Repository Analysis

dotnet/machinelearning

ML.NET is an open source and cross-platform machine learning framework for .NET.

2.7 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of dotnet/machinelearning, a C# project with 9,341 GitHub stars. SynthScan v2.0 examined 1,220,503 lines of code across 3968 source files, recording 2237 pattern matches distributed across 18 syntactic categories. The overall adjusted score of 2.7 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).

2.7
Adjusted Score
2.7
Raw Score
100%
Time Factor
2026-07-13
Last Push
9.3K
Stars
C#
Language
1.2M
Lines of Code
4.0K
Files
2.2K
Pattern Hits
2026-07-14
Scan Date
0.03
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 74HIGH 36MEDIUM 44LOW 2083

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 2237 distinct pattern matches across 18 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 Block1912 hits · 1872 pts
SeverityFileLineSnippetContext
LOWeng/common/SetupNugetSources.sh1#!/usr/bin/env bashCOMMENT
LOWeng/common/vmr-sync.sh1#!/bin/bashCOMMENT
LOWeng/common/vmr-sync.sh21###COMMENT
LOWeng/common/native/init-distro-rid.sh1#!/bin/shCOMMENT
LOWeng/common/native/init-distro-rid.sh61COMMENT
LOW…common/templates-official/variables/pool-providers.yml1# Select a pool provider based off branch name. Anything with branch name containing 'release' must go into an -Svc poolCOMMENT
LOWeng/common/templates/vmr-build-pr.yml1# This pipeline is used for running the VMR verification of the PR changes in repo-level PRs.COMMENT
LOWeng/common/templates/variables/pool-providers.yml1# Select a pool provider based off branch name. Anything with branch name containing 'release' must go into an -Svc poolCOMMENT
LOWeng/common/core-templates/stages/renovate.yml1# --------------------------------------------------------------------------------------COMMENT
LOWeng/common/core-templates/steps/install-microbuild.yml1parameters:COMMENT
LOWeng/common/core-templates/job/renovate.yml1# --------------------------------------------------------------------------------------COMMENT
LOWeng/common/core-templates/job/source-build.yml1parameters:COMMENT
LOWeng/common/core-templates/job/source-build.yml21 # published by source-build.COMMENT
LOW…toML.SourceGenerator/Template/SweepableEstimator_T_.cs1// ------------------------------------------------------------------------------COMMENT
LOW….AutoML.SourceGenerator/Template/SweepableEstimator.cs1// ------------------------------------------------------------------------------COMMENT
LOW….SourceGenerator/Template/SweepableEstimatorFactory.cs1// ------------------------------------------------------------------------------COMMENT
LOW…soft.ML.AutoML.SourceGenerator/Template/SearchSpace.cs1// ------------------------------------------------------------------------------COMMENT
LOW…soft.ML.AutoML.SourceGenerator/Template/SearchSpace.cs281 /// <summary>COMMENT
LOW…ft.ML.AutoML.SourceGenerator/Template/EstimatorType.cs1// ------------------------------------------------------------------------------COMMENT
LOW…osoft.ML.InternalCodeAnalyzer/Descriptions.Designer.cs1//------------------------------------------------------------------------------COMMENT
LOWtools-local/Microsoft.ML.InternalCodeAnalyzer/Utils.cs1// Licensed to the .NET Foundation under one or more agreements.COMMENT
LOWtest/Microsoft.ML.Core.Tests/UnitTests/TestLoss.cs21COMMENT
LOW…t/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs161 CheckSameValues(data1.OutputData, data2.OutputData);COMMENT
LOW…t/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs181COMMENT
LOW…t/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs201 // var postSelectDv = Env.CreateTransform("Keep{col=ColA col=ColB}", postDropDv);COMMENT
LOW…t/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs1441COMMENT
LOW…t/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs1461 // }COMMENT
LOW…t/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs1481 // }COMMENT
LOWtest/Microsoft.ML.Core.Tests/UnitTests/TestVBuffer.cs1081 /// <param name="len">The length of the vectors to generate</param>COMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs2181 /// <param name="onnxTxtName">Optional file path to write the text version of the onnx model.</param>COMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs2201 {COMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs2221 where TRow : classCOMMENT
LOW…ML.Tests/TrainerEstimators/MatrixFactorizationTests.cs381 // Apply the trained model to the examples with out-of-range indexes.COMMENT
LOW…ML.Tests/TrainerEstimators/MatrixFactorizationTests.cs501COMMENT
LOW…ML.Tests/TrainerEstimators/MatrixFactorizationTests.cs521 // s.NumIterations = 100;COMMENT
LOW…arios/Api/CookbookSamples/CookbookSamplesDynamicApi.cs161 // a caching step, which provides the same caching function, will be inserted in the considered "pipeline."COMMENT
LOW…arios/Api/CookbookSamples/CookbookSamplesDynamicApi.cs241 // Inspect the model parameters. COMMENT
LOW…L.Tests/Scenarios/Api/Estimators/PredictAndMetadata.cs41COMMENT
LOWtest/Microsoft.ML.TestFramework/TestCommandBase.cs141 {COMMENT
LOWtest/Microsoft.ML.TestFramework/TestCommandBase.cs401 var paths = toCompare.Where(pa => (pa.CmpUsage & situation) != PathArgument.Usage.None).ToArray();COMMENT
LOWtest/Microsoft.ML.TestFramework/TestCommandBase.cs421 return TestCoreCore(ctx, cmdName, dataPath, PathArgument.Usage.DataModel, null, ctx.ModelPath(), loaderArgs,COMMENT
