Repository Analysis

apache/parquet-java

Apache Parquet Java

4.3 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of apache/parquet-java, a Java project with 3,065 GitHub stars. SynthScan v2.0 examined 207,873 lines of code across 937 source files, recording 102 pattern matches distributed across 5 syntactic categories. The overall adjusted score of 4.3 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).

4.3
Adjusted Score
4.3
Raw Score
100%
Time Factor
2026-07-14
Last Push
3.1K
Stars
Java
Language
207.9K
Lines of Code
937
Files
102
Pattern Hits
2026-07-14
Scan Date
0.08
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

Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.

No multi-scan history yet — run the scanner again to build trend data.

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 71HIGH 0MEDIUM 10LOW 21

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 102 distinct pattern matches across 5 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.

Hallucination Indicators71 hits · 838 pts
SeverityFileLineSnippetContext
CRITICAL…a/org/apache/parquet/avro/TestAvroRecordConverter.java150 // MODEL$.addLogicalTypeConversion(new org.apache.avro.data.TimeConversions.TimestampMillisConversion());COMMENT
CRITICAL…a/org/apache/parquet/avro/TestAvroRecordConverter.java151 // MODEL$.addLogicalTypeConversion(new org.apache.avro.data.TimeConversions.TimeMillisConversion());COMMENT
CRITICAL…a/org/apache/parquet/avro/TestAvroRecordConverter.java155 new org.apache.avro.data.TimeConversions.DateConversion(),CODE
CRITICAL…a/org/apache/parquet/avro/TestAvroRecordConverter.java156 new org.apache.avro.data.TimeConversions.TimeMillisConversion(),CODE
CRITICAL…a/org/apache/parquet/avro/TestAvroRecordConverter.java185 // MODEL$.addLogicalTypeConversion(new org.apache.avro.data.TimeConversions.DateConversion());COMMENT
CRITICAL…a/org/apache/parquet/avro/TestAvroRecordConverter.java186 // MODEL$.addLogicalTypeConversion(new org.apache.avro.data.TimeConversions.TimestampMillisConversion());COMMENT
CRITICAL…a/org/apache/parquet/avro/TestAvroRecordConverter.java187 // MODEL$.addLogicalTypeConversion(new org.apache.avro.data.TimeConversions.TimeMillisConversion());COMMENT
CRITICAL…a/org/apache/parquet/avro/TestAvroRecordConverter.java191 new org.apache.avro.data.TimeConversions.TimestampMillisConversion(), null, nullCODE
CRITICAL…t/java/org/apache/parquet/avro/TestStringBehavior.java178 avroFile, new SpecificDatumReader<>(org.apache.parquet.avro.StringBehaviorTest.getClassSchema()))) {CODE
CRITICAL…t/java/org/apache/parquet/avro/TestStringBehavior.java359 AvroReadSupport.setAvroReadSchema(conf, org.apache.parquet.avro.StringBehaviorTest.getClassSchema());CODE
CRITICAL…/thrift/TestParquetToThriftReadWriteAndProjection.java295 new RequiredListFixture(Arrays.asList(new org.apache.parquet.thrift.test.Name("first_name")));CODE
CRITICAL…/thrift/TestParquetToThriftReadWriteAndProjection.java309 Arrays.asList(new org.apache.parquet.thrift.test.Name("first_name"))));CODE
CRITICAL…quet/filter2/statisticslevel/TestStatisticsFilter.java114 org.apache.parquet.column.statistics.Statistics.getBuilderForReading(CODE
CRITICAL…quet/filter2/statisticslevel/TestStatisticsFilter.java119 org.apache.parquet.column.statistics.Statistics.getBuilderForReading(CODE
CRITICAL…quet/filter2/statisticslevel/TestStatisticsFilter.java400 org.apache.parquet.column.statistics.Statistics.getBuilderForReading(CODE
