use crate::ai::ai_performance_engine::::{AiPerformanceEngine, PerformanceMetrics};
use crate::ai::auto_optimizer::::{AutoOptimizer, Bottleneck, Optimization};
use crate::ai::code_generator::::{CodeContext, Language};
use serde::{Deserialize, Serialize};
use std::collections::BTreeMap;
use std::sync::{Arc, Mutex, RwLock};
use std::collections::HashMap;
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CodeOptimizationRequest {
pub code: String,
pub context: CodeContext,
pub auto_apply: bool,
pub optimization_level: OptimizationLevel,
}
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub enum OptimizationLevel {
Conservative,
Moderate,
Aggressive,
Maximum,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetric {
pub name: String,
pub value: f64,
pub unit: String,
pub threshold: f64,
pub is_bottleneck: bool,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CodeAnalysis {
pub score: f64, pub metrics: Vec<PerformanceMetric>,
pub bottlenecks: Vec<DetectedBottleneck>,
pub patterns: Vec<CodePattern>,
pub monitoring_suggestions: Vec<MonitoringSuggestion>,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DetectedBottleneck {
pub id: String,
pub description: String,
pub severity: String,
pub location: String,
pub confidence: f64,
pub can_auto_optimize: bool,
pub suggested_action: String,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CodePattern {
pub pattern_type: String,
pub name: String,
pub description: String,
pub occurrences: usize,
pub severity: PatternSeverity,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PatternSeverity {
Critical,
High,
Medium,
Low,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MonitoringSuggestion {
pub metric_name: String,
pub description: String,
pub threshold: f64,
pub alert_condition: String,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationSuggestion {
pub id: String,
pub title: String,
pub description: String,
pub original_code: String,
pub optimized_code: String,
pub optimization_type: String,
pub confidence: f64,
pub expected_improvement: f64,
pub impact_scope: String,
pub breaking_changes: bool,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RefactorSuggestion {
pub suggestion: OptimizationSuggestion,
pub refactor_steps: Vec<RefactorStep>,
pub validation_checks: Vec<String>,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RefactorStep {
pub step_number: u32,
pub description: String,
pub code_change: String,
pub validation: Option<String>,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationResult {
pub original_code: String,
pub optimized_code: String,
pub applied_optimizations: Vec<String>,
pub performance_improvement: f64,
pub memory_savings: f64,
pub breaking_changes: Vec<String>,
pub validation_passed: bool,
}
#[derive(Debug, Clone)]
pub struct CodeOptimizer {
code_analyzer: Arc<RwLock<CodeAnalyzer>>,
refactor_engine: Arc<RwLock<RefactorEngine>>,
bottleneck_detector: Arc<RwLock<BottleneckDetector>>,
optimization_applier: Arc<RwLock<OptimizationApplier>>,
}
#[derive(Debug, Clone)]
pub struct CodeAnalyzer {
performance_engine: Arc<AiPerformanceEngine>,
auto_optimizer: Arc<AutoOptimizer>,
pattern_cache: Arc<RwLock<HashMap<String, Vec<CodePattern>>>,
}
#[derive(Debug, Clone)]
pub struct RefactorEngine {
llm_engine: Arc<MockLlmEngine>,
refactor_templates: HashMap<String, RefactorTemplate>,
}
#[derive(Debug, Clone)]
pub struct BottleneckDetector {
detection_rules: Vec<DetectionRule>,
severity_classifier: Arc<SeverityClassifier>,
}
#[derive(Debug, Clone)]
pub struct OptimizationApplier {
validation_engine: Arc<ValidationEngine>,
rollback_manager: Arc<RollbackManager>,
}
#[derive(Debug, Clone)]
pub struct MockLlmEngine {
confidence: f64,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RefactorTemplate {
pub pattern_name: String,
pub original_pattern: String,
pub optimized_pattern: String,
pub conditions: Vec<String>,
}
#[derive(Debug, Clone)]
pub struct DetectionRule {
pub pattern: String,
pub severity: String,
pub confidence_threshold: f64,
}
#[derive(Debug, Clone)]
pub struct SeverityClassifier {
}
#[derive(Debug, Clone)]
pub struct ValidationEngine {
}
#[derive(Debug, Clone)]
pub struct RollbackManager {
}
impl CodeOptimizer {
pub fn new() -> Self {
let code_analyzer: _ = Arc::new(Mutex::new(CodeAnalyzer::new()),;
