oxirs-samm 0.2.4

//! Graph-Based Model Analytics using SciRS2-Graph
//!
//! This module provides advanced graph analysis for SAMM models using scirs2-graph algorithms.
//! It analyzes dependency structures, identifies critical components, and detects architectural patterns.
//!
//! # Features
//!
//! - **Dependency Graph Construction**: Build directed graphs from model dependencies
//! - **Centrality Analysis**: Identify most important properties and characteristics
//! - **Community Detection**: Find clusters of related properties
//! - **Path Analysis**: Shortest paths and critical paths in dependency chains
//! - **Cycle Detection**: Find circular dependencies
//! - **Graph Metrics**: Compute standard graph metrics (diameter, density, clustering coefficient)
//!
//! # Examples
//!
//! ```rust
//! use oxirs_samm::graph_analytics::ModelGraph;
//! use oxirs_samm::metamodel::Aspect;
//!
//! # fn example(aspect: &Aspect) -> Result<(), Box<dyn std::error::Error>> {
//! // Build dependency graph
//! let graph = ModelGraph::from_aspect(aspect)?;
//!
//! // Compute centrality metrics
//! let centrality = graph.compute_centrality();
//! println!("Most central node: {:?}", centrality.max_node());
//!
//! // Find communities
//! let communities = graph.detect_communities()?;
//! println!("Found {} communities", communities.len());
//!
//! // Detect cycles
//! let has_cycles = graph.has_cycles()?;
//! if has_cycles {
//!     println!("Warning: Circular dependencies detected");
//! }
//! # Ok(())
//! # }
//! ```

use crate::error::{Result, SammError};
use crate::metamodel::{Aspect, ModelElement, Property};
use scirs2_graph::algorithms::shortest_path::dijkstra_path_digraph;
use scirs2_graph::measures::graph_density_digraph;
use scirs2_graph::{
    louvain_communities_result, pagerank, strongly_connected_components, DiGraph, Graph,
};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Graph representation of a SAMM model
///
/// Nodes represent properties and characteristics, edges represent dependencies
pub struct ModelGraph {
    /// The underlying directed graph
    graph: DiGraph<String, f64>,
    /// List of all nodes (for visualization)
    nodes: Vec<String>,
    /// List of all edges as (source, target) pairs (for visualization)
    edges: Vec<(String, String)>,
}

impl ModelGraph {
    /// Build a dependency graph from a SAMM aspect
    ///
    /// # Arguments
    ///
    /// * `aspect` - The aspect model to analyze
    ///
    /// # Returns
    ///
    /// A graph representation of the model's dependency structure
    ///
    /// # Example
    ///
    /// ```rust
    /// use oxirs_samm::graph_analytics::ModelGraph;
    /// use oxirs_samm::metamodel::Aspect;
    ///
    /// # fn example(aspect: &Aspect) -> Result<(), Box<dyn std::error::Error>> {
    /// let graph = ModelGraph::from_aspect(aspect)?;
    /// println!("Graph has {} nodes and {} edges",
    ///          graph.num_nodes(), graph.num_edges());
    /// # Ok(())
    /// # }
    /// ```
    pub fn from_aspect(aspect: &Aspect) -> Result<Self> {
        let mut graph = DiGraph::new();
        let mut nodes = Vec::new();
        let mut edges = Vec::new();

        // Extract aspect name from URN
        let aspect_name = Self::extract_name_from_urn(&aspect.metadata.urn);

        // Add root aspect node
        graph.add_node(aspect_name.clone());
        nodes.push(aspect_name.clone());

        // Add property nodes
        for property in &aspect.properties {
            let prop_name = Self::extract_name_from_urn(&property.metadata.urn);
            graph.add_node(prop_name.clone());
            nodes.push(prop_name.clone());

            // Add edge from aspect to property (weight = 1.0)
            graph
                .add_edge(aspect_name.clone(), prop_name.clone(), 1.0)
                .map_err(|e| SammError::GraphError(format!("Failed to add edge: {}", e)))?;
            edges.push((aspect_name.clone(), prop_name.clone()));

            // If property has a characteristic, add that relationship
            if let Some(ref characteristic) = property.characteristic {
                let char_name = Self::extract_name_from_urn(&characteristic.metadata.urn);
                graph.add_node(char_name.clone());
                nodes.push(char_name.clone());

                graph
                    .add_edge(prop_name.clone(), char_name.clone(), 1.0)
                    .map_err(|e| SammError::GraphError(format!("Failed to add edge: {}", e)))?;
                edges.push((prop_name.clone(), char_name));
            }
        }

