift89 // Step 2: Process all bands and sectionsCOMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRTextProcessor.swift97 // Step 3: Store results and calculate confidenceCOMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRTextProcessor.swift104 // Step 4: Merge bands and finalize resultCOMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRTextProcessor.swift332 // Step 1: Group observations into horizontal bandsCOMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRTextProcessor.swift336 // Step 2: Analyze each band group to determine its column structureCOMMENT
LOW…ft/Service/Apple/AppleOCREngine/OCRTextProcessor.swift345 // Step 3: Sort each section's observations for reading orderCOMMENT
LOWEasydict/Swift/Service/Volcano/VolcanoSigning.swift42 // Step 1: Create a canonical requestCOMMENT
LOWEasydict/Swift/Service/Volcano/VolcanoSigning.swift54 // Step 2: Create string to signCOMMENT
LOWEasydict/Swift/Service/Volcano/VolcanoSigning.swift66 // Step 3: Calculate the signatureCOMMENT
LOWEasydict/Swift/Service/Volcano/VolcanoSigning.swift73 // Step 4: Add the signature to the requestCOMMENT
LOW…ict/Swift/Utility/ChineseGenre/MetadataExtractor.swift50 // Step 1: Extract formatted text with markersCOMMENT
LOW…ict/Swift/Utility/ChineseGenre/MetadataExtractor.swift53 // Step 2: Extract clean contentCOMMENT
Excessive Try-Catch Wrapping4 hits · 10 pts
SeverityFileLineSnippetContext
MEDIUM…fireworks-tech-graph/scripts/generate-from-template.py1542 print(f"Error: {exc}")CODE
MEDIUM…fireworks-tech-graph/scripts/generate-from-template.py1545 print(f"Error: Invalid JSON: {exc}")CODE
MEDIUM…fireworks-tech-graph/scripts/generate-from-template.py1548 print(f"Error: {exc}")CODE
LOW…fireworks-tech-graph/scripts/generate-from-template.py1550 except Exception as exc: # pragma: no coverCODE
Modern AI Meta-Vocabulary4 hits · 10 pts
SeverityFileLineSnippetContext
MEDIUM.agents/skills/fireworks-tech-graph/SKILL.md303| Async / event | gray `#6b7280` | 1.5px | `4,2` | Non-blocking, event-driven |CODE
MEDIUM.agents/skills/fireworks-tech-graph/references/icons.md161### AI Memory & RAG ProductsCOMMENT
MEDIUM…s/fireworks-tech-graph/references/style-1-flat-icon.md84 <text x="36" y="28" fill="#6b7280" font-size="12">RAG flow</text>CODE
MEDIUMPods/ReactiveObjC/README.md250### Handling asynchronous or event-driven data sourcesCOMMENT
Hallucination Indicators1 hit · 10 pts
SeverityFileLineSnippetContext
CRITICALPods/Masonry/README.md223make.left.right.and.bottom.equalTo(superview);CODE
Hyper-Verbose Identifiers9 hits · 9 pts
SeverityFileLineSnippetContext
LOW…ict/Swift/Service/Dictionary/MDict/MDictEntryScript.js81function changeWebViewBodyFontSize(fontSizeRatio) {CODE
LOW…ict/Swift/Service/Dictionary/MDict/MDictEntryScript.js85function updateWebViewContentStyle() {CODE
LOW…/Dictionary/DictionaryRendering/dictionary-result.html109 function calculateSummaryTextWidth(summary) {CODE
LOW…/Dictionary/DictionaryRendering/dictionary-result.html115 function updateDetailsSummaryLineWidth() {CODE
LOW…/Dictionary/DictionaryRendering/dictionary-result.html187 function isNotCustomBackgroundColor(color) {CODE
LOW…/Dictionary/DictionaryRendering/dictionary-result.html255 function updateAllIframeAppleSystemLabelBorderBottomColor(isDark) {CODE
LOW…fireworks-tech-graph/scripts/generate-from-template.py569def render_blueprint_title_block(CODE
LOW…fireworks-tech-graph/scripts/generate-from-template.py1023def label_position_candidates(points: Sequence[Point]) -> List[Point]:CODE
LOW…fireworks-tech-graph/scripts/generate-from-template.py1048def choose_label_position_avoiding(points: Sequence[Point], text: str, occupied: Sequence[Bounds]) -> Point:CODE
Redundant / Tautological Comments3 hits · 4 pts
SeverityFileLineSnippetContext
LOW…skills/fireworks-tech-graph/scripts/test-all-styles.sh46 # Check if style reference existsCOMMENT
LOW.github/workflows/star_fork_notification.yml70 # Check if user meets the shared notification criteria for fork/star events.COMMENT
LOW.github/workflows/star_fork_notification.yml85 # Output resultCOMMENT
Fake / Example Data3 hits · 4 pts
SeverityFileLineSnippetContext
LOW…ler/Window/BaseQueryWindow/EZBaseQueryViewController.m1442 NSString *placeholderText = NSLocalizedString(@"placeholder", nil);CODE
LOWEasydict/objc/ViewController/View/TextView/EZTextView.m84 _placeholderText = @"placeholder";CODE
LOWEasydictTests/Utility/Regex/RegexTests.swift78 #expect("user@example.com".contains(regex))COMMENT
AI Slop Vocabulary1 hit · 3 pts
SeverityFileLineSnippetContext
MEDIUM…ct/Swift/Utility/Extensions/String/String+Detect.swift20 /// This approach is robust against mixed content with a few Traditional characters.COMMENT
Unused Imports3 hits · 3 pts
SeverityFileLineSnippetContext
LOW…fireworks-tech-graph/scripts/generate-from-template.py13CODE
LOW…fireworks-tech-graph/scripts/generate-from-template.py17CODE
LOW…fireworks-tech-graph/scripts/generate-from-template.py22CODE
Slop Phrases1 hit · 3 pts
SeverityFileLineSnippetContext
MEDIUM.github/workflows/codeql.yml54 # If you are analyzing a compiled language, you can modify the 'build-mode' for that language to customize howCOMMENT
Deep Nesting1 hit · 1 pts
SeverityFileLineSnippetContext
LOW…fireworks-tech-graph/scripts/generate-from-template.py1111CODE