LOWtest/Microsoft.ML.TestFramework/TestCommandBase.cs461 /// stdout</param>COMMENT
LOWtest/Microsoft.ML.TestFramework/TestCommandBase.cs481 {COMMENT
LOWtest/Microsoft.ML.TestFramework/BaseTestBaseline.cs981 if (inner != null)COMMENT
LOW…icrosoft.ML.TestFramework/DataPipe/TestDataPipeBase.cs161 {COMMENT
LOW…mmon/SavePipe/SavePipeTermDictionaryNgramHash-Data.txt1#@ TextLoader{COMMENT
LOW…t/Common/SavePipe/SavePipeCountTargetEncoding-Data.txt1#@ TextLoader{COMMENT
LOW…t/Common/SavePipe/SavePipeTermDictionaryNgram-Data.txt1#@ TextLoader{COMMENT
LOW…aselineOutput/Common/SavePipe/SavePipeWordBag-Data.txt1#@ TextLoader{COMMENT
LOW…ineOutput/Common/SavePipe/SavePipeInvertHash3-Data.txt1#@ TextLoader{COMMENT
LOW…ineOutput/Common/SavePipe/SavePipeInvertHash2-Data.txt1#@ TextLoader{COMMENT
LOW…selineOutput/Common/SavePipe/SavePipeExprVec2-Data.txt1#@ TextLoader{COMMENT
LOW…t/BaselineOutput/Common/SavePipe/SavePipeHash-Data.txt1#@ TextLoader{COMMENT
LOW…mon/SavePipe/SavePipeTrainAndScoreFccFastTree-Data.txt1#@ TextLoader{COMMENT
LOW…ineOutput/Common/SavePipe/SavePipeInvertHash4-Data.txt1#@ TextLoader{COMMENT
LOW…ineOutput/Common/SavePipe/SavePipeInvertHash5-Data.txt1#@ TextLoader{COMMENT
LOW…selineOutput/Common/SavePipe/SavePipeExpr-All-Data.txt1#@ TextLoader{COMMENT
LOW…selineOutput/Common/SavePipe/SavePipeKeyToVec-Data.txt1#@ TextLoader{COMMENT
LOW…Common/SavePipe/TestParquetPrimitiveDataTypes-Data.txt1#@ TextLoader{COMMENT
LOW…st/BaselineOutput/Common/SavePipe/SavePipeLda-Data.txt1#@ TextLoader{COMMENT
1852 more matches not shown…
Hallucination Indicators74 hits · 730 pts
SeverityFileLineSnippetContext
CRITICAL…oft.ML.TestFramework/Attributes/OnnxTheoryAttribute.cs25 && Microsoft.ML.TestFrameworkCommon.Utility.NativeLibrary.NativeLibraryExists("onnxruntime");CODE
CRITICAL…ML.TestFramework/Attributes/TorchSharpFactAttribute.cs26 // && Microsoft.ML.TestFrameworkCommon.Utility.NativeLibrary.NativeLibraryExists("torch_cpu");COMMENT
CRITICAL…ML.TestFramework/Attributes/TensorflowFactAttribute.cs26 && Microsoft.ML.TestFrameworkCommon.Utility.NativeLibrary.NativeLibraryExists("tensorflow");CODE
CRITICAL….TestFramework/Attributes/TensorflowTheoryAttribute.cs26 && Microsoft.ML.TestFrameworkCommon.Utility.NativeLibrary.NativeLibraryExists("tensorflow");CODE
CRITICAL…Rank/FastRank-TrainTest-MSM-sparse-sample-test-out.txt16 at Microsoft.ML.Runtime.Data.TestCommand.RunCore(IChannel ch) in C:\DotNet\machinelearning\src\Microsoft.ML.Data\CommCODE
CRITICAL…Rank/FastRank-TrainTest-MSM-sparse-sample-test-out.txt17 at Microsoft.ML.Runtime.Data.TestCommand.Run() in C:\DotNet\machinelearning\src\Microsoft.ML.Data\Commands\TestCommanCODE
CRITICAL…Rank/FastRank-TrainTest-MSM-sparse-sample-test-out.txt18 at Microsoft.ML.Runtime.Tools.Maml.MainCore(ConsoleEnvironment env, String args, Boolean alwaysPrintStacktrace) in C:CODE
CRITICALtest/Microsoft.ML.CodeGenerator.Tests/UtilTest.cs200 var actualStr = Microsoft.ML.CodeGenerator.Utilities.Utils.Normalize(testStrArray[i]);CODE
CRITICALtest/Microsoft.ML.AutoML.Tests/TunerTests.cs397 Thread.CurrentThread.CurrentCulture.NumberFormat.NumberDecimalSeparator.Should().Be(".");CODE
CRITICALtest/Microsoft.ML.AutoML.Tests/TunerTests.cs410 Thread.CurrentThread.CurrentCulture.NumberFormat.NumberDecimalSeparator.Should().Be(",");CODE
CRITICAL…les/Dynamic/Trainers/Regression/LightGbmWithOptions.cs43 Booster = new Microsoft.ML.Trainers.LightGbm.GossBooster.Options()CODE
CRITICAL…c/Microsoft.ML.StandardTrainers/Standard/SdcaBinary.cs327 int numFeatures = data.Schema.Feature.Value.Type.GetVectorSize();CODE
CRITICAL…c/Microsoft.ML.StandardTrainers/Standard/SdcaBinary.cs2026 int numFeatures = data.Schema.Feature.Value.Type.GetVectorSize();CODE
CRITICAL…osoft.ML.Mkl.Components/SymSgdClassificationTrainer.cs703 int numFeatures = data.Schema.Feature.Value.Type.GetVectorSize();CODE
CRITICALsrc/Microsoft.ML.FastTree/FastTreeRanking.cs150 FeatureCount = trainData.Schema.Feature.Value.Type.GetValueCount();CODE
CRITICALsrc/Microsoft.ML.FastTree/GamTrainer.cs233 InputLength = context.TrainingSet.Schema.Feature.Value.Type.GetValueCount();CODE
CRITICALsrc/Microsoft.ML.FastTree/RandomForestClassification.cs224 FeatureCount = trainData.Schema.Feature.Value.Type.GetValueCount();CODE
CRITICALsrc/Microsoft.ML.FastTree/FastTreeClassification.cs197 FeatureCount = trainData.Schema.Feature.Value.Type.GetValueCount();CODE
CRITICALsrc/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs655 if (vm.InputType.GetVectorSize() != data.Schema.Feature.Value.Type.GetVectorSize())CODE
CRITICALsrc/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs659 vm.InputType.GetVectorSize(), data.Schema.Feature.Value.Type.GetVectorSize());CODE
CRITICALsrc/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs726 if (data != null && vm.InputType.GetVectorSize() != data.Schema.Feature.Value.Type.GetVectorSize())CODE
CRITICALsrc/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs730 vm.InputType.GetVectorSize(), data.Schema.Feature.Value.Type.GetVectorSize());CODE
CRITICALsrc/Microsoft.ML.FastTree/FastTreeTweedie.cs130 FeatureCount = trainData.Schema.Feature.Value.Type.GetValueCount();CODE
CRITICALsrc/Microsoft.ML.FastTree/RandomForestRegression.cs363 FeatureCount = trainData.Schema.Feature.Value.Type.GetValueCount();CODE
CRITICALsrc/Microsoft.ML.FastTree/FastTreeRegression.cs120 FeatureCount = trainData.Schema.Feature.Value.Type.GetValueCount();CODE