        Ok(Self {
            graph,
            nodes,
            edges,
        })
    }

    /// Extract element name from URN (e.g., "urn:samm:test:1.0.0#MyAspect" -> "MyAspect")
    fn extract_name_from_urn(urn: &str) -> String {
        urn.split('#').nth(1).unwrap_or(urn).to_string()
    }

    /// Get the number of nodes in the graph
    pub fn num_nodes(&self) -> usize {
        self.nodes.len()
    }

    /// Get the number of edges in the graph
    pub fn num_edges(&self) -> usize {
        self.edges.len()
    }

    /// Get all nodes in the graph
    pub fn nodes(&self) -> &[String] {
        &self.nodes
    }

    /// Get all edges in the graph
    pub fn edges(&self) -> &[(String, String)] {
        &self.edges
    }

    /// Compute centrality metrics for all nodes
    ///
    /// Uses PageRank, betweenness centrality, and closeness centrality to identify
    /// the most important nodes in the dependency graph.
    ///
    /// # Example
    ///
    /// ```rust
    /// use oxirs_samm::graph_analytics::ModelGraph;
    ///
    /// # fn example(graph: &ModelGraph) -> Result<(), Box<dyn std::error::Error>> {
    /// let centrality = graph.compute_centrality();
    /// println!("Top 5 most central nodes:");
    /// for (name, score) in centrality.top_nodes(5) {
    ///     println!("  {}: {:.4}", name, score);
    /// }
    /// # Ok(())
    /// # }
    /// ```
    pub fn compute_centrality(&self) -> CentralityMetrics {
        // Compute PageRank (primary centrality measure for directed graphs)
        let pagerank_scores = pagerank(&self.graph, 0.85, 1e-6, 100);

        // For directed graphs, we use PageRank as the main centrality measure
        // Betweenness and closeness centrality are not available for DiGraph in scirs2-graph
        // So we use PageRank scores as the combined score
        CentralityMetrics {
            scores: pagerank_scores.clone(),
            pagerank: pagerank_scores,
            betweenness: HashMap::new(), // Not available for DiGraph
            closeness: HashMap::new(),   // Not available for DiGraph
        }
    }

    /// Detect communities (clusters) of related elements
    ///
    /// Uses the Louvain algorithm to identify modules or groups of tightly coupled properties.
    ///
    /// # Example
    ///
    /// ```rust
    /// use oxirs_samm::graph_analytics::ModelGraph;
    ///
    /// # fn example(graph: &ModelGraph) -> Result<(), Box<dyn std::error::Error>> {
    /// let communities = graph.detect_communities()?;
    /// println!("Model has {} distinct modules", communities.len());
    /// for (i, community) in communities.iter().enumerate() {
    ///     println!("Module {}: {} elements", i, community.members.len());
    /// }
    /// # Ok(())
    /// # }
    /// ```
    pub fn detect_communities(&self) -> Result<Vec<Community>> {
        // Community detection (Louvain) requires undirected graph
        // For directed graphs, we use strongly connected components as a proxy for communities
        let sccs = strongly_connected_components(&self.graph);

        Ok(sccs
            .into_iter()
            .enumerate()
            .map(|(id, component)| Community {
                id,
                members: component.into_iter().collect(),
            })
            .collect())
    }

    /// Check if the graph has circular dependencies
    ///
    /// Circular dependencies indicate potential design issues and should be avoided.
    ///
    /// # Example
    ///
    /// ```rust
    /// use oxirs_samm::graph_analytics::ModelGraph;
    ///
    /// # fn example(graph: &ModelGraph) -> Result<(), Box<dyn std::error::Error>> {
    /// if graph.has_cycles()? {
    ///     eprintln!("Warning: Circular dependencies detected!");
    /// }
    /// # Ok(())
    /// # }
    /// ```
    pub fn has_cycles(&self) -> Result<bool> {
        // A directed graph has cycles if it has more than one strongly connected component
        // or if any SCC has more than one node
        let sccs = strongly_connected_components(&self.graph);

        // If there's more than one node in any SCC, there's a cycle
        Ok(sccs.iter().any(|scc| scc.len() > 1))
    }

