a239f3a14f
Add cross-implementation consistency tests for dataset hash functionality: ## Test Fixtures - Single file, nested directories, and multiple file test cases - Expected hashes in JSON format for validation ## Test Infrastructure - harness.go: Common test utilities and reference implementation runner - dataset_hash_test.go: Consistency test cases comparing implementations - cmd/update.go: Tool to regenerate expected hashes from reference ## Purpose Ensures hash implementations (Go, C++, Zig) produce identical results across all supported platforms and implementations.
40 lines
1.3 KiB
Markdown
40 lines
1.3 KiB
Markdown
# Consistency Test Fixtures
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This directory contains canonical test fixtures for cross-implementation consistency testing.
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Each implementation (native C++, Go, Zig) must produce identical outputs for these fixtures.
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## Algorithm Specification
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### Dataset Hash Algorithm v1
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1. Recursively collect all regular files (not symlinks, not directories)
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2. Skip hidden files (names starting with '.')
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3. Sort file paths lexicographically (full relative paths)
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4. For each file:
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- Compute SHA256 of file contents
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- Convert to lowercase hex (64 chars)
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5. Combine: SHA256(concatenation of all file hashes in sorted order)
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6. Return lowercase hex (64 chars)
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**Empty directory**: Returns SHA256 of empty string:
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`e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855`
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### Directory Structure
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```
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dataset_hash/
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├── 01_empty_dir/ # Empty directory
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├── 02_single_file/ # One file with "hello world"
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├── 03_nested/ # Nested directories
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├── 04_special_chars/ # Files with spaces and unicode
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└── expected_hashes.json # All expected outputs
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```
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## Adding New Fixtures
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1. Create directory with `input/` subdirectory
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2. Add files to `input/`
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3. Compute expected hash using reference implementation
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4. Add entry to `expected_hashes.json`
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5. Document any special considerations in `README.md`
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