CRITICAL…soft.ML.Data/Scorers/MulticlassClassificationScorer.cs417 var keyType = trainSchema.Label.Value.Annotations.Schema.GetColumnOrNull(AnnotationUtils.Kinds.KeyValues)?.TCODE
CRITICAL…soft.ML.Data/Scorers/MulticlassClassificationScorer.cs493 var type = trainSchema.Label.Value.Annotations.Schema.GetColumnOrNull(AnnotationUtils.Kinds.KeyValues)?.TypeCODE
CRITICALsrc/Microsoft.ML.Data/Scorers/BinaryClassifierScorer.cs67 var keyType = trainSchema.Label.Value.Annotations.Schema.GetColumnOrNull(AnnotationUtils.Kinds.KeyValues)?.TCODE
CRITICALsrc/Microsoft.ML.Data/Utilities/StreamUtils.cs21 return Microsoft.ML.Internal.IO.ZStreamIn.Open(fileName);CODE
CRITICALsrc/Microsoft.ML.Data/Transforms/ValueMapping.cs508 else if (Microsoft.ML.Data.Conversion.Conversions.DefaultInstance.TryParse(in value, out res))CODE
CRITICAL…/Microsoft.ML.Ensemble/OutputCombiners/BaseStacking.cs155 switch (data.Schema.Label.Value.Type.GetRawKind())CODE
CRITICAL…oft.ML.TorchSharp/NasBert/TextClassificationTrainer.cs219 Microsoft.ML.Internal.Utilities.Utils.MarshalActionInvoke(SaveLabelValues<int>, labelColType.ItemType.RawTypCODE
CRITICAL…oft.ML.TorchSharp/NasBert/TextClassificationTrainer.cs284 var labelGetter = Microsoft.ML.Internal.Utilities.Utils.MarshalInvoke(_decodeInitMethodInfo, vecType.ItemTypCODE
CRITICALsrc/Microsoft.ML.TorchSharp/NasBert/NerTrainer.cs282 Microsoft.ML.Internal.Utilities.Utils.MarshalActionInvoke(SaveLabelValues<int>, labelColType.ItemType.RawTypCODE
CRITICALsrc/Microsoft.ML.TorchSharp/NasBert/NerTrainer.cs347 var labelGetter = Microsoft.ML.Internal.Utilities.Utils.MarshalInvoke(_decodeInitMethodInfo, vecType.ItemTypCODE
CRITICALsrc/Microsoft.ML.TorchSharp/NasBert/NasBertTrainer.cs521 var getter = Microsoft.ML.Internal.Utilities.Utils.MarshalInvoke(_makeLabelAnnotationGetter, this, kCODE
CRITICALsrc/Microsoft.ML.TorchSharp/NasBert/NasBertTrainer.cs543 var getter = Microsoft.ML.Internal.Utilities.Utils.MarshalInvoke(_makeLabelAnnotationGetter, this, kCODE
CRITICAL…t.ML.TorchSharp/AutoFormerV2/ObjectDetectionTrainer.cs626 Microsoft.ML.Internal.Utilities.Utils.MarshalActionInvoke(SaveLabelValues<int>, labelColType.ItemType.RawTypCODE
CRITICAL…t.ML.TorchSharp/AutoFormerV2/ObjectDetectionTrainer.cs701 var labelGetter = Microsoft.ML.Internal.Utilities.Utils.MarshalInvoke(_decodeInitMethodInfo, vecType.ItemTypCODE
CRITICAL…t.ML.TorchSharp/AutoFormerV2/ObjectDetectionTrainer.cs756 var getter = Microsoft.ML.Internal.Utilities.Utils.MarshalInvoke(_makeLabelAnnotationGetter, this, keyTyCODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs740 T = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.TensorProto();CODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs748 G = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.GraphProto();CODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs756 SparseTensor = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.SparseTensoCODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs4627 TensorType = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.TypeProtoCODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs4634 SequenceType = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.TypeProCODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs4641 MapType = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.TypeProto.TyCODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs4648 SparseTensorType = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.TypCODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs4655 OpaqueType = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.TypeProtoCODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs804 T = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.TensorProtCODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs813 G = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.GraphProtoCODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs864 SparseTensor = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapperCODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs1109 Type = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.TypeProto();CODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs1140 Type = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.TypeProCODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs1903 Graph = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.GraphProto();CODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs1956 Graph = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.GraphPCODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs3220 Segment = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.TensorProto.TypeCODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs3276 Segment = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.TensCODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs3820 Values = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.TensorProto();CODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs3828 Indices = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.TensorProto();CODE
CRITICALsrc/Microsoft.ML.OnnxConverter/OnnxMl.cs3851 Values = new global::Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper.TensoCODE
14 more matches not shown…
Structural Annotation Overuse83 hits · 148 pts
SeverityFileLineSnippetContext