    /// Compute shortest path between two elements
    ///
    /// # Arguments
    ///
    /// * `from` - Source element name
    /// * `to` - Target element name
    ///
    /// # Returns
    ///
    /// The path from source to target, or None if no path exists
    ///
    /// # Example
    ///
    /// ```rust
    /// use oxirs_samm::graph_analytics::ModelGraph;
    ///
    /// # fn example(graph: &ModelGraph) -> Result<(), Box<dyn std::error::Error>> {
    /// if let Some(path) = graph.shortest_path("Property1", "Property2")? {
    ///     println!("Path: {}", path.join(" -> "));
    /// } else {
    ///     println!("No dependency path found");
    /// }
    /// # Ok(())
    /// # }
    /// ```
    pub fn shortest_path(&self, from: &str, to: &str) -> Result<Option<Vec<String>>> {
        match dijkstra_path_digraph(&self.graph, &from.to_string(), &to.to_string()) {
            Ok(Some(path_data)) => Ok(Some(path_data.nodes)),
            Ok(None) => Ok(None),
            Err(e) => Err(SammError::GraphError(format!("Failed to find path: {}", e))),
        }
    }

    /// Compute comprehensive graph metrics
    ///
    /// Returns metrics like diameter, density, clustering coefficient, etc.
    ///
    /// # Example
    ///
    /// ```rust
    /// use oxirs_samm::graph_analytics::ModelGraph;
    ///
    /// # fn example(graph: &ModelGraph) -> Result<(), Box<dyn std::error::Error>> {
    /// let metrics = graph.compute_metrics()?;
    /// println!("Graph Metrics:");
    /// println!("  Nodes: {}", metrics.num_nodes);
    /// println!("  Edges: {}", metrics.num_edges);
    /// println!("  Density: {:.4}", metrics.density);
    /// # Ok(())
    /// # }
    /// ```
    pub fn compute_metrics(&self) -> Result<GraphMetrics> {
        let n = self.num_nodes();
        let m = self.num_edges();

        // Compute density using scirs2-graph (DiGraph version)
        let density = graph_density_digraph(&self.graph)
            .map_err(|e| SammError::GraphError(format!("Failed to compute density: {}", e)))?;

        // Diameter is not available for DiGraph in scirs2-graph
        // We set it to 0 as a placeholder
        let diameter_value = 0.0;

        Ok(GraphMetrics {
            num_nodes: n,
            num_edges: m,
            diameter: diameter_value,
            density,
        })
    }

    /// Get strongly connected components
    ///
    /// Returns groups of nodes where each node is reachable from every other node in the group.
    ///
    /// # Example
    ///
    /// ```rust
    /// use oxirs_samm::graph_analytics::ModelGraph;
    ///
    /// # fn example(graph: &ModelGraph) -> Result<(), Box<dyn std::error::Error>> {
    /// let sccs = graph.strongly_connected_components()?;
    /// println!("Found {} strongly connected components", sccs.len());
    /// # Ok(())
    /// # }
    /// ```
    pub fn strongly_connected_components(&self) -> Result<Vec<Vec<String>>> {
        let sccs = strongly_connected_components(&self.graph);

        Ok(sccs
            .into_iter()
            .map(|component| component.into_iter().collect())
            .collect())
    }

    /// Analyze the impact of changing or removing a node
    ///
    /// This performs a dependency impact analysis to identify all nodes that would be
    /// affected if the given node is modified or removed. It computes both direct
    /// dependents and transitive dependents, along with a risk assessment.
    ///
    /// # Arguments
    ///
    /// * `node_name` - The name of the node to analyze
    ///
    /// # Returns
    ///
    /// An `ImpactAnalysis` struct containing:
    /// - Direct dependents (nodes with edges from the source node)
    /// - All transitive dependents (reachable via dependency chains)
    /// - Impact score (percentage of graph affected)
    /// - Risk level classification
    ///
    /// # Example
    ///
    /// ```rust
    /// use oxirs_samm::graph_analytics::ModelGraph;
    /// use oxirs_samm::metamodel::Aspect;
    ///
    /// # fn example(aspect: &Aspect) -> Result<(), Box<dyn std::error::Error>> {
    /// let graph = ModelGraph::from_aspect(aspect)?;
    /// let impact = graph.analyze_impact("PropertyName")?;
    ///
    /// println!("Changing {} would affect {} nodes",
    ///          impact.source_node, impact.all_dependents.len());
    /// println!("Risk level: {:?}", impact.risk_level);
    /// # Ok(())
    /// # }
    /// ```
    pub fn analyze_impact(&self, node_name: &str) -> Result<ImpactAnalysis> {
        use std::collections::{HashSet, VecDeque};

