DTL Viewer

DTL Viewer enables developers and data analysts to load and inspect decision tree logic, validate routing rules, and debug domain decision flows.

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About This Tool

DTL Viewer parses decision tree logic definitions from structured sources (JSON, YAML, or inline definitions) and renders a readable data model for analysis without executing domain code. It performs structural validation on nodes, edges, and conditional expressions, and flags syntax anomalies before deeper analysis. The tool emphasizes reproducibility by maintaining deterministic parses of definitions, enabling auditors to compare versions and verify that changes propagate as intended. Core Logic & Features: The tool builds an in-memory graph from JSON, YAML, or inline definitions; it validates node types, edges, conditions, and default paths; it detects cycles and unreachable segments; it provides an explorable view of evaluation paths and supports filtering by node type, criteria, or confidence score. It offers export options to JSON and to lightweight reports suitable for integration with documentation or CI pipelines. Inputs & Outputs: Inputs include DTL definitions in supported formats, optional version markers, and sample input payloads for path tracing. Outputs are a normalized graph model, comprehensive validation reports, path traces, and lineage data that can be serialized as JSON, CSV, or human-readable summaries. Algorithms & Calculations: Parsing uses schema-aware deserialization, graph construction with cycle detection and reachability analysis, and path evaluation via depth-first traversal with memoization and memoized path caches. Error & Edge Cases: It reports syntax errors, missing nodes, ambiguous or conflicting rules, cycles, and unreachable paths; handles large trees via chunked processing, and yields deterministic results for reproducibility; returns precise error codes for automation. Industry/Region & Localization: Aligns with data engineering standards, supports region-specific terminology, and can incorporate locale-aware naming and currency or unit hints when path justification requires it. Assumptions & Exclusions: Assumes DTL definitions contain explicit node identifiers and conditions; excludes runtime execution of domain logic, UI rendering details, performance benchmarks, and code generation.

How to Use

  1. Provide inputs: Upload or paste the DTL definition in JSON, YAML, or inline string.
  2. Validate & parse: Run structural validation to ensure node definitions, edges, and conditions are correct.
  3. Explore paths: Input sample payloads to trace evaluation paths and highlight applicable rules.
  4. Analyze results: Review validation reports, path traces, and export options for documentation or CI pipelines.
  5. Iterate: Update definitions based on findings and re-run analysis.
How to use dtl viewer

Frequently Asked Questions

Find Quick Answers

What formats are supported?
DTL Viewer accepts JSON, YAML, and inline string definitions. The parser validates syntax, node and edge references, and conditional expressions; it reports missing fields, invalid types, and unresolved references, enabling early correction before deployment.
What does validation cover?
Validation covers node definitions, edge connections, and conditional expressions. It flags cycles, unreachable branches, conflicting rules, and inconsistent defaults; results include a structured report and a path-level explanation to guide fixes.
Can I export results?
Yes. The tool can export evaluation path traces, validation reports, and the tree structure as JSON, CSV, or concise summaries; these exports support integration with documentation, testing suites, and CI/CD pipelines.
Are large trees supported?
The tool is designed to scale to large trees; it uses memoization and selective traversal to maintain performance. For extremely large graphs, users may apply filtering or chunked processing to analyze specific regions.

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