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Description
Overview
This epic tracks the creation of comprehensive integration tests for all modules in the AiDotNet library. The goal is to:
- Find bugs through edge cases, numerical stability testing, and error condition handling
- Verify correctness by comparing against established frameworks (PyTorch, scikit-learn, TensorFlow)
Testing Philosophy
All integration tests should:
- NOT trust the code - verify mathematical correctness independently
- Test edge cases (empty inputs, single elements, very large/small values)
- Test numerical stability (NaN, Infinity, very small gradients)
- Test error handling (mismatched dimensions, invalid parameters)
- Compare results against reference implementations where possible
- Test serialization/deserialization round-trips
- Test Clone() produces identical behavior
Child Issues Summary
Total: 53 child issues created
P0 - CRITICAL (6 issues)
| Issue | Module | Coverage | Files |
|---|---|---|---|
| #616 | NeuralNetworks | 0% | 110+ |
| #617 | Clustering | 0% | 90+ |
| #618 | Optimizers | 7% | 42 |
| #619 | LossFunctions | 85% | 34 |
| #620 | DecompositionMethods | 62% | 21 |
| #621 | Regression | 0% | 45 |
P1 - HIGH (7 issues)
| Issue | Module | Files |
|---|---|---|
| #622 | ReinforcementLearning | 83 |
| #623 | MetaLearning | 3 |
| #624 | FeatureSelectors | 8 |
| #625 | CrossValidators | 9 |
| #626 | Preprocessing | 5 |
| #627 | Engines | 1 |
| #628 | Autodiff | 5 |
P2 - MEDIUM (14 issues)
| Issue | Module | Files |
|---|---|---|
| #630 | ActiveLearning | 46 |
| #631 | AdversarialRobustness | 15 |
| #632 | Augmentation | 58 |
| #633 | ContinualLearning | 36 |
| #634 | CurriculumLearning | 18 |
| #635 | FederatedLearning | 60 |
| #636 | KnowledgeDistillation | 46 |
| #637 | PhysicsInformed | 49 |
| #638 | SelfSupervisedLearning | 56 |
| #639 | NestedLearning | 2 |
| #640 | FineTuning | 16 |
| #641 | LoRA | 37 |
| #642 | MixedPrecision | 4 |
| #643 | Pruning | 5 |
P3 - STANDARD (30 issues)
| Issue | Module | Files |
|---|---|---|
| #644 | Agents | 7 |
| #645 | Benchmarking | 16 |
| #646 | CheckpointManagement | 2 |
| #647 | ComputerVision | 59 |
| #648 | Configuration | 37 |
| #649 | DataProcessor | 1 |
| #650 | DataVersionControl | 2 |
| #651 | DataVersioning | 2 |
| #652 | Deployment | 51 |
| #653 | Diagnostics | 2 |
| #654 | DistributedTraining | 34 |
| #655 | Evaluation | 2 |
| #656 | ExperimentTracking | 2 |
| #657 | HyperparameterOptimization | 9 |
| #658 | InferenceOptimization | 36 |
| #659 | Interpretability | 18 |
| #660 | JitCompiler | 228 |
| #661 | LanguageModels | 9 |
| #662 | Logging | 9 |
| #663 | Metrics | 9 |
| #664 | ModelRegistry | 2 |
| #665 | ProgramSynthesis | 104 |
| #666 | PromptEngineering | 48 |
| #667 | Prototypes | 5 |
| #668 | Reasoning | 56 |
| #669 | Serialization | 6 |
| #670 | Serving | 8 |
| #671 | Tokenization | 25 |
| #672 | Tools | 22 |
| #673 | TrainingMonitoring | 16 |
Existing Tests (Complete - May Need Review)
These modules already have integration tests:
- Classification - 8 test files (PR test: add comprehensive classification integration tests #614)
- Helpers - 14 test files
- Statistics - 15 test files
- AdvancedLinearAlgebra - 15 test files
- LinearAlgebra - 5 test files
- AutoML - 5 test files
- TransferLearning - 3 test files
- Genetics - 2 test files
- Geometry - 2 test files
- Caching - 3 test files
- FitDetectors - 3 test files
- And 20+ more single-file test modules...
Progress Tracking
| Priority | Issues | Status |
|---|---|---|
| P0 Critical | 6 | All issues created |
| P1 High | 7 | All issues created |
| P2 Medium | 14 | All issues created |
| P3 Standard | 30 | All issues created |
| Total | 57 | All created |
Definition of Done
A module is considered complete when:
- All public classes have integration tests
- Edge cases are tested (empty, null, boundary values)
- Numerical stability is verified (no NaN/Infinity leaks)
- Error handling is tested (appropriate exceptions thrown)
- Serialization round-trip works correctly
- Clone() produces identical predictions
- Tests pass on both net8.0 and net471
- Code coverage for the module is at least 80%
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