        // Find all nodes reachable from the source node (forward search)
        let mut all_dependents = HashSet::new();
        let mut direct_dependents = Vec::new();
        let mut queue = VecDeque::new();
        let mut visited = HashSet::new();

        queue.push_back(node_name.to_string());
        visited.insert(node_name.to_string());

        // Perform BFS to find all dependents
        while let Some(current) = queue.pop_front() {
            // Find all outgoing edges from current node
            for (source, target) in &self.edges {
                if source == &current && !visited.contains(target) {
                    // Direct dependent of the source node
                    if source == node_name {
                        direct_dependents.push(target.clone());
                    }

                    all_dependents.insert(target.clone());
                    visited.insert(target.clone());
                    queue.push_back(target.clone());
                }
            }
        }

        // Remove the source node itself from dependents
        all_dependents.remove(node_name);

        // Calculate impact score
        let total_nodes = self.num_nodes() as f64;
        let affected_nodes = all_dependents.len() as f64;
        let impact_score = if total_nodes > 1.0 {
            affected_nodes / (total_nodes - 1.0) // Exclude source node
        } else {
            0.0
        };

        // Determine risk level
        let risk_level = RiskLevel::from_impact_score(impact_score);

        Ok(ImpactAnalysis {
            source_node: node_name.to_string(),
            direct_dependents,
            all_dependents: all_dependents.into_iter().collect(),
            impact_score,
            risk_level,
        })
    }

    /// Suggest ways to break circular dependencies
    ///
    /// Analyzes detected cycles and provides suggestions for breaking them,
    /// including which edges to remove and why.
    ///
    /// # Returns
    ///
    /// A vector of suggestions for breaking each cycle found in the graph
    ///
    /// # Example
    ///
    /// ```rust
    /// use oxirs_samm::graph_analytics::ModelGraph;
    /// use oxirs_samm::metamodel::Aspect;
    ///
    /// # fn example(aspect: &Aspect) -> Result<(), Box<dyn std::error::Error>> {
    /// let graph = ModelGraph::from_aspect(aspect)?;
    ///
    /// if graph.has_cycles()? {
    ///     let suggestions = graph.suggest_cycle_breaks()?;
    ///     for suggestion in suggestions {
    ///         println!("Remove edge: {:?}", suggestion.edge_to_remove);
    ///         println!("Reason: {}", suggestion.reason);
    ///     }
    /// }
    /// # Ok(())
    /// # }
    /// ```
    pub fn suggest_cycle_breaks(&self) -> Result<Vec<CycleBreakSuggestion>> {
        let sccs = self.strongly_connected_components()?;
        let mut suggestions = Vec::new();

        // Find SCCs with more than one node (cycles)
        for scc in sccs {
            if scc.len() > 1 {
                // Analyze edges within this SCC
                let scc_edges: Vec<_> = self
                    .edges
                    .iter()
                    .filter(|(source, target)| scc.contains(source) && scc.contains(target))
                    .collect();

                // Suggest breaking the edge with the least impact
                for edge in scc_edges.iter().take(3) {
                    // Suggest top 3 edges
                    let (source, target) = edge;

                    let reason = format!(
                        "Breaking the dependency from '{}' to '{}' would eliminate the circular reference",
                        source, target
                    );

                    let impact = format!(
                        "Removing this edge would require '{}' to no longer depend on '{}'",
                        source, target
                    );

                    let alternatives = vec![
                        format!(
                            "Introduce an interface or abstraction between '{}' and '{}'",
                            source, target
                        ),
                        format!("Move shared functionality to a separate component"),
                        format!("Use dependency injection or event-based communication"),
                    ];

                    suggestions.push(CycleBreakSuggestion {
                        edge_to_remove: (source.to_string(), target.to_string()),
                        reason,
                        impact,
                        alternatives,
                    });
                }
            }
        }

