CodeAnalysis MCP Server

#!/usr/bin/env python3 # MEMORY_ANCHOR: code_health_dashboard """Code Health Dashboard Generator This script generates an HTML dashboard for code health metrics. Maturity: beta Why: A visual dashboard makes it easier to monitor code health metrics over time and identify areas that need attention. HTML format allows for easy sharing and viewing in any browser. """ import os import json import datetime import argparse from pathlib import Path import pandas as pd import plotly.express as px import plotly.graph_objects as go from plotly.subplots import make_subplots # Define paths BASE_DIR = Path(__file__).parent.parent METRICS_FILE = BASE_DIR / "code_health" / "metrics.json" HISTORY_FILE = BASE_DIR / "code_health" / "history.json" OUTPUT_FILE = BASE_DIR / "code_health" / "dashboard.html" def load_metrics(): """Load metrics from file.""" if not os.path.exists(METRICS_FILE): print(f"Metrics file {METRICS_FILE} not found.") return None with open(METRICS_FILE, 'r') as f: return json.load(f) def load_history(): """Load metrics history from file.""" if not os.path.exists(HISTORY_FILE): print(f"History file {HISTORY_FILE} not found.") return [] with open(HISTORY_FILE, 'r') as f: return json.load(f) def create_summary_section(metrics): """Create summary section for the dashboard.""" overall = metrics["overall"] # Create summary figure fig = go.Figure() # Add summary metrics fig.add_trace(go.Indicator( mode="number+gauge+delta", value=overall["complexity"]["average"], title={"text": "Avg. Complexity"}, domain={"x": [0, 0.3], "y": [0, 0.5]}, gauge={ "axis": {"range": [0, 10]}, "bar": {"color": "darkblue"}, "steps": [ {"range": [0, 5], "color": "lightgreen"}, {"range": [5, 7], "color": "yellow"}, {"range": [7, 10], "color": "red"} ], "threshold": { "line": {"color": "red", "width": 4}, "thickness": 0.75, "value": 7 } } )) fig.add_trace(go.Indicator( mode="number+gauge+delta", value=overall["maintainability"]["average"], title={"text": "Maintainability"}, domain={"x": [0.35, 0.65], "y": [0, 0.5]}, gauge={ "axis": {"range": [0, 100]}, "bar": {"color": "darkblue"}, "steps": [ {"range": [0, 40], "color": "red"}, {"range": [40, 60], "color": "yellow"}, {"range": [60, 100], "color": "lightgreen"} ], "threshold": { "line": {"color": "red", "width": 4}, "thickness": 0.75, "value": 40 } } )) fig.add_trace(go.Indicator( mode="number+gauge+delta", value=overall["coverage"]["total"], title={"text": "Test Coverage (%)"}, domain={"x": [0.7, 1], "y": [0, 0.5]}, gauge={ "axis": {"range": [0, 100]}, "bar": {"color": "darkblue"}, "steps": [ {"range": [0, 50], "color": "red"}, {"range": [50, 80], "color": "yellow"}, {"range": [80, 100], "color": "lightgreen"} ], "threshold": { "line": {"color": "red", "width": 4}, "thickness": 0.75, "value": 80 } } )) # Add code size metrics fig.add_trace(go.Indicator( mode="number", value=overall["raw"]["total_sloc"], title={"text": "Total SLOC"}, domain={"x": [0, 0.3], "y": [0.6, 1]} )) fig.add_trace(go.Indicator( mode="number", value=overall["raw"]["comment_ratio"], title={"text": "Comment Ratio"}, number={"suffix": "x"}, domain={"x": [0.35, 0.65], "y": [0.6, 1]} )) fig.add_trace(go.Indicator( mode="number", value=overall["churn"]["average_churn_rate"], title={"text": "Avg. Churn Rate"}, domain={"x": [0.7, 1], "y": [0.6, 1]} )) fig.update_layout( title="Code Health Summary", height=400 ) return fig def create_metrics_over_time_section(history): """Create metrics over time section for the dashboard.""" if not history: return None # Convert to DataFrame df = pd.DataFrame([ { "timestamp": entry["timestamp"], "complexity": entry["overall"]["complexity"]["average"], "maintainability": entry["overall"]["maintainability"]["average"], "coverage": entry["overall"]["coverage"]["total"], "churn_rate": entry["overall"]["churn"]["average_churn_rate"] } for entry in history ]) # Convert timestamp to datetime df["timestamp"] = pd.to_datetime(df["timestamp"]) # Sort by timestamp df = df.sort_values("timestamp") # Create figure fig = make_subplots( rows=2, cols=2, subplot_titles=("Average Complexity", "Maintainability", "Test Coverage (%)", "Average Churn Rate") ) fig.add_trace( go.Scatter(x=df["timestamp"], y=df["complexity"], mode="lines+markers", name="Complexity"), row=1, col=1 ) fig.add_trace( go.Scatter(x=df["timestamp"], y=df["maintainability"], mode="lines+markers", name="Maintainability"), row=1, col=2 ) fig.add_trace( go.Scatter(x=df["timestamp"], y=df["coverage"], mode="lines+markers", name="Coverage"), row=2, col=1 ) fig.add_trace( go.Scatter(x=df["timestamp"], y=df["churn_rate"], mode="lines+markers", name="Churn Rate"), row=2, col=2 ) fig.update_layout( title="Metrics Over Time", height=600, showlegend=False ) return fig def create_file_metrics_section(metrics): """Create file metrics section for the dashboard.""" files = metrics["files"] # Prepare data for complexity complexity_data = { file_path: data["complexity"]["average"] for file_path, data in files.items() } # Sort by complexity and get top 20 complexity_data = dict(sorted(complexity_data.items(), key=lambda x: x[1], reverse=True)[:20]) # Create complexity