CRITICAL…quet/filter2/statisticslevel/TestStatisticsFilter.java745 org.apache.parquet.column.statistics.Statistics.getBuilderForReading(CODE
CRITICAL…quet/filter2/statisticslevel/TestStatisticsFilter.java798 org.apache.parquet.column.statistics.Statistics.getBuilderForReading(ieee754Type)CODE
CRITICAL…quet/filter2/statisticslevel/TestStatisticsFilter.java953 org.apache.parquet.column.statistics.Statistics.getBuilderForReading(FLOAT16_TYPE)CODE
CRITICAL…quet/filter2/statisticslevel/TestStatisticsFilter.java982 org.apache.parquet.column.statistics.Statistics.getBuilderForReading(FLOAT16_TYPE)CODE
CRITICAL…quet/filter2/statisticslevel/TestStatisticsFilter.java1013 org.apache.parquet.column.statistics.Statistics.getBuilderForReading(FLOAT16_TYPE)CODE
CRITICAL…quet/filter2/dictionarylevel/DictionaryFilterTest.java649 org.apache.parquet.column.statistics.Statistics.getBuilderForReading(type)CODE
CRITICAL…a/org/apache/parquet/hadoop/TestParquetFileWriter.java154 org.apache.parquet.column.statistics.Statistics.getBuilderForReading(CODE
CRITICAL…a/org/apache/parquet/hadoop/TestParquetFileWriter.java870 org.apache.parquet.format.converter.ParquetMetadataConverter.toParquetStatistics(parquetMRstats);CODE
CRITICAL…a/org/apache/parquet/hadoop/TestParquetFileWriter.java872 (LongStatistics) org.apache.parquet.format.converter.ParquetMetadataConverter.fromParquetStatistics(CODE
CRITICAL…a/org/apache/parquet/hadoop/TestParquetFileWriter.java1144 return org.apache.parquet.column.statistics.Statistics.getBuilderForReading(col.getPrimitiveType())CODE
CRITICAL…a/org/apache/parquet/hadoop/TestParquetFileWriter.java1503 org.apache.parquet.column.statistics.Statistics.createStats(C1.getPrimitiveType());CODE
CRITICAL…a/org/apache/parquet/hadoop/TestParquetFileWriter.java1516 org.apache.parquet.column.statistics.Statistics.createStats(C2.getPrimitiveType());CODE
CRITICAL…he/parquet/hadoop/TestInputFormatColumnProjection.java127 org.apache.hadoop.mapreduce.JobContext.class.isInterface());CODE
CRITICAL…g/apache/parquet/hadoop/DeprecatedInputFormatTest.java124 org.apache.hadoop.mapred.TextOutputFormat.setOutputPath(jobConf, outputPath);CODE
CRITICAL…g/apache/parquet/hadoop/DeprecatedInputFormatTest.java246 org.apache.hadoop.mapred.FileInputFormat.setInputPaths(conf, new Path(inputDir.toURI()));CODE
CRITICAL…g/apache/parquet/hadoop/DeprecatedInputFormatTest.java247 org.apache.hadoop.mapred.TextOutputFormat.setOutputPath(conf, new Path(outputDir.toURI()));CODE
CRITICAL…ache/parquet/hadoop/example/TestInputOutputFormat.java352 Method getGroup = org.apache.hadoop.mapreduce.Counters.class.getMethod("getGroup", String.class);CODE
CRITICAL…ache/parquet/hadoop/example/TestInputOutputFormat.java354 Method findCounter = org.apache.hadoop.mapreduce.CounterGroup.class.getMethod("findCounter", String.class);CODE
CRITICAL…ache/parquet/hadoop/example/TestInputOutputFormat.java356 Method getValue = org.apache.hadoop.mapreduce.Counter.class.getMethod("getValue");CODE
CRITICAL…uet/format/converter/TestParquetMetadataConverter.java473 for (org.apache.parquet.column.Encoding encoding : org.apache.parquet.column.Encoding.values()) {CODE
CRITICAL…uet/format/converter/TestParquetMetadataConverter.java477 for (org.apache.parquet.format.Encoding encoding : org.apache.parquet.format.Encoding.values()) {CODE
CRITICAL…uet/format/converter/TestParquetMetadataConverter.java661 new org.apache.parquet.hadoop.metadata.FileMetaData(CODE
