Add AI Testing Methodology for TI and Jellyfish AI Truthful Intelligence

[dreamer-coding]

Overview

Fossil Test should introduce a dedicated AI testing methodology to support Truthful Intelligence (TI) and Jellyfish AI–driven projects. As AI-based systems increasingly rely on probabilistic reasoning and adaptive outputs, traditional deterministic testing approaches may not be sufficient. This enhancement will extend Fossil Test with tailored strategies for testing AI and machine learning systems in a truthful, explainable, and resilient manner.

Motivation

• Current test methodologies (TDD, BDD, DDD) focus on deterministic logic.
• AI systems introduce non-determinism, probabilistic results, and adaptive behaviors.
• Jellyfish AI and TI require assumptions, summaries, and scoring that reflect the reliability and truthfulness of AI outputs.
• Developers need a framework-native way to test AI reasoning and validate AI responses for correctness, truthfulness, and explainability.

Proposed Features

• AI Testing Methodology Mode
  • New methodology flag for AI-specific testing in Fossil Test.
• Probabilistic Assertions
  • Assumptions/assertions that allow ranges, tolerances, or confidence thresholds.
• Truthfulness Metrics
  • TI-powered scoring for evaluating the truthfulness and consistency of AI outputs.
• Explainability Hooks
  • TI summaries can include why AI results were accepted or rejected.
• AI Data Set Integration
  • Support for structured datasets (CSV/JSON/YAML) to drive AI assumption testing.
• Adaptive Test Behavior
  • TI can adapt test thresholds based on historical AI model performance.

Benefits

• Provides a native methodology for testing AI/ML-driven projects.
• Integrates seamlessly with Jellyfish AI assumptions and TI scoring system.
• Encourages truthful, reproducible, and explainable AI behavior validation.
• Extends Fossil Test beyond deterministic systems into AI and probabilistic testing domains.

Tasks

• Define AI testing methodology in Fossil Test documentation.
• Implement methodology flag in the core framework.
• Add probabilistic and fuzzy assumption utilities.
• Integrate TI scoring into AI methodology runs.
• Provide sample test cases for AI/ML workflows.

References

• TI Summaries and Assumptions
• Jellyfish AI Project Guidelines
• Research on AI testing methodologies (fuzzing, probabilistic validation, explainability metrics)

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Category: enhancement TI Jellyfish AI methodology