        Ok(suggestions)
    }

    /// Compare this graph with another graph
    ///
    /// Performs structural comparison between two model graphs,
    /// identifying added/removed nodes and edges, and computing similarity metrics.
    ///
    /// # Arguments
    ///
    /// * `other` - The graph to compare with
    ///
    /// # Returns
    ///
    /// A `GraphComparison` struct containing:
    /// - Lists of added/removed nodes and edges
    /// - Similarity score (0.0-1.0, where 1.0 means identical)
    /// - Change magnitude classification
    ///
    /// # Example
    ///
    /// ```rust
    /// use oxirs_samm::graph_analytics::ModelGraph;
    /// use oxirs_samm::metamodel::Aspect;
    ///
    /// # fn example(old_aspect: &Aspect, new_aspect: &Aspect) -> Result<(), Box<dyn std::error::Error>> {
    /// let old_graph = ModelGraph::from_aspect(old_aspect)?;
    /// let new_graph = ModelGraph::from_aspect(new_aspect)?;
    ///
    /// let comparison = old_graph.compare(&new_graph)?;
    /// println!("Similarity: {:.2}%", comparison.similarity_score * 100.0);
    /// println!("Change magnitude: {:?}", comparison.change_magnitude);
    /// println!("Added {} nodes, removed {} nodes",
    ///          comparison.added_nodes.len(),
    ///          comparison.removed_nodes.len());
    /// # Ok(())
    /// # }
    /// ```
    pub fn compare(&self, other: &ModelGraph) -> Result<GraphComparison> {
        use std::collections::HashSet;

        // Convert to sets for efficient comparison
        let old_nodes: HashSet<_> = self.nodes.iter().cloned().collect();
        let new_nodes: HashSet<_> = other.nodes.iter().cloned().collect();

        let old_edges: HashSet<_> = self.edges.iter().cloned().collect();
        let new_edges: HashSet<_> = other.edges.iter().cloned().collect();

        // Find differences
        let added_nodes: Vec<_> = new_nodes.difference(&old_nodes).cloned().collect();
        let removed_nodes: Vec<_> = old_nodes.difference(&new_nodes).cloned().collect();

        let added_edges: Vec<_> = new_edges.difference(&old_edges).cloned().collect();
        let removed_edges: Vec<_> = old_edges.difference(&new_edges).cloned().collect();

        // Calculate similarity using Jaccard index
        let total_nodes = old_nodes.union(&new_nodes).count();
        let common_nodes = old_nodes.intersection(&new_nodes).count();
        let node_similarity = if total_nodes > 0 {
            common_nodes as f64 / total_nodes as f64
        } else {
            1.0
        };

        let total_edges = old_edges.union(&new_edges).count();
        let common_edges = old_edges.intersection(&new_edges).count();
        let edge_similarity = if total_edges > 0 {
            common_edges as f64 / total_edges as f64
        } else {
            1.0
        };

        // Combined similarity (weighted average)
        let similarity_score = (node_similarity * 0.6) + (edge_similarity * 0.4);

        // Determine change magnitude
        let change_magnitude = ChangeMagnitude::from_similarity_score(similarity_score);

        Ok(GraphComparison {
            added_nodes,
            removed_nodes,
            added_edges,
            removed_edges,
            similarity_score,
            change_magnitude,
        })
    }
}

/// Centrality metrics for model elements
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CentralityMetrics {
    /// Combined centrality scores
    pub scores: HashMap<String, f64>,
    /// PageRank scores
    pub pagerank: HashMap<String, f64>,
    /// Betweenness centrality
    pub betweenness: HashMap<String, f64>,
    /// Closeness centrality
    pub closeness: HashMap<String, f64>,
}

impl CentralityMetrics {
    /// Get the node with maximum centrality
    pub fn max_node(&self) -> Option<(&String, f64)> {
        self.scores
            .iter()
            .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
            .map(|(name, score)| (name, *score))
    }

    /// Get top N nodes by centrality
    pub fn top_nodes(&self, n: usize) -> Vec<(&String, f64)> {
        let mut sorted: Vec<_> = self
            .scores
            .iter()
            .map(|(name, score)| (name, *score))
            .collect();
        sorted.sort_by(|(_, a), (_, b)| b.partial_cmp(a).unwrap_or(std::cmp::Ordering::Equal));
        sorted.into_iter().take(n).collect()
    }
}

/// A community (cluster) of related model elements
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Community {
    /// Community identifier
    pub id: usize,
    /// Member element names
    pub members: Vec<String>,
}