CRITICAL…uet/format/converter/TestParquetMetadataConverter.java688 new org.apache.parquet.hadoop.metadata.FileMetaData(CODE
CRITICAL…uet/format/converter/TestParquetMetadataConverter.java767 new HashSet<>(Arrays.asList(org.apache.parquet.column.Encoding.values()));CODE
CRITICAL…uet/format/converter/TestParquetMetadataConverter.java1326 new org.apache.parquet.hadoop.metadata.FileMetaData(schema, new HashMap<String, String>(), null);CODE
CRITICAL…uet/format/converter/TestParquetMetadataConverter.java1394 new org.apache.parquet.hadoop.metadata.FileMetaData(schema, new HashMap<String, String>(), null);CODE
CRITICAL…uet/format/converter/TestParquetMetadataConverter.java1832 new org.apache.parquet.column.statistics.geospatial.BoundingBox(CODE
CRITICAL…uet/format/converter/TestParquetMetadataConverter.java1845 new org.apache.parquet.column.statistics.geospatial.GeospatialStatistics(bbox, geospatialTypes);CODE
CRITICAL…uet/format/converter/TestParquetMetadataConverter.java1914 new org.apache.parquet.column.statistics.geospatial.GeospatialStatistics(null, geospatialTypes);CODE
CRITICAL…uet/format/converter/TestParquetMetadataConverter.java1945 new org.apache.parquet.column.statistics.geospatial.BoundingBox(CODE
CRITICAL…uet/format/converter/TestParquetMetadataConverter.java1956 new org.apache.parquet.column.statistics.geospatial.GeospatialStatistics(invalidBbox, null);CODE
CRITICAL…uet/format/converter/TestParquetMetadataConverter.java1994 new org.apache.parquet.hadoop.metadata.FileMetaData(schema, new HashMap<String, String>(), null);CODE
CRITICAL…uet/format/converter/TestParquetMetadataConverter.java2031 new org.apache.parquet.hadoop.metadata.FileMetaData(schema, new HashMap<String, String>(), null);CODE
CRITICAL…org/apache/parquet/hadoop/rewrite/ParquetRewriter.java811 org.apache.parquet.column.statistics.Statistics.getBuilderForReading(type);CODE
CRITICAL…/java/org/apache/parquet/hadoop/codec/CodecConfig.java160 return org.apache.hadoop.mapred.FileOutputFormat.getCompressOutput(conf);CODE
CRITICAL…/java/org/apache/parquet/hadoop/codec/CodecConfig.java165 return org.apache.hadoop.mapred.FileOutputFormat.getOutputCompressorClass(conf, defaultCodec);CODE
CRITICAL…/org/apache/parquet/hadoop/codec/Lz4RawCompressor.java31 return io.airlift.compress.lz4.Lz4RawCompressor.maxCompressedLength(byteSize);CODE
CRITICAL…parquet/format/converter/ParquetMetadataConverter.java381 return org.apache.parquet.format.TimeUnit.MICROS(new MicroSeconds());CODE
CRITICAL…parquet/format/converter/ParquetMetadataConverter.java383 return org.apache.parquet.format.TimeUnit.MILLIS(new MilliSeconds());CODE
CRITICAL…parquet/format/converter/ParquetMetadataConverter.java764 return org.apache.parquet.column.Encoding.valueOf(encoding.name());CODE
CRITICAL…parquet/format/converter/ParquetMetadataConverter.java952 org.apache.parquet.column.statistics.Statistics.getBuilderForReading(type);CODE
CRITICAL…parquet/format/converter/ParquetMetadataConverter.java1039 bbox = new org.apache.parquet.column.statistics.geospatial.BoundingBox(CODE
CRITICAL…parquet/format/converter/ParquetMetadataConverter.java1059 return new org.apache.parquet.column.statistics.geospatial.GeospatialStatistics(bbox, geospatialTypes);CODE
CRITICAL…parquet/format/converter/ParquetMetadataConverter.java1061 return new org.apache.parquet.column.statistics.geospatial.GeospatialStatistics(CODE
