I’ve completely rewritten the README.md with an honest, pragmatic approach that accurately reflects what JsonRemedy actually does and should do. Here are the key changes:
README.md Changes
✅ Honest Architecture Description
- Removed misleading claims about “binary pattern matching supremacy”
- Clearly describes the layered approach: Content cleaning → Structural repair → Syntax normalization → Validation → Fallback parsing
- Acknowledges that each layer uses the right tool (regex, state machines, pattern matching)
✅ Realistic Performance Claims
- Removed unsupported benchmark numbers
- Honest about performance characteristics: Fast path for valid JSON, slower repair path for malformed
- Focuses on correctness first, performance second
✅ Practical Use Cases
- Real LLM output examples with actual malformations
- Legacy system integration scenarios
- API response healing patterns
- Configuration file recovery workflows
✅ Clear Limitations
- What JsonRemedy does well vs what it doesn’t do
- Design philosophy of pragmatism over purity
- Honest about trade-offs
Comprehensive Test Plan
The test plan covers 10 major categories with 300+ individual test cases:
1. Layer-Specific Tests (60+ tests)
- Content cleaning (code fences, comments, wrapper text)
- Structural repair (missing delimiters, nesting issues)
- Syntax normalization (quotes, booleans, commas)
- Validation (fast path testing)
- Tolerant parsing (edge case fallbacks)
2. Integration Tests (40+ tests)
- End-to-end LLM output scenarios
- Legacy system outputs (Python, JavaScript)
- API response patterns
- File operations and streaming
3. Performance Tests (20+ tests)
- Valid JSON fast path validation
- Malformed JSON repair timing
- Memory usage profiling
- Large file handling
4. Edge Cases (80+ tests)
- Unicode and encoding issues
- Deeply nested structures
- Malformed numbers and strings
- Severely corrupted inputs
5. Real-World Scenarios (60+ tests)
- ChatGPT/Claude output patterns
- Python pickle-style outputs
- JavaScript object literals
- Truncated streaming data
6. Error Handling (30+ tests)
- Graceful failure modes
- Infinite recursion protection
- Memory exhaustion protection
- Comprehensive logging validation
Sample JSON Test Data
The test plan includes comprehensive fixtures with:
LLM-Style Outputs
// ChatGPT with code fences and comments
// Claude with reasoning text
// Truncated responses
// Mixed syntax issues
Legacy System Formats
# Python-style with True/False/None
# JavaScript object literals
# YAML-like structures
Structural Issues
// Missing delimiters: {"name": "Alice"
// Extra delimiters: {"name": "Alice"}}
// Mismatched: [{"name": "Alice"}]
Complex Real-World Examples
// API responses with HTTP headers
// Config files with comments
// Deeply nested malformed structures
// Large arrays with mixed issues
Key Implementation Insights
1. The Binary Pattern Matching Reality
- Pattern matching is NOT the primary approach for JSON repair
- Useful for final parsing after preprocessing
- Malformed JSON is inherently unpredictable - can’t pattern match unknown malformations
2. The Right Architecture
- Layer 1: Regex preprocessing (perfect for syntax fixes)
- Layer 2: State machine (context-aware structural repairs)
- Layer 3: Jason.decode (leverage battle-tested parser)
- Layer 4: Custom fallback (handle truly edge cases)
3. Success Rate Targets
@success_targets %{
syntax_fixes: 0.95, # Unquoted keys, boolean variants
structural_repairs: 0.85, # Missing braces, commas
content_cleaning: 0.98, # Code fences, comments
complex_scenarios: 0.75, # Multiple issues combined
edge_cases: 0.50 # Severely malformed (graceful failure ok)
}
Next Steps
- Implement the honest layered architecture described in the new README
- Build the comprehensive test suite to validate all repair scenarios
- Focus on correctness and reliability over theoretical performance claims
- Position as a practical tool for real-world JSON repair needs
The new approach is much more maintainable, testable, and honest about what JsonRemedy actually does and should do well.