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs90 // Step 2: Convert ML.NET model to ONNX format and save it as a model file and a text file.COMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs93 // Step 3: Check ONNX model's text format. This test will be not necessary if Step 2 can run on Linux andCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs310 // Step 1: Test calibrator with binary prediction trainerCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs318 // Step 2: Test calibrator without any binary prediction trainerCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs323 // Step 3: Test calibrator with a non-default Score column name and without any binary prediction trainerCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs1783 // Step 2: Load back the saved .idvCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs1790 // Step 3: Create ONNX model which simply applies Identity to Label columnCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs1798 // Step 4: Get input and output names of modelCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs1805 // Step 5: Apply Onnx ModelCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs1810 // Step 6: Compare results to an onnx model created using the mappedData IDataViewCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs68 // Step 1: Create and train a ML.NET pipeline.COMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs168 // Step 2: Convert ML.NET model to ONNX format and save it as a model file and a text file.COMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs209 // Step 2: Convert ML.NET model to ONNX format and save it as a model file and a text file.COMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs564 // Step 1: Create and train a ML.NET pipeline.COMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs595 // Step 1: Create and train a ML.NET pipeline.COMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs1759 // Step 1: Load the Iris Dataset and apply a Value To Key Mapping to it.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs66 // Step 1: Read the data as an IDataView.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs73 // Step 2: Create a binary classifier.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs77 // Step 3: Train the pipeline created.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs80 // Step 4: Make prediction and evaluate its quality (on training set).COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs90 // Step 5: Inspect the prediction of the first example.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs225 // Step 1: Read the data as an IDataView.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs232 // Step 2: Create a binary classifier.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs237 // Step 3: Train the pipeline created.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs240 // Step 4: Make prediction and evaluate its quality (on training set).COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs249 // Step 5: Inspect the prediction of the first example.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs267 // Step 1: Read the data as an IDataView.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs274 // Step 2: Create a binary classifier.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs280 // Step 3: Train the pipeline created.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs283 // Step 4: Make prediction and evaluate its quality (on training set).COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs302 // Step 1: Read the data as an IDataView.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs309 // Step 2: Create a binary classifier.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs314 // Step 3: Train the pipeline created.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs317 // Step 4: Make prediction and evaluate its quality (on training set).COMMENT
LOW.github/workflows/ci-scan.agent.md83## Step 1 - Select the source buildCOMMENT
LOW.github/workflows/ci-scan.agent.md94## Step 2 - Walk the timelineCOMMENT
LOW.github/workflows/ci-scan.agent.md118## Step 3 - Classify each failureCOMMENT
LOW.github/workflows/ci-scan.agent.md122## Step 4 - Follow-up gateCOMMENT
LOW.github/workflows/ci-scan.agent.md126## Step 5 - DedupCOMMENT
LOW.github/workflows/ci-scan.agent.md130## Step 6 - File the KBECOMMENT
LOW.github/workflows/ci-scan.agent.md134## Step 7 - TallyCOMMENT
LOW.github/agents/code-testing-generator.agent.md28### Step 1: Clarify the RequestCOMMENT
LOW.github/agents/code-testing-generator.agent.md36### Step 2: Choose Execution StrategyCOMMENT
LOW.github/agents/code-testing-generator.agent.md46### Step 3: Research PhaseCOMMENT
LOW.github/agents/code-testing-generator.agent.md59### Step 4: Planning PhaseCOMMENT
LOW.github/agents/code-testing-generator.agent.md72### Step 5: Implementation PhaseCOMMENT
LOW.github/agents/code-testing-generator.agent.md83### Step 6: Final Build ValidationCOMMENT
LOW.github/agents/code-testing-generator.agent.md94### Step 7: Final Test ValidationCOMMENT
LOW.github/agents/code-testing-generator.agent.md102### Step 8: Coverage Gap IterationCOMMENT
LOW.github/agents/code-testing-generator.agent.md112### Step 9: Report ResultsCOMMENT
LOW.github/skills/code-testing-agent/SKILL.md66### Step 1: Determine the user requestCOMMENT
