Repository Analysis

uber-go/zap

Blazing fast, structured, leveled logging in Go.

7.7 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of uber-go/zap, a Go project with 24,581 GitHub stars. SynthScan v2.0 examined 25,856 lines of code across 160 source files, recording 198 pattern matches distributed across 2 syntactic categories. The overall adjusted score of 7.7 places this repository in the Low AI signal 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).

7.7
Adjusted Score
7.7
Raw Score
100%
Time Factor
2026-04-28
Last Push
24.6K
Stars
Go
Language
25.9K
Lines of Code
160
Files
198
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 0HIGH 0MEDIUM 0LOW 198

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 198 distinct pattern matches across 2 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 Block196 hits · 196 pts
SeverityFileLineSnippetContext
LOWsink.go1// Copyright (c) 2016-2022 Uber Technologies, Inc.COMMENT
LOWglobal.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWconfig.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWconfig.go41 Thereafter int `json:"thereafter" yaml:"thereafter"`COMMENT
LOWconfig.go61 // level of all loggers descended from this config.COMMENT
LOWconfig.go81 EncoderConfig zapcore.EncoderConfig `json:"encoderConfig" yaml:"encoderConfig"`COMMENT
LOWconfig.go101//COMMENT
LOWconfig.go141// NewProductionConfig builds a reasonable default production loggingCOMMENT
LOWconfig.go181// - The message passed to the log statement.COMMENT
LOWerror.go1// Copyright (c) 2017 Uber Technologies, Inc.COMMENT
LOWexample_test.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWexample_test.go61 )COMMENT
LOWflag.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWincrease_level_test.go1// Copyright (c) 2020 Uber Technologies, Inc.COMMENT
LOWoptions.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWoptions.go161// WithFatalHook sets a CheckWriteHook to run on fatal logs.COMMENT
LOWwriter_test.go1// Copyright (c) 2016-2022 Uber Technologies, Inc.COMMENT
LOWfield.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWfield.go41// Skip constructs a no-op field, which is often useful when handling invalidCOMMENT
LOWfield.go321// any object into the logging context, but it's relatively slow andCOMMENT
LOWfield.go441 return dictObject(val)COMMENT
LOWfield.go461// and then call a function on it.COMMENT
LOWtime.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWencoder.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWsink_windows_test.go1// Copyright (c) 2022 Uber Technologies, Inc.COMMENT
LOWlogger.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWlogger.go181 if len(fields) == 0 {COMMENT
LOWlogger_bench_test.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWsugar_test.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWcommon_test.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWwriter.go1// Copyright (c) 2016-2022 Uber Technologies, Inc.COMMENT
LOWflag_test.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWlevel_test.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWdoc.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWdoc.go21// Package zap provides fast, structured, leveled logging.COMMENT
LOWdoc.go41// variadic number of key-value pairs. (For more advanced use cases, they alsoCOMMENT
LOWdoc.go61//COMMENT
LOWdoc.go81// The simplest way to build a Logger is to use zap's opinionated presets:COMMENT
LOWdoc.go101// # Extending ZapCOMMENT
LOWlevel.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWlevel.go41 ErrorLevel = zapcore.ErrorLevelCOMMENT
LOWlevel.go61COMMENT
LOWhttp_handler_test.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWsink_test.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWleak_test.go1// Copyright (c) 2021 Uber Technologies, Inc.COMMENT
LOWhttp_handler.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWhttp_handler.go41//COMMENT
LOWglobal_test.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWsugar.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWsugar.go41// Unlike the Logger, the SugaredLogger doesn't insist on structured logging.COMMENT
LOWsugar.go81 return &SugaredLogger{base: base}COMMENT
LOWsugar.go101// unsugared.With(COMMENT
LOWsugar.go121// Until that occurs, the logger may retain references to objects inside the fields,COMMENT
LOWencoder_test.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWstacktrace_ext_test.go1// Copyright (c) 2016, 2017 Uber Technologies, Inc.COMMENT
LOWerror_test.go1// Copyright (c) 2017 Uber Technologies, Inc.COMMENT
LOWconfig_test.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWtime_test.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWarray.go1// Copyright (c) 2016 Uber Technologies, Inc.COMMENT
LOWarray.go101// Note that these objects must implement zapcore.ObjectMarshaler directly.COMMENT
136 more matches not shown…
Fake / Example Data2 hits · 2 pts
SeverityFileLineSnippetContext
LOWlogger_bench_test.go49 Name: "Jane Doe",CODE
LOWbenchmarks/zap_test.go53 Name: "Jane Doe",CODE