figure complexity_fig = px.bar( x=list(complexity_data.keys()), y=list(complexity_data.values()), title="Top 20 Files by Complexity", labels={"x": "File", "y": "Average Complexity"} ) complexity_fig.update_layout(height=400, xaxis_tickangle=-45) # Prepare data for maintainability maintainability_data = { file_path: data["maintainability"]["maintainability_index"] for file_path, data in files.items() } # Sort by maintainability (ascending) and get bottom 20 maintainability_data = dict(sorted(maintainability_data.items(), key=lambda x: x[1])[:20]) # Create maintainability figure maintainability_fig = px.bar( x=list(maintainability_data.keys()), y=list(maintainability_data.values()), title="Bottom 20 Files by Maintainability", labels={"x": "File", "y": "Maintainability Index"} ) maintainability_fig.update_layout(height=400, xaxis_tickangle=-45) # Prepare data for churn churn_data = { file_path: data["churn"]["churn_rate"] for file_path, data in files.items() if data["churn"]["commits"] > 0 } # Sort by churn rate and get top 20 churn_data = dict(sorted(churn_data.items(), key=lambda x: x[1], reverse=True)[:20]) # Create churn figure churn_fig = px.bar( x=list(churn_data.keys()), y=list(churn_data.values()), title="Top 20 Files by Churn Rate", labels={"x": "File", "y": "Churn Rate"} ) churn_fig.update_layout(height=400, xaxis_tickangle=-45) # Prepare data for coverage coverage_data = { file_path: data["coverage"] for file_path, data in files.items() if data["coverage"] > 0 } # Sort by coverage (ascending) and get bottom 20 coverage_data = dict(sorted(coverage_data.items(), key=lambda x: x[1])[:20]) # Create coverage figure coverage_fig = px.bar( x=list(coverage_data.keys()), y=list(coverage_data.values()), title="Bottom 20 Files by Test Coverage", labels={"x": "File", "y": "Coverage (%)"} ) coverage_fig.update_layout(height=400, xaxis_tickangle=-45) return complexity_fig, maintainability_fig, churn_fig, coverage_fig def create_dashboard(metrics, history): """Create the dashboard HTML.""" # Create summary section summary_fig = create_summary_section(metrics) # Create metrics over time section history_fig = create_metrics_over_time_section(history) # Create file metrics section complexity_fig, maintainability_fig, churn_fig, coverage_fig = create_file_metrics_section(metrics) # Create HTML html = f""" <!DOCTYPE html> <html> <head> <title>Code Health Dashboard</title> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1"> <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0-alpha1/dist/css/bootstrap.min.css" rel="stylesheet"> <script src="https://cdn.plot.ly/plotly-latest.min.js"></script> <style> body {{ padding: 20px; font-family: Arial, sans-serif; }} .dashboard-header {{ margin-bottom: 30px; }} .dashboard-section {{ margin-bottom: 30px; padding: 20px; border-radius: 5px; background-color: #f8f9fa; }} </style> </head> <body> <div class="container-fluid"> <div class="dashboard-header"> <h1>Code Health Dashboard</h1> <p>Generated on {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}</p> </div> <div class="dashboard-section"> <div id="summary-chart"></div> </div> <div class="dashboard-section"> <div id="history-chart"></div> </div> <div class="dashboard-section"> <div class="row"> <div class="col-md-6"> <div id="complexity-chart"></div> </div> <div class="col-md-6"> <div id="maintainability-chart"></div> </div> </div> </div> <div class="dashboard-section"> <div class="row"> <div class="col-md-6"> <div id="churn-chart"></div> </div> <div class="col-md-6"> <div id="coverage-chart"></div> </div> </div> </div> </div> <script> // Summary chart var summaryData = {summary_fig.to_json()}; Plotly.newPlot('summary-chart', summaryData.data, summaryData.layout); // History chart {"var historyData = " + history_fig.to_json() + ";" if history_fig else ""} {"Plotly.newPlot('history-chart', historyData.data, historyData.layout);" if history_fig else ""} // Complexity chart var complexityData = {complexity_fig.to_json()}; Plotly.newPlot('complexity-chart', complexityData.data, complexityData.layout); // Maintainability chart var maintainabilityData = {maintainability_fig.to_json()}; Plotly.newPlot('maintainability-chart', maintainabilityData.data, maintainabilityData.layout); // Churn chart var churnData = {churn_fig.to_json()}; Plotly.newPlot('churn-chart', churnData.data, churnData.layout); // Coverage chart var coverageData = {coverage_fig.to_json()}; Plotly.newPlot('coverage-chart', coverageData.data, coverageData.layout); </script> </body> </html> """ return html def main(): parser = argparse.ArgumentParser(description="Generate code health dashboard") parser.add_argument("--output", help="Output file path", default=str(OUTPUT_FILE)) args = parser.parse_args() output_file = Path(args.output) # Load metrics metrics = load_metrics() if not metrics: return # Load history history = load_history() # Create dashboard print("Generating dashboard...") dashboard_html = create_dashboard(metrics, history) # Save dashboard with open(output_file, 'w') as f: f.write(dashboard_html) print(f"Dashboard saved to {output_file}") if __name__ == "__main__": main()