LOW.github/skills/code-testing-agent/SKILL.md72### Step 2: Invoke the Test GeneratorCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs550 // Step 1: Get backadd waveCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs553 // Step 2: FFT transformationCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs561 // Step 3: Calculate mags of FFTCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs577 // Step 4: Calculate spectralCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs585 // Step 5: IFFT transformationCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs726 //Step 8: Calculate Expected ValueCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs735 //Step 8: Calculated Expected ValueCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs738 //Step 9: Calculate Boundary UnitCOMMENT
23 more matches not shown…
Synthetic Comment Markers19 hits · 140 pts
SeverityFileLineSnippetContext
HIGH…toML.SourceGenerator/Template/SweepableEstimator_T_.cs3// This code was generated by a tool.COMMENT
HIGH….AutoML.SourceGenerator/Template/SweepableEstimator.cs3// This code was generated by a tool.COMMENT
HIGH….SourceGenerator/Template/SweepableEstimatorFactory.cs3// This code was generated by a tool.COMMENT
HIGH…soft.ML.AutoML.SourceGenerator/Template/SearchSpace.cs3// This code was generated by a tool.COMMENT
HIGH…ft.ML.AutoML.SourceGenerator/Template/EstimatorType.cs3// This code was generated by a tool.COMMENT
HIGH…osoft.ML.InternalCodeAnalyzer/Descriptions.Designer.cs3// This code was generated by a tool.COMMENT
HIGHtest/Microsoft.ML.Tokenizers.Tests/BpeTests.cs696 "mock testing with AI-generated scenarios: # test_database.py from unittest.mock import Mock import CODE
HIGH…es/Azure/Model/AzureObjectDetectionModelOutputClass.cs3// This code was generated by a tool.COMMENT
HIGH…or/Templates/Azure/Model/AzureImageModelOutputClass.cs3// This code was generated by a tool.COMMENT
HIGH…Generator/Templates/Azure/Console/AzureModelBuilder.cs3// This code was generated by a tool.COMMENT
HIGH…oft.ML.CodeGenerator/Templates/Console/ModelBuilder.cs3// This code was generated by a tool.COMMENT
HIGH…oft.ML.CodeGenerator/Templates/Console/ModelProject.cs3// This code was generated by a tool.COMMENT
HIGH…oft.ML.CodeGenerator/Templates/Console/ConsumeModel.cs3// This code was generated by a tool.COMMENT
HIGH…ML.CodeGenerator/Templates/Console/ModelOutputClass.cs3// This code was generated by a tool.COMMENT
HIGH…t.ML.CodeGenerator/Templates/Console/PredictProgram.cs3// This code was generated by a tool.COMMENT
HIGH…t.ML.CodeGenerator/Templates/Console/PredictProject.cs3// This code was generated by a tool.COMMENT
HIGH….ML.CodeGenerator/Templates/Console/ModelInputClass.cs3// This code was generated by a tool.COMMENT
HIGHsrc/Microsoft.Data.Analysis/Strings.Designer.cs3// This code was generated by a tool.COMMENT
HIGH…crosoft.ML.Transforms/Properties/Resources.Designer.cs3// This code was generated by a tool.COMMENT
Verbosity Indicators63 hits · 111 pts
SeverityFileLineSnippetContext
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs90 // Step 2: Convert ML.NET model to ONNX format and save it as a model file and a text file.COMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs93 // Step 3: Check ONNX model's text format. This test will be not necessary if Step 2 can run on Linux andCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs310 // Step 1: Test calibrator with binary prediction trainerCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs318 // Step 2: Test calibrator without any binary prediction trainerCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs323 // Step 3: Test calibrator with a non-default Score column name and without any binary prediction trainerCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs1783 // Step 2: Load back the saved .idvCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs1790 // Step 3: Create ONNX model which simply applies Identity to Label columnCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs1798 // Step 4: Get input and output names of modelCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs1805 // Step 5: Apply Onnx ModelCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs1810 // Step 6: Compare results to an onnx model created using the mappedData IDataViewCOMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs68 // Step 1: Create and train a ML.NET pipeline.COMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs168 // Step 2: Convert ML.NET model to ONNX format and save it as a model file and a text file.COMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs209 // Step 2: Convert ML.NET model to ONNX format and save it as a model file and a text file.COMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs564 // Step 1: Create and train a ML.NET pipeline.COMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs595 // Step 1: Create and train a ML.NET pipeline.COMMENT
LOWtest/Microsoft.ML.Tests/OnnxConversionTest.cs1759 // Step 1: Load the Iris Dataset and apply a Value To Key Mapping to it.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs66 // Step 1: Read the data as an IDataView.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs73 // Step 2: Create a binary classifier.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs77 // Step 3: Train the pipeline created.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs80 // Step 4: Make prediction and evaluate its quality (on training set).COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs90 // Step 5: Inspect the prediction of the first example.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs225 // Step 1: Read the data as an IDataView.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs232 // Step 2: Create a binary classifier.