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INNOVATIONS IN JSON REPAIR

Documentation for INNOVATIONS_IN_JSON_REPAIR from the Json remedy repository.

Innovations in JSON Repair for JsonRemedy

This document outlines foundational innovative ideas for advancing the capabilities of the JsonRemedy library, drawing inspiration from the concepts detailed in docs/design/1.md. These ideas aim to elevate JsonRemedy to a world-class JSON repair tool by moving beyond deterministic fixes to a more intelligent, adaptable, and robust system.

Core Concepts for Next-Generation JSON Repair

The central theme is a shift from a linear, deterministic repair pipeline to a probabilistic and context-aware repair engine. This engine would explore multiple potential fixes and select the most likely valid JSON structure.

1. Probabilistic Repair Model & Cost System

  • Concept: Instead of a layer making a single, definitive change, it proposes multiple repair candidates.
  • Cost Assignment: Each candidate is assigned a “cost” (or negative log-likelihood). This cost quantifies how drastic or unusual the repair is.
    • Simple fixes (e.g., correcting a quote type) have low costs.
    • Complex changes (e.g., deleting a significant portion of text or deeply restructuring nested elements) have high costs.
  • Goal: To find the repair path that results in valid JSON with the minimum total cost. This reframes repair as finding the “most probable valid JSON given the malformed input.”

2. Beam Search Engine

  • Concept: To manage the exploration of multiple repair candidates without exponential complexity, a beam search algorithm would be employed.
  • Workflow:
    1. The engine starts with the initial input as the first candidate (cost 0).
    2. Each layer processes the current set of promising candidates (those within the “beam”).
    3. A layer can generate multiple new candidates from each input candidate.
    4. After each layer, the engine prunes the expanded list of candidates, keeping only the top N (the beam_width) lowest-cost candidates.
    5. This process continues through all layers.
  • Outcome: The final selection is the candidate with the lowest cumulative cost that successfully validates as JSON.
  • Benefit: This allows JsonRemedy to explore various repair hypotheses simultaneously and choose the globally most plausible one, rather than getting stuck on a locally optimal but incorrect fix.

3. Enhanced Contextual Awareness

  • Concept: To make more informed repair decisions and assign costs more accurately, the JsonContext (the data structure tracking the state of parsing) needs to be significantly enriched.
  • Enhancements: Beyond basic state (e.g., inside an object, inside an array), the context should track:
    • last_significant_char: The last non-whitespace character encountered.
    • last_token_type: The type of the last logical JSON token (e.g., string, number, brace).
    • lookahead_buffer: A small buffer of upcoming characters to allow for more informed decisions without extensive re-parsing.
  • Benefit: A richer context enables more nuanced heuristics. For example, the cost of inserting a comma can be very low if the last_token_type was a value and the next token also appears to be a value.

4. Declarative Rule Set for Heuristics

  • Concept: Many complex repair heuristics currently embedded in imperative code (as seen in some other JSON repair libraries) should be codified as a declarative, extensible rule set.
  • Structure: Each rule would define:
    • A name for the rule.
    • A context_pattern to match against the current JsonContext.
    • A char_pattern to match against the upcoming text.
    • The repair action to take (e.g., insert character, replace text).
    • The cost associated with applying this repair.
  • Benefit: This approach makes the repair logic more transparent, easier to understand, extend, test, and maintain. Complex decision-making becomes a matter of defining and ordering rules.

Implications

Adopting these innovations would represent a significant evolution for JsonRemedy:

  • Increased Robustness: Ability to handle more ambiguous and complex errors by exploring multiple solutions.
  • Greater Accuracy: The cost system, informed by rich context, can lead to more semantically correct repairs.
  • Improved Extensibility: New repair strategies can be added more easily via the declarative rule set.
  • Principled Design: Moves from purely heuristic-based fixes to a more formal, tunable model of repair.

These ideas lay the groundwork for a truly intelligent JSON repair system. Further documents will explore specific aspects of this vision in more detail.