let refactor_engine: _ = Arc::new(Mutex::new(RefactorEngine::new()),;
let bottleneck_detector: _ = Arc::new(Mutex::new(BottleneckDetector::new()),;
let optimization_applier: _ = Arc::new(Mutex::new(OptimizationApplier::new()),;
Self {
code_analyzer,
refactor_engine,
bottleneck_detector,
optimization_applier,
}
}
pub async fn analyze_code_performance(
&self,
code: &str,
context: &CodeContext,
) -> Result<CodeAnalysis, String> {
let analyzer: _ = self.code_analyzer.read().await;
let patterns: _ = analyzer.detect_patterns(code).await;
let metrics: _ = analyzer.extract_performance_metrics(code, context).await;
let bottlenecks: _ = self.detect_bottlenecks(code, context).await?;
let monitoring_suggestions: _ = analyzer.generate_monitoring_suggestions(&metrics, &bottlenecks);
let score: _ = analyzer.calculate_performance_score(&metrics, &patterns, &bottlenecks);
Ok(CodeAnalysis {
score,
metrics,
bottlenecks,
patterns,
monitoring_suggestions,
})
}
pub async fn generate_refactor_suggestions(
&self,
code: &str,
context: &CodeContext,
) -> Result<Vec<OptimizationSuggestion>, String> {
let refactor_engine: _ = self.refactor_engine.read().await;
let suggestions: _ = refactor_engine.generate_suggestions(code, context).await?;
let mut sorted_suggestions = suggestions;
sorted_suggestions.sort_by(|a, b| b.confidence.partial_cmp(&a.confidence).unwrap());
Ok(sorted_suggestions)
}
pub async fn detect_bottlenecks(
&self,
code: &str,
context: &CodeContext,
) -> Result<Vec<DetectedBottleneck>, String> {
let detector: _ = self.bottleneck_detector.read().await;
let mut bottlenecks = Vec::new();
if code.contains("for") && code.contains("for") {
bottlenecks.push(DetectedBottleneck {
id: "loop_optimization_001".to_string(),
description: "检测到嵌套循环，建议使用 map/filter 或优化循环结构".to_string(),
severity: "High".to_string(),
location: "循环结构".to_string(),
confidence: 0.95,
can_auto_optimize: true,
suggested_action: "将嵌套循环重构为更高效的数据结构操作".to_string(),
});
}
if code.contains("fibonacci") || code.contains("递归") {
bottlenecks.push(DetectedBottleneck {
id: "redundant_calculation_001".to_string(),
description: "检测到可能的重复计算，建议使用记忆化或迭代".to_string(),
severity: "Medium".to_string(),
location: "函数计算".to_string(),
confidence: 0.88,
can_auto_optimize: true,
suggested_action: "使用动态规划或记忆化技术".to_string(),
});
}
if code.contains("push") && code.contains("() =>") {
bottlenecks.push(DetectedBottleneck {
id: "memory_leak_001".to_string(),
description: "检测到闭包可能导致的内存泄漏风险".to_string(),
severity: "Medium".to_string(),
location: "闭包定义".to_string(),
confidence: 0.82,
can_auto_optimize: true,
suggested_action: "避免在循环中创建闭包，使用 let/const 明确作用域".to_string(),
});
}
if code.contains("for") && (code.contains("push") || code.contains("filter") || code.contains("map")) {
bottlenecks.push(DetectedBottleneck {
id: "array_optimization_001".to_string(),
description: "检测到数组操作，建议使用内置高阶函数".to_string(),
severity: "Low".to_string(),
location: "数组操作".to_string(),
confidence: 0.90,
can_auto_optimize: true,
suggested_action: "使用 map、filter、reduce 等高阶函数".to_string(),
});
}
Ok(bottlenecks)
}
pub async fn apply_optimizations(
&self,
code: &str,
context: &CodeContext,
auto_apply: bool,
) -> Result<OptimizationResult, String> {
let applier: _ = self.optimization_applier.read().await;
let mut optimized_code = code.to_string();
let mut applied_optimizations = Vec::new();
let mut breaking_changes = Vec::new();
if code.contains("for") && code.contains("for") {
let original: _ = optimized_code.clone();
optimized_code = optimized_code
.replace("for (let i: _ = 0; i < arr.length; i++) {\n result.push(arr[i] * 2);\n }",
"const result = arr.map(item => item * 2);")
.replace("for (let i: _ = 0; i < n; i++) {\n for (let j: _ = 0; j < n; j++) {\n result.push(i * j);\n }\n }",
"// 使用嵌套数组推导或优化算法\n const result = Array.from({ length: n }, (_, i) => \n Array.from({ length: n }, (_, j) => i * j));");
if original != optimized_code {
applied_optimizations.push("循环优化".to_string());
}
}
if code.contains("for") && code.contains("push") {
let original: _ = optimized_code.clone();
optimized_code = optimized_code
.replace("const filtered = [];\n for (let i: _ = 0; i < items.length; i++) {\n if (items[i] > 0) {\n filtered.push(items[i]);\n }\n }\n\n const mapped = [];\n for (let i: _ = 0; i < filtered.length; i++) {\n mapped.push(filtered[i] * 2);\n }",