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs237 // Step 3: Train the pipeline created.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs240 // Step 4: Make prediction and evaluate its quality (on training set).COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs249 // Step 5: Inspect the prediction of the first example.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs267 // Step 1: Read the data as an IDataView.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs274 // Step 2: Create a binary classifier.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs280 // Step 3: Train the pipeline created.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs283 // Step 4: Make prediction and evaluate its quality (on training set).COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs302 // Step 1: Read the data as an IDataView.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs309 // Step 2: Create a binary classifier.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs314 // Step 3: Train the pipeline created.COMMENT
LOWtest/Microsoft.ML.Tests/TrainerEstimators/SdcaTests.cs317 // Step 4: Make prediction and evaluate its quality (on training set).COMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs550 // Step 1: Get backadd waveCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs553 // Step 2: FFT transformationCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs561 // Step 3: Calculate mags of FFTCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs577 // Step 4: Calculate spectralCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs585 // Step 5: IFFT transformationCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs726 //Step 8: Calculate Expected ValueCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs735 //Step 8: Calculated Expected ValueCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs738 //Step 9: Calculate Boundary UnitCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs743 //Step 10: Calculate UpperBound and LowerBoundCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs830 //Step 8: Calculate Expected ValueCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs834 //Step 9: Calculate Boundary UnitCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs839 //Step 10: Calculate UpperBound and LowerBoundCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs847 //Step 11: Update Anomaly ScoreCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs850 //Step 12: Update IsAnomalyCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs853 //Step 13: Update Expected Value, LowerBound and UpperBound for not anomaly points.COMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs606 // Step 6: Calculate mag and ave_mag of IFFTCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs619 // Step 7: Calculate raw score and set resultCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs714 //Step 8: Calculate Expected ValueCOMMENT
LOW…/Microsoft.ML.TimeSeries/SrCnnEntireAnomalyDetector.cs812 //Step 11: Update Anomaly Score, Expected Value and BoundariesCOMMENT
LOW…c/Microsoft.ML.TimeSeries/SrCnnAnomalyDetectionBase.cs174 // Step 1: Get backadd waveCOMMENT
LOW…c/Microsoft.ML.TimeSeries/SrCnnAnomalyDetectionBase.cs177 // Step 2: FftTransform transformationCOMMENT
LOW…c/Microsoft.ML.TimeSeries/SrCnnAnomalyDetectionBase.cs183 // Step 3: Calculate mags of FftTransformCOMMENT
LOW…c/Microsoft.ML.TimeSeries/SrCnnAnomalyDetectionBase.cs190 // Step 4: Calculate spectralCOMMENT
LOW…c/Microsoft.ML.TimeSeries/SrCnnAnomalyDetectionBase.cs199 // Step 5: IFFT transformationCOMMENT
LOW…c/Microsoft.ML.TimeSeries/SrCnnAnomalyDetectionBase.cs220 // Step 6: Calculate mag and ave_mag of IFFTCOMMENT
LOW…c/Microsoft.ML.TimeSeries/SrCnnAnomalyDetectionBase.cs228 // Step 7: Calculate score and set resultCOMMENT
3 more matches not shown…
AI Slop Vocabulary34 hits · 100 pts
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LOW…ommon/core-templates/steps/enable-internal-sources.yml24 # If running on dnceng (internal project), just use the default behavior for NuGetAuthenticate.COMMENT
MEDIUMtest/Microsoft.ML.TestFramework/TestCommandBase.cs640 /// do not use this, but instead utilize the <see cref="TestCommandBase.RunContextBase.InitPath"/>COMMENT
MEDIUM…ard/LogisticRegression/MulticlassLogisticRegression.cs173 // Should we instead leverage the pass made by the LBFGS base class? Ideally, it wouldn'tCOMMENT
MEDIUMsrc/Microsoft.ML.FastTree/GamTrainer.cs76 /// Whether to utilize the disk or the data's native transposition facilities (where applicable) when perforCOMMENT
MEDIUMsrc/Microsoft.ML.FastTree/FastTreeArguments.cs397 /// Whether to utilize the disk or the data's native transposition facilities (where applicable) when performingCOMMENT
MEDIUMsrc/Microsoft.ML.TimeSeries/STL/InnerStl.cs113 /// The core for the robust trend-seasonal decomposition. See the ref: http://www.wessa.net/download/stl.pdf,COMMENT
MEDIUMsrc/Microsoft.ML.TimeSeries/STL/Loess.cs132 /// This is used for robust weight, It is one iteration step of loess.COMMENT
MEDIUMsrc/Microsoft.ML.TimeSeries/STL/Loess.cs175 /// The number of iterations for robust regression.COMMENT
MEDIUMsrc/Microsoft.ML.Core/Utilities/Stats.cs234 /// <remarks>Should be robust for all values of n, p</remarks>COMMENT
MEDIUMsrc/Microsoft.ML.Core/Data/ServerChannel.cs37 /// performant though more robust calls to <see cref="Register{TRet}"/> andCOMMENT
