# PMAT-REPORT-V1 Specification: Universal Rich Reporting with Data Science and ASCII Visualization

## Summary

This document outlines the **PMAT-REPORT-V1 specification** for universal rich reporting across ALL PMAT commands. It integrates advanced data science methods with Toyota Way principles for actionable insights and effective visual management, presented through versatile ASCII and Unicode visualizations.

## Toyota Way Foundations

The reporting framework is built upon key Toyota Way principles to ensure reports are not just informative, but actionable and aligned with continuous improvement:

-   **Visual Management (Mieruka)**: Reports will feature color-coded severity indicators, clear progress bars, and "Andon" signals to immediately highlight critical issues or status changes, enabling rapid understanding and response.
-   **Genchi Genbutsu (Go and See)**: Reports provide evidence-based insights with confidence scores, linking directly to the underlying data and code locations where issues were detected. This ensures decisions are based on verifiable facts.
-   **Jidoka (Autonomation with a Human Touch)**: Reports will include prioritized fix recommendations, complete with auto-fix markers where applicable. The system identifies deviations (stops) and suggests solutions, prompting human intervention only when necessary.
-   **Muda Elimination (Waste Reduction)**: Reports are structured using an inverted pyramid approach, offering progressive disclosure of information. This eliminates "muda" (waste) by presenting the most critical information first and allowing users to drill down for details, avoiding information overload.

## Data Science Methods (5 Algorithms for Deeper Insight)

The PMAT-REPORT-V1 leverages advanced data science techniques to transform raw data into actionable intelligence:

1.  **K-Means Clustering**: Used to group similar defects or code patterns, enabling batch remediation strategies and identifying common underlying issues.
2.  **PageRank Centrality**: Applied to dependency graphs to identify high-impact defects or critical components whose failures would have significant ripple effects across the project.
3.  **Louvain Community Detection**: Utilized to discover architectural boundaries and tightly coupled modules within the codebase, aiding in refactoring efforts and understanding system structure.
4.  **Isolation Forest**: Employed to detect anomalous code patterns, unusual metrics, or rare defect types that might indicate emerging problems or novel vulnerabilities.
5.  **Time Series Analysis**: Tracks quality trends over time, providing change point detection to identify when and where significant shifts in code quality or defect rates occurred.

## 10 Peer-Reviewed Citations

The methodologies employed are grounded in established academic research, drawing from leading conferences and institutions:

-   ACM SODA, ICSE, IEEE ICDM, SANER, ASE conferences (2000-2018)
-   Stanford PageRank paper (foundational work by Page, Brin, Motwani, Winograd)
-   Bell Labs research (contributions to statistical quality control and data analysis)
-   Statistical mechanics (e.g., Blondel et al. on community detection algorithms)

## Command Coverage Matrix

A comprehensive matrix will specify which data science methods are applied to enhance the reporting of each of the 15+ PMAT commands. This includes, but is not limited to: `oracle`, `analyze`, `quality-gate`, `tdg`, `repo-score`, `rust-project-score`, and `five-whys`.

## Open Questions for Team

Before full implementation, the team needs to address several open questions to ensure optimal design and user experience:

1.  **Color palette**: Decision between full ANSI 256 color support for richer visualizations versus basic 16-color compatibility for broader terminal support.
2.  **Graph rendering**: Choice between pure ASCII art for universal compatibility and Unicode characters for more visually appealing and precise graph renderings.
3.  **Data persistence**: Strategies for caching generated report data to improve performance for subsequent views and analyses.
4.  **Configuration scope**: Defining how reporting preferences (e.g., verbosity, visualization style) are configured (CLI flags, project-level TOML, global settings).
5.  **Streaming support**: Investigating the feasibility and benefits of streaming report output for very large projects or real-time monitoring scenarios.

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**Status**: DRAFT - Awaiting team review before implementation.