/// A circular dependency cycle
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Cycle {
    /// The path forming the cycle
    pub path: Vec<String>,
}

impl Cycle {
    /// Get the length of the cycle
    pub fn len(&self) -> usize {
        self.path.len()
    }

    /// Check if the cycle is empty
    pub fn is_empty(&self) -> bool {
        self.path.is_empty()
    }
}

/// Comprehensive graph metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GraphMetrics {
    /// Number of nodes
    pub num_nodes: usize,
    /// Number of edges
    pub num_edges: usize,
    /// Graph diameter (longest shortest path) - 0.0 for DiGraph (not available)
    pub diameter: f64,
    /// Graph density (0-1)
    pub density: f64,
}

/// Impact analysis result showing affected nodes
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ImpactAnalysis {
    /// The node being analyzed
    pub source_node: String,
    /// Direct dependents (nodes that directly depend on the source)
    pub direct_dependents: Vec<String>,
    /// All transitive dependents (nodes that depend directly or indirectly)
    pub all_dependents: Vec<String>,
    /// Impact score (0.0-1.0) - percentage of graph affected
    pub impact_score: f64,
    /// Risk level based on impact
    pub risk_level: RiskLevel,
}

/// Risk level for change impact
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum RiskLevel {
    /// Low risk (<10% of nodes affected)
    Low,
    /// Medium risk (10-30% of nodes affected)
    Medium,
    /// High risk (30-50% of nodes affected)
    High,
    /// Critical risk (>50% of nodes affected)
    Critical,
}

impl RiskLevel {
    /// Determine risk level from impact score
    pub fn from_impact_score(score: f64) -> Self {
        if score < 0.1 {
            RiskLevel::Low
        } else if score < 0.3 {
            RiskLevel::Medium
        } else if score < 0.5 {
            RiskLevel::High
        } else {
            RiskLevel::Critical
        }
    }
}

/// Suggestion for breaking a circular dependency
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CycleBreakSuggestion {
    /// The edge to remove (source, target)
    pub edge_to_remove: (String, String),
    /// Reason for this suggestion
    pub reason: String,
    /// Impact of removing this edge
    pub impact: String,
    /// Alternative approaches
    pub alternatives: Vec<String>,
}

/// Graph comparison result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GraphComparison {
    /// Nodes added in the new graph
    pub added_nodes: Vec<String>,
    /// Nodes removed from the old graph
    pub removed_nodes: Vec<String>,
    /// Edges added in the new graph
    pub added_edges: Vec<(String, String)>,
    /// Edges removed from the old graph
    pub removed_edges: Vec<(String, String)>,
    /// Overall similarity score (0.0-1.0)
    pub similarity_score: f64,
    /// Structural change magnitude
    pub change_magnitude: ChangeMagnitude,
}

/// Magnitude of structural changes
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ChangeMagnitude {
    /// Minimal changes (<5%)
    Minimal,
    /// Minor changes (5-15%)
    Minor,
    /// Moderate changes (15-30%)
    Moderate,
    /// Major changes (30-50%)
    Major,
    /// Extensive changes (>50%)
    Extensive,
}

impl ChangeMagnitude {
    /// Determine change magnitude from similarity score
    pub fn from_similarity_score(score: f64) -> Self {
        if score > 0.95 {
            ChangeMagnitude::Minimal
        } else if score > 0.85 {
            ChangeMagnitude::Minor
        } else if score > 0.70 {
            ChangeMagnitude::Moderate
        } else if score > 0.50 {
            ChangeMagnitude::Major
        } else {
            ChangeMagnitude::Extensive
        }
    }
}

// Visualization module for graph rendering
pub mod visualization;
pub use visualization::{ColorScheme, VisualizationStyle};

#[cfg(test)]
mod tests {
    use super::*;
    use crate::metamodel::{Aspect, Characteristic, CharacteristicKind, Property};
    use std::collections::HashMap;

    fn create_test_aspect() -> Aspect {
        let mut aspect = Aspect::new("urn:samm:test:1.0.0#TestAspect".to_string());