MEDIUMsrc/Microsoft.ML.Data/DataView/CacheDataView.cs740 /// robust to that usage.)COMMENT
MEDIUM…soft.ML.Data/DataLoadSave/LegacyCompositeDataLoader.cs345 /// Essentially, this is a helper method for the LoadTransform class.COMMENT
MEDIUMsrc/Microsoft.ML.Data/DataLoadSave/MultiFileSource.cs29 /// The provided <paramref name="paths"/> can utilize wildcards to load all source files. For example:COMMENT
MEDIUMsrc/Microsoft.ML.Data/DataLoadSave/TransformWrapper.cs12 /// Note that there are some important differences in usages that make this shimming somewhat non-seamless, so the gCOMMENT
MEDIUM…c/Microsoft.ML.Data/DataLoadSave/Binary/BinarySaver.cs843 /// <param name="type">The type of the codec to write and utilize</param>COMMENT
MEDIUMsrc/Microsoft.ML.Data/Utilities/ApplyTransformUtils.cs77 // It's probably more robust to make CompositeDataLoader not even be an IDataView, thisCOMMENT
MEDIUMsrc/Microsoft.ML.Data/Transforms/Normalizer.cs50 /// * Robust Scaling - Optionally centers the data and scales based on the range of data and the quantile min and maCOMMENT
MEDIUMsrc/Microsoft.ML.Data/Transforms/InvertHashUtils.cs48 // Second choice: if key, utilize the KeyValues metadata for that key, if it has one and is text.COMMENT
MEDIUM…crosoft.ML.Data/Transforms/TrainAndScoreTransformer.cs103 // Essentially, all trainer estimators when fitted return a transformer that produces scores -- which is to say, allCOMMENT
MEDIUM…oft.ML.Data/Transforms/ValueToKeyMappingTransformer.cs69 // REVIEW: This pattern isn't robust enough. If a new field is added, this code needsCOMMENT
MEDIUM…/Microsoft.ML.Data/Transforms/LabelConvertTransform.cs165 // Certainly there's nothing contractual requiring I suppress this. Should I suppressCOMMENT
MEDIUMsrc/Microsoft.ML.Data/Dirty/IniFileUtils.cs19 // TODO: Should be made more robust to handle things like "Evaluators = " sand such.COMMENT
MEDIUMsrc/Microsoft.ML.Data/Commands/SaveDataCommand.cs165 // If it is a text saver, utilize a special utility for this purpose.COMMENT
MEDIUM…ft.ML.AutoML/API/MulticlassClassificationExperiment.cs32 /// Collection of trainers the AutoML experiment can leverage.COMMENT
MEDIUMsrc/Microsoft.ML.AutoML/API/RecommendationExperiment.cs24 /// Collection of trainers the AutoML experiment can leverage.COMMENT
MEDIUM…rosoft.ML.AutoML/API/BinaryClassificationExperiment.cs34 /// Collection of trainers the AutoML experiment can leverage.COMMENT
MEDIUMsrc/Microsoft.ML.AutoML/API/RegressionExperiment.cs32 /// Collection of trainers the AutoML experiment can leverage.COMMENT
MEDIUMsrc/Microsoft.ML.AutoML/API/RankingExperiment.cs23 /// Collection of trainers the AutoML experiment can leverage.COMMENT
MEDIUMsrc/Microsoft.ML.DataView/VBuffer.cs368 /// if one is doing a more involved computation involving many operations, it may be faster to utilizeCOMMENT
MEDIUM…osoft.ML.Transforms/MissingValueHandlingTransformer.cs117 /// <param name="replaceWith">The replacement method to utilize.</param>COMMENT
MEDIUMsrc/Microsoft.ML.Transforms/NormalizerCatalog.cs327 /// Create a <see cref="NormalizingEstimator"/>, which normalizes using statistics that are robust to outliers bCOMMENT
MEDIUMsrc/Microsoft.ML.Transforms/NormalizerCatalog.cs358 /// Create a <see cref="NormalizingEstimator"/>, which normalizes using statistics that are robust to outliers bCOMMENT
MEDIUMsrc/Microsoft.ML.Transforms/LambdaTransform.cs92 /// argument into its <typeparamref name="TDst"/> argument and can utilize the per-cursor <typeparamref name="TSCOMMENT
MEDIUM…icrosoft.ML.Transforms/Text/NgramHashingTransformer.cs833 // re-used. The collector will utilize its copier to make it safe, inCOMMENT
AI Response Leakage8 hits · 60 pts
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HIGH…L.Transforms/PermutationFeatureImportanceExtensions.cs44 /// In this implementation, PFI computes the change in all possible regression evaluation metrics for each featuCOMMENT
HIGH…L.Transforms/PermutationFeatureImportanceExtensions.cs107 /// In this implementation, PFI computes the change in all possible regression evaluation metrics for each featuCOMMENT
HIGH…L.Transforms/PermutationFeatureImportanceExtensions.cs194 /// In this implementation, PFI computes the change in all possible binary classification evaluation metrics forCOMMENT
HIGH…L.Transforms/PermutationFeatureImportanceExtensions.cs257 /// In this implementation, PFI computes the change in all possible binary classification evaluation metrics forCOMMENT
HIGH…L.Transforms/PermutationFeatureImportanceExtensions.cs348 /// In this implementation, PFI computes the change in all possible multiclass classification evaluation metricsCOMMENT
HIGH…L.Transforms/PermutationFeatureImportanceExtensions.cs411 /// In this implementation, PFI computes the change in all possible multiclass classification evaluation metricsCOMMENT
HIGH…L.Transforms/PermutationFeatureImportanceExtensions.cs507 /// In this implementation, PFI computes the change in all possible ranking evaluation metrics for each feature,COMMENT
HIGH…L.Transforms/PermutationFeatureImportanceExtensions.cs572 /// In this implementation, PFI computes the change in all possible ranking evaluation metrics for each feature,COMMENT
Cross-File Repetition9 hits · 45 pts
SeverityFileLineSnippetContext
HIGH…mon/Command/SavePipeTextLoaderWithMultilines-1-out.txt0bird 16 here is text after the empty line dog 17 this is a line with an empty animal "" 0 this is a line with an empty iSTRING
