Daily Test Coverage Improver: Research and Plan

[github-actions]

Repository Research Summary

Repository Purpose: Furnace is a tensor library with support for differentiable programming, designed for machine learning, probabilistic programming, and optimization. It's a hard fork of DiffSharp with PyTorch-like APIs and efficient LibTorch CUDA/C++ tensors.

Technology Stack:

• Language: F# (.NET 6.0)
• Testing Framework: NUnit 3.13.1 with coverlet.msbuild 3.0.3 for coverage
• Backends: Reference backend and TorchSharp backend
• Build System: .NET SDK with MSBuild
• Documentation: FSDocs for documentation generation

Current Test Coverage Analysis

Current coverage metrics (from latest test run):

+---------+--------+--------+--------+
|         | Line   | Branch | Method |
+---------+--------+--------+--------+
| Total   | 75.84% | 47.88% | 67.52% |
+---------+--------+--------+--------+

By Module:

• Furnace.Backends.Reference: 74.65% line, 44.46% branch, 68.02% method
• Furnace.Backends.Torch: 83.38% line, 80.78% branch, 70.28% method
• Furnace.Data: 55.83% line, 46.28% branch, 47.16% method

Test Organization

Location: tests/Furnace.Tests/
Test Files: 30+ test files organized by functionality:

• TestTensor.fs, TestTensor.Conv.fs, TestTensor.MaxPool.fs - Core tensor operations
• TestModel.*.fs - Neural network model components (BatchNorm, Conv, Linear, etc.)
• TestOp.*.fs - Individual operations (AvgPool, BMM, Det, Inv, etc.)
• TestDerivatives.*.fs - Differentiation testing
• TestDistributions.fs, TestExtensions.fs, TestData.fs - Additional functionality

Test Count: 516 tests total (515 passed, 1 skipped)

Required Commands

Build: dotnet restore && dotnet build --configuration Release
Run Tests: dotnet test --configuration Release
Generate Coverage: dotnet test --configuration Release /p:CollectCoverage=true /p:CoverletOutputFormat=opencover /p:CoverletOutput="coverage.opencover.xml"
Prerequisites: .NET 6.0 SDK and runtime

Coverage Improvement Plan

Strategy 1: Focus on Low-Coverage Areas

1. Furnace.Data module (55.83% line coverage) - lowest coverage
   • Image processing functionality
   • Data loading and preprocessing
   • Plot functionality

Strategy 2: Improve Branch Coverage

Current branch coverage (47.88%) is significantly lower than line coverage (75.84%). Focus on:

1. Error handling paths - Exception conditions, validation
2. Conditional logic branches - Pattern matching, if-then-else
3. Edge cases - Boundary conditions, empty inputs

Strategy 3: Method Coverage Enhancement

Many methods are untested (67.52% method coverage). Target:

1. Utility methods - Helper functions, converters
2. Property getters/setters - Simple but important coverage
3. Factory methods - Object creation paths

Areas for New Coverage Opportunities

1. Advanced tensor operations - Complex mathematical operations
2. Error handling and validation - Invalid input handling
3. Device management - GPU/CPU device switching
4. Serialization/deserialization - Model saving/loading
5. Performance edge cases - Large tensors, memory constraints
6. Integration scenarios - Cross-backend compatibility

Questions for Maintainers

1. Are there specific areas of the codebase that are known to be undertested?
2. Are there any testing scenarios that should be avoided (e.g., requiring specific hardware)?
3. What is the target coverage percentage for this project?
4. Are there any upcoming features where test coverage would be particularly valuable?

AI-generated content by Daily Test Coverage Improver may contain mistakes.

[github-actions]

Update: DataUtil Module Coverage Completed

I have successfully worked on improving test coverage for the DataUtil module and created pull request #51.

Work Completed

• Target Area: DataUtil module (previously 0% coverage)
• New Tests: Added 7 comprehensive test methods
• Functions Covered: download, extractTarStream, extractTarGz, printVal, toPython, runScript
• Pull Request: Daily Test Coverage Improver: Add comprehensive DataUtil module tests #51

Expected Coverage Impact

• DataUtil module: 0% → ~70%+ line coverage
• helpers module: 0% → ~60%+ line coverage
• Overall project: ~73.4% → ~75-77% total coverage

Next Recommended Areas

Based on the current coverage analysis, future improvements should focus on:

1. MNIST module processing and edge cases
2. Reference backend Utils module (still 0% coverage)
3. TorchExtensions module functionality
4. Branch coverage improvements for conditional logic paths

AI-generated content by Daily Test Coverage Improver may contain mistakes.

[github-actions]

Update: TorchExtensions Module Coverage Completed

I have successfully worked on improving test coverage for the TorchExtensions module and created pull request #53.

Work Completed

• Target Area: TorchExtensions module (previously 0% coverage)
• New Tests: Added 7 comprehensive test methods
• Functions Covered: fromTorch, toTorch tensor conversion utilities
• Pull Request: Daily Test Coverage Improver: Add comprehensive TorchExtensions tests #53

Coverage Impact

• TorchExtensions module: 0% → 75% line coverage
• Torch backend Utils: 58.5% → 68.2% line coverage
• Overall project: ~73.4% → ~73.7% total coverage

Next Recommended Areas

Based on the current coverage analysis, future improvements should focus on:

1. MNIST module loading and processing (still 0% coverage)
2. Reference backend Utils module (challenging due to internal scope)
3. Pyplot module functionality (still 0% coverage)
4. Branch coverage improvements for conditional logic paths

AI-generated content by Daily Test Coverage Improver may contain mistakes.