Feature Request: Unified AI System for AI Magic in Fossil Test

[dreamer-coding]

Is your feature request related to a problem? Please describe.
Currently, Fossil Test, Fossil Mock, and Fossil Benchmark support separate AI features (such as anomaly detection, root cause analysis, and predictive modeling) in isolated manners. This fragmented approach creates challenges in maintaining consistency, modularity, and scalability when integrating and expanding AI-driven capabilities across these modules. As a result, the AI features are not able to interact with each other seamlessly, and adding new AI functionalities becomes complex and error-prone.

Describe the solution you’d like
We propose a unified AI system for Fossil Test (and its associated libraries, Fossil Mock and Fossil Benchmark), which will provide a centralized framework for integrating, managing, and extending AI functionalities. This unified system, referred to as AI Magic, would:
1. Standardize Input and Output: A common data pipeline for all AI features, allowing them to handle data in the same format and enabling easier integration.
2. Modular and Extensible: The system should allow the addition of new AI algorithms and models without disrupting existing features. For example, models for anomaly detection, time-series forecasting, or log parsing can be easily added.
3. Centralized AI Interface: A base interface or abstract class (fossil_ai_t) that all AI models (e.g., anomaly detection, NLP for root cause analysis, predictive models) can implement. This will ensure consistent interaction across modules.
4. Cross-Feature AI Integration: Allow AI features to communicate with each other. For example, anomaly detection models can trigger predictive models to adjust thresholds dynamically, or root cause analysis can be triggered by detected anomalies.
5. AI Model Evaluation: Include a system for model evaluation, allowing the continuous improvement and fine-tuning of models based on real-time test data.
6. CLI and TUI Integration: Allow users to interact with AI features easily via command-line and text-based interfaces. For instance, enabling training, prediction, and evaluation of AI models directly from the CLI.
7. Real-time Feedback: Provide users with real-time feedback on AI insights during test execution, with anomaly alerts, prediction results, and diagnostic information presented clearly in the TUI or logs.

Describe alternatives you’ve considered
While the current modular AI implementations in each library (Test, Mock, Benchmark) work individually, there’s a lack of synergy and unified interaction. We’ve considered enhancing the existing models individually, but the need for interaction between these models and consistent data handling makes a unified approach more efficient and maintainable in the long term.

Additional context
The AI Magic system would enable several advanced features across the Fossil ecosystem:
• Anomaly detection: For identifying test failures or performance degradation in real-time.
• Root cause analysis: Using NLP techniques to suggest potential causes for detected anomalies.
• Predictive modeling: For forecasting test behavior and automatically adjusting thresholds or configurations to optimize performance.
• Cross-feature feedback: Allowing AI features like predictive models and anomaly detection to influence each other dynamically.

This system will enhance the overall testing experience, allowing for smarter, more adaptive test environments and making it easier to diagnose issues, optimize performance, and improve test reliability.

This feature request aims to lay out the vision for an AI Magic unified system in Fossil Test and its associated libraries.