HIGH…ommon/Command/SavePipeTextLoaderWithMultilines-out.txt0bird 16 here is text after the empty line dog 17 this is a line with an empty animal "" 0 this is a line with an empty iSTRING
HIGH…d/linux-arm/SavePipeTextLoaderWithMultilines-1-out.txt0bird 16 here is text after the empty line dog 17 this is a line with an empty animal "" 0 this is a line with an empty iSTRING
HIGH…and/linux-arm/SavePipeTextLoaderWithMultilines-out.txt0bird 16 here is text after the empty line dog 17 this is a line with an empty animal "" 0 this is a line with an empty iSTRING
HIGH…linux-arm64/SavePipeTextLoaderWithMultilines-1-out.txt0bird 16 here is text after the empty line dog 17 this is a line with an empty animal "" 0 this is a line with an empty iSTRING
HIGH…d/linux-arm64/SavePipeTextLoaderWithMultilines-out.txt0bird 16 here is text after the empty line dog 17 this is a line with an empty animal "" 0 this is a line with an empty iSTRING
HIGH…cs/samples/Microsoft.ML.GenAI.Samples/MEAI/Llama3_1.cs0write a c# program to print the sum of two numbers. use top-level statement, put code between ```csharp and ```.STRING
HIGHdocs/samples/Microsoft.ML.GenAI.Samples/MEAI/Phi3.cs0write a c# program to print the sum of two numbers. use top-level statement, put code between ```csharp and ```.STRING
HIGH…amples/Microsoft.ML.GenAI.Samples/Llama/LlamaSample.cs0write a c# program to print the sum of two numbers. use top-level statement, put code between ```csharp and ```.STRING
Decorative Section Separators6 hits · 27 pts
SeverityFileLineSnippetContext
MEDIUMeng/common/core-templates/stages/renovate.yml1# --------------------------------------------------------------------------------------COMMENT
MEDIUMeng/common/core-templates/stages/renovate.yml3# --------------------------------------------------------------------------------------COMMENT
MEDIUMeng/common/core-templates/stages/renovate.yml9# --------------------------------------------------------------------------------------COMMENT
MEDIUMeng/common/core-templates/job/renovate.yml1# --------------------------------------------------------------------------------------COMMENT
MEDIUMeng/common/core-templates/job/renovate.yml3# --------------------------------------------------------------------------------------COMMENT
MEDIUMeng/common/core-templates/job/renovate.yml17# --------------------------------------------------------------------------------------COMMENT
Redundant / Tautological Comments8 hits · 12 pts
SeverityFileLineSnippetContext
LOWeng/common/tools.sh314 # Check if the script is older than 30 days using timestamp fileCOMMENT
LOWeng/common/SetupNugetSources.sh59 # Check if disabledPackageSources section existsCOMMENT
LOWeng/common/SetupNugetSources.sh65 # Check if this source name is disabledCOMMENT
LOWeng/common/SetupNugetSources.sh85 # Check if source already existsCOMMENT
LOWeng/common/SetupNugetSources.sh115 # Check if disabledPackageSources section existsCOMMENT
LOWeng/common/SetupNugetSources.sh185 # Check if there is no existing credential for this FeedNameCOMMENT
LOWeng/common/core-templates/steps/source-build.yml31 # Check if AzDO substitutes in a build config from a variable, and use it if so.COMMENT
LOWeng/common/core-templates/steps/install-microbuild.yml64 # Check if ESRP is disabled while SignType is realCOMMENT
Excessive Try-Catch Wrapping4 hits · 7 pts
SeverityFileLineSnippetContext
MEDIUMeng/common/cross/install-debs.py35 print(f"Error downloading {url}: {type(e).__name__} - {e}. Retrying...")CODE
LOWeng/common/cross/install-debs.py92 except Exception as e:CODE
MEDIUMeng/common/cross/install-debs.py93 print(f"Error fetching {url}: {e}")CODE
MEDIUMeng/common/cross/install-debs.py145 print(f"Error: Package '{current}' was not found in the available packages.")CODE
Example Usage Blocks4 hits · 7 pts
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LOWeng/common/native/install-dependencies.sh7# Usage:COMMENT
LOW…common/templates-official/variables/pool-providers.yml12# How to use:COMMENT
LOWeng/common/templates/variables/pool-providers.yml12# How to use:COMMENT
LOWeng/common/core-templates/job/renovate.yml11# Usage:COMMENT
Deep Nesting6 hits · 6 pts
SeverityFileLineSnippetContext
LOWeng/common/cross/install-debs.py19CODE
LOWeng/common/cross/install-debs.py135CODE
LOWeng/common/cross/install-debs.py160CODE
LOWeng/common/cross/install-debs.py196CODE
LOWeng/common/cross/install-debs.py230CODE
LOWeng/common/cross/install-debs.py273CODE
Hyper-Verbose Identifiers3 hits · 3 pts
SeverityFileLineSnippetContext
LOWeng/common/cross/install-debs.py42async def download_deb_files_parallel(mirror, packages, tmp_dir):CODE
LOWeng/common/cross/install-debs.py58async def download_package_index_parallel(mirror, arch, suites):CODE
LOW…osoft.ML.Mkl.Components/SymSgdClassificationTrainer.cs528 private sealed class InputDataManager : IDisposableCODE
Self-Referential Comments1 hit · 3 pts
SeverityFileLineSnippetContext
MEDIUMeng/common/cross/build-rootfs.sh665 # Create the symlink in the same directoryCOMMENT
Modern AI Meta-Vocabulary1 hit · 2 pts
SeverityFileLineSnippetContext
MEDIUM.github/workflows/ci-scan-feedback.agent.md80The scanner's methodology lives in [`shared/ci-scan.instructions.md`](shared/ci-scan.instructions.md). Read it once at tCODE
Unused Imports1 hit · 1 pts
SeverityFileLineSnippetContext
LOWeng/common/cross/install-debs.py17CODE
AI Structural Patterns1 hit · 1 pts
SeverityFileLineSnippetContext
LOWdocs/samples/Microsoft.ML.Samples.OneDal/run_bench.py92CODE