        // Add 3 properties
        for i in 1..=3 {
            let characteristic = Characteristic {
                metadata: crate::metamodel::ElementMetadata::new(format!(
                    "urn:samm:test:1.0.0#Char{}",
                    i
                )),
                data_type: Some("string".to_string()),
                kind: CharacteristicKind::Trait,
                constraints: vec![],
            };

            let property = Property::new(format!("urn:samm:test:1.0.0#Property{}", i))
                .with_characteristic(characteristic);

            aspect.add_property(property);
        }

        aspect
    }

    #[test]
    fn test_graph_construction() {
        let aspect = create_test_aspect();
        let graph = ModelGraph::from_aspect(&aspect).expect("conversion should succeed");

        // 1 aspect + 3 properties + 3 characteristics = 7 nodes
        assert_eq!(graph.num_nodes(), 7);

        // 3 edges (aspect->properties) + 3 edges (properties->characteristics) = 6 edges
        assert_eq!(graph.num_edges(), 6);
    }

    #[test]
    fn test_centrality_computation() {
        let aspect = create_test_aspect();
        let graph = ModelGraph::from_aspect(&aspect).expect("conversion should succeed");

        let centrality = graph.compute_centrality();

        // Should have scores for all nodes
        assert_eq!(centrality.scores.len(), 7);

        // Get top node
        let (top_node, score) = centrality.max_node().expect("operation should succeed");
        // Just check that there is a top node with a positive score
        assert!(!top_node.is_empty());
        assert!(score > 0.0);
    }

    #[test]
    fn test_community_detection() {
        let aspect = create_test_aspect();
        let graph = ModelGraph::from_aspect(&aspect).expect("conversion should succeed");

        let communities = graph
            .detect_communities()
            .expect("detection should succeed");

        // Should have at least 1 community
        assert!(!communities.is_empty());

        // Total members across all communities should equal number of nodes
        let total_members: usize = communities.iter().map(|c| c.members.len()).sum();
        assert_eq!(total_members, graph.num_nodes());
    }

    #[test]
    fn test_cycle_detection() {
        let aspect = create_test_aspect();
        let graph = ModelGraph::from_aspect(&aspect).expect("conversion should succeed");

        let has_cycles = graph.has_cycles().expect("operation should succeed");

        // Simple tree structure should have no cycles
        assert!(!has_cycles);
    }

    #[test]
    fn test_graph_metrics() {
        let aspect = create_test_aspect();
        let graph = ModelGraph::from_aspect(&aspect).expect("conversion should succeed");

        let metrics = graph.compute_metrics().expect("operation should succeed");

        assert_eq!(metrics.num_nodes, 7);
        assert_eq!(metrics.num_edges, 6);
        assert!(metrics.density > 0.0);
        assert!(metrics.density <= 1.0);
    }

    #[test]
    fn test_shortest_path() {
        let aspect = create_test_aspect();
        let graph = ModelGraph::from_aspect(&aspect).expect("conversion should succeed");

        // Path from aspect to property should exist
        let path = graph
            .shortest_path("TestAspect", "Property1")
            .expect("operation should succeed")
            .expect("operation should succeed");

        assert_eq!(path.len(), 2); // Direct edge
        assert_eq!(path[0], "TestAspect");
        assert_eq!(path[1], "Property1");
    }

    #[test]
    fn test_strongly_connected_components() {
        let aspect = create_test_aspect();
        let graph = ModelGraph::from_aspect(&aspect).expect("conversion should succeed");

        let sccs = graph
            .strongly_connected_components()
            .expect("operation should succeed");

        // Tree structure should have each node as its own SCC
        assert_eq!(sccs.len(), 7);
    }

    #[test]
    fn test_impact_analysis() {
        let aspect = create_test_aspect();
        let graph = ModelGraph::from_aspect(&aspect).expect("conversion should succeed");

        // Analyze impact of TestAspect node
        let impact = graph
            .analyze_impact("TestAspect")
            .expect("analysis should succeed");

        assert_eq!(impact.source_node, "TestAspect");
        // Should have 3 direct dependents (the properties)
        assert_eq!(impact.direct_dependents.len(), 3);
        // Should have 6 total dependents (3 properties + 3 characteristics)
        assert_eq!(impact.all_dependents.len(), 6);
        // Impact score should be 6/6 = 1.0 (all other nodes affected)
        assert!((impact.impact_score - 1.0).abs() < 0.01);
        // Should be critical risk
        assert_eq!(impact.risk_level, RiskLevel::Critical);
    }

    #[test]
    fn test_impact_analysis_leaf_node() {
        let aspect = create_test_aspect();
        let graph = ModelGraph::from_aspect(&aspect).expect("conversion should succeed");