CRITICAL…parquet/format/converter/ParquetMetadataConverter.java1991 new org.apache.parquet.hadoop.metadata.FileMetaData(CODE
11 more matches not shown…
Self-Referential Comments8 hits · 22 pts
SeverityFileLineSnippetContext
MEDIUM…ava/org/apache/parquet/cli/commands/ToAvroCommand.java126 "# Create an Avro file from a Parquet file",CODE
MEDIUM…ava/org/apache/parquet/cli/commands/ToAvroCommand.java128 "# Create an Avro file in HDFS from a local JSON file",CODE
MEDIUM…ava/org/apache/parquet/cli/commands/ToAvroCommand.java130 "# Create an Avro file from data in S3",CODE
MEDIUM…va/org/apache/parquet/cli/commands/ConvertCommand.java157 "# Create a Parquet file from an Avro file",CODE
MEDIUM…va/org/apache/parquet/cli/commands/ConvertCommand.java159 "# Create a Parquet file in S3 from a local Avro file",CODE
MEDIUM…va/org/apache/parquet/cli/commands/ConvertCommand.java161 "# Create a Parquet file from Avro data in S3",CODE
MEDIUM…org/apache/parquet/cli/commands/ConvertCSVCommand.java203 "# Create a Parquet file from a CSV file",CODE
MEDIUM…org/apache/parquet/cli/commands/ConvertCSVCommand.java205 "# Create a Parquet file in HDFS from local CSV",CODE
Over-Commented Block18 hits · 18 pts
SeverityFileLineSnippetContext
LOWchangelog.sh1#COMMENT
LOW.asf.yaml1#COMMENT
LOWparquet-benchmarks/run.sh1#!/usr/bin/env bashCOMMENT
LOW…mn/src/main/java/org/apache/parquet/example/Paper.java81 // NameCOMMENT
LOW…/org/apache/parquet/io/RecordReaderImplementation.java101COMMENT
LOW.github/dependabot.yml1#COMMENT
LOW.github/workflows/ci-hadoop3.yml1# Licensed to the Apache Software Foundation (ASF) under oneCOMMENT
LOW.github/workflows/stale-prs.yml1# Licensed to the Apache Software Foundation (ASF) under oneCOMMENT
LOW.github/workflows/vector-plugins.yml1# Licensed to the Apache Software Foundation (ASF) under oneCOMMENT
LOW.github/ISSUE_TEMPLATE/config.yaml1# Licensed to the Apache Software Foundation (ASF) under oneCOMMENT
LOW.github/ISSUE_TEMPLATE/usage_question.yaml1# Licensed to the Apache Software Foundation (ASF) under oneCOMMENT
LOW.github/ISSUE_TEMPLATE/bug_report.yaml1# Licensed to the Apache Software Foundation (ASF) under oneCOMMENT
LOW.github/ISSUE_TEMPLATE/feature_request.yaml1# Licensed to the Apache Software Foundation (ASF) under oneCOMMENT
LOW…olumn/values/bitpacking/ByteBitPacking512VectorLE.java3181 // TODO Replace these helpers with ByteVector.fromMemorySegment(...) once the project's minimumCOMMENT
LOWdev/prepare-release.sh1#!/bin/bashCOMMENT
LOWdev/ci-before_install.sh1#!/usr/bin/env bashCOMMENT
LOWdev/source-release.sh1#!/bin/bashCOMMENT
LOWdev/ci-before_install-master.sh1# Licensed to the Apache Software Foundation (ASF) under oneCOMMENT
AI Slop Vocabulary2 hits · 6 pts
SeverityFileLineSnippetContext
MEDIUM…ache/parquet/hadoop/UnmaterializableRecordCounter.java28// Essentially taken from:COMMENT
MEDIUMparquet-benchmarks/run.sh82 # Actually run a benchmark in the JMH harness.COMMENT
Fake / Example Data3 hits · 3 pts
SeverityFileLineSnippetContext
LOW…va/org/apache/parquet/cli/commands/CatCommandTest.java153 .append("customer-name", "Jane Doe")CODE
LOW…e/parquet/cli/commands/ShowBloomFilterCommandTest.java91 phoneCommand.testValues = Arrays.asList(new String[] {"555-0001", "555-9999"});CODE
LOW…e/parquet/cli/commands/ShowBloomFilterCommandTest.java126 String[] phoneValues = {"555-0001", "555-0002", "555-0003", "555-0004", "555-0005"};CODE