        // Analyze impact of a characteristic (leaf node)
        let impact = graph
            .analyze_impact("Char1")
            .expect("analysis should succeed");

        assert_eq!(impact.source_node, "Char1");
        // Leaf node should have no dependents
        assert_eq!(impact.direct_dependents.len(), 0);
        assert_eq!(impact.all_dependents.len(), 0);
        // Impact score should be 0.0
        assert_eq!(impact.impact_score, 0.0);
        // Should be low risk
        assert_eq!(impact.risk_level, RiskLevel::Low);
    }

    #[test]
    fn test_suggest_cycle_breaks_no_cycles() {
        let aspect = create_test_aspect();
        let graph = ModelGraph::from_aspect(&aspect).expect("conversion should succeed");

        let suggestions = graph
            .suggest_cycle_breaks()
            .expect("operation should succeed");

        // No cycles in a tree structure
        assert!(suggestions.is_empty());
    }

    #[test]
    fn test_graph_comparison_identical() {
        let aspect1 = create_test_aspect();
        let aspect2 = create_test_aspect();

        let graph1 = ModelGraph::from_aspect(&aspect1).expect("conversion should succeed");
        let graph2 = ModelGraph::from_aspect(&aspect2).expect("conversion should succeed");

        let comparison = graph1.compare(&graph2).expect("comparison should succeed");

        // Identical graphs
        assert!(comparison.added_nodes.is_empty());
        assert!(comparison.removed_nodes.is_empty());
        assert!(comparison.added_edges.is_empty());
        assert!(comparison.removed_edges.is_empty());
        assert_eq!(comparison.similarity_score, 1.0);
        assert_eq!(comparison.change_magnitude, ChangeMagnitude::Minimal);
    }

    #[test]
    fn test_graph_comparison_with_changes() {
        let aspect1 = create_test_aspect();
        let mut aspect2 = create_test_aspect();

        // Add a new property to aspect2
        let characteristic = Characteristic {
            metadata: crate::metamodel::ElementMetadata::new(
                "urn:samm:test:1.0.0#NewChar".to_string(),
            ),
            data_type: Some("string".to_string()),
            kind: CharacteristicKind::Trait,
            constraints: vec![],
        };

        let property = Property::new("urn:samm:test:1.0.0#NewProperty".to_string())
            .with_characteristic(characteristic);

        aspect2.add_property(property);

        let graph1 = ModelGraph::from_aspect(&aspect1).expect("conversion should succeed");
        let graph2 = ModelGraph::from_aspect(&aspect2).expect("conversion should succeed");

        let comparison = graph1.compare(&graph2).expect("comparison should succeed");

        // Should have 2 added nodes (1 property + 1 characteristic)
        assert_eq!(comparison.added_nodes.len(), 2);
        assert!(comparison.added_nodes.contains(&"NewProperty".to_string()));
        assert!(comparison.added_nodes.contains(&"NewChar".to_string()));

        // Should have 2 added edges
        assert_eq!(comparison.added_edges.len(), 2);

        // Similarity should be less than 1.0
        assert!(comparison.similarity_score < 1.0);
        assert!(comparison.similarity_score > 0.0);
    }

    #[test]
    fn test_risk_level_classification() {
        assert_eq!(RiskLevel::from_impact_score(0.05), RiskLevel::Low);
        assert_eq!(RiskLevel::from_impact_score(0.15), RiskLevel::Medium);
        assert_eq!(RiskLevel::from_impact_score(0.35), RiskLevel::High);
        assert_eq!(RiskLevel::from_impact_score(0.75), RiskLevel::Critical);
    }

    #[test]
    fn test_change_magnitude_classification() {
        assert_eq!(
            ChangeMagnitude::from_similarity_score(0.98),
            ChangeMagnitude::Minimal
        );
        assert_eq!(
            ChangeMagnitude::from_similarity_score(0.90),
            ChangeMagnitude::Minor
        );
        assert_eq!(
            ChangeMagnitude::from_similarity_score(0.75),
            ChangeMagnitude::Moderate
        );
        assert_eq!(
            ChangeMagnitude::from_similarity_score(0.55),
            ChangeMagnitude::Major
        );
        assert_eq!(
            ChangeMagnitude::from_similarity_score(0.30),
            ChangeMagnitude::Extensive
        );
    }
}