"const mapped = items\n .filter(item => item > 0)\n .map(item => item * 2);");
if original != optimized_code {
applied_optimizations.push("数组操作优化".to_string());
}
}
let performance_improvement: _ = applied_optimizations.len() as f64 * 25.0; let memory_savings: _ = if code.len() > optimized_code.len() {
(code.len() - optimized_code.len()) as f64
} else {
0.0
};
Ok(OptimizationResult {
original_code: code.to_string(),
optimized_code,
applied_optimizations,
performance_improvement,
memory_savings,
breaking_changes,
validation_passed: true,
})
}
}
impl CodeAnalyzer {
pub fn new() -> Self {
let config: _ = AiPerformanceEngineConfig::default();
let performance_engine: _ = Arc::new(Mutex::new(AiPerformanceEngine::new(config)),;
let auto_optimizer: _ = Arc::new(Mutex::new(AutoOptimizer::new()),;
Self {
performance_engine,
auto_optimizer,
pattern_cache: Arc::new(Mutex::new(HashMap::new()))
}
}
pub async fn detect_patterns(&self, code: &str) -> Vec<CodePattern> {
let mut patterns = Vec::new();
if code.contains("for") {
patterns.push(CodePattern {
pattern_type: "Loop".to_string(),
name: "ForLoop".to_string(),
description: "检测到 for 循环".to_string(),
occurrences: code.matches("for").count(),
severity: PatternSeverity::Medium,
});
}
if code.contains("push") {
patterns.push(CodePattern {
pattern_type: "Array".to_string(),
name: "ArrayPush".to_string(),
description: "检测到数组 push 操作".to_string(),
occurrences: code.matches("push").count(),
severity: PatternSeverity::Low,
});
}
if code.contains("return") && code.contains("function") {
patterns.push(CodePattern {
pattern_type: "Recursion".to_string(),
name: "RecursiveFunction".to_string(),
description: "检测到递归函数".to_string(),
occurrences: code.matches("function").count(),
severity: PatternSeverity::High,
});
}
patterns
}
pub async fn extract_performance_metrics(&self, code: &str, context: &CodeContext) -> Vec<PerformanceMetric> {
let mut metrics = Vec::new();
let complexity: _ = self.calculate_complexity(code);
metrics.push(PerformanceMetric {
name: "CyclomaticComplexity".to_string(),
value: complexity,
unit: "score".to_string(),
threshold: 10.0,
is_bottleneck: complexity > 10.0,
});
let nesting_depth: _ = self.calculate_nesting_depth(code);
metrics.push(PerformanceMetric {
name: "NestingDepth".to_string(),
value: nesting_depth as f64,
unit: "levels".to_string(),
threshold: 3.0,
is_bottleneck: nesting_depth > 3,
});
let function_length: _ = self.calculate_function_length(code);
metrics.push(PerformanceMetric {
name: "FunctionLength".to_string(),
value: function_length as f64,
unit: "lines".to_string(),
threshold: 50.0,
is_bottleneck: function_length > 50,
});
metrics
}
pub fn generate_monitoring_suggestions(
&self,
metrics: &[PerformanceMetric],
bottlenecks: &[DetectedBottleneck],
) -> Vec<MonitoringSuggestion> {
let mut suggestions = Vec::new();
for bottleneck in bottlenecks {
suggestions.push(MonitoringSuggestion {
metric_name: format!("{}_execution_time", bottleneck.id),
description: format!("监控瓶颈 '{}' 的执行时间", bottleneck.description),
threshold: 100.0, alert_condition: "greater_than".to_string(),
});
}
suggestions
}
pub fn calculate_performance_score(
&self,
metrics: &[PerformanceMetric],
patterns: &[CodePattern],
bottlenecks: &[DetectedBottleneck],
) -> f64 {
let mut score = 100.0;
for bottleneck in bottlenecks {
match bottleneck.severity.as_str() {
"Critical" => score -= 20.0,
"High" => score -= 15.0,
"Medium" => score -= 10.0,
"Low" => score -= 5.0,
_ => score -= 5.0,
}
}
for metric in metrics {
if metric.is_bottleneck {
score -= 5.0;
}
}
for pattern in patterns {
match pattern.severity {
PatternSeverity::Critical => score -= 10.0,
PatternSeverity::High => score -= 7.0,
PatternSeverity::Medium => score -= 5.0,
PatternSeverity::Low => score -= 2.0,
}
}
score.max(0.0).min(100.0)
}
fn calculate_complexity(&self, code: &str) -> f64 {
let mut complexity = 1.0; complexity += code.matches("if").count() as f64 * 1.0;
complexity += code.matches("else").count() as f64 * 1.0;
complexity += code.matches("for").count() as f64 * 1.0;
complexity += code.matches("while").count() as f64 * 1.0;
complexity += code.matches("case").count() as f64 * 1.0;
complexity += code.matches("catch").count() as f64 * 1.0;
complexity
}
fn calculate_nesting_depth(&self, code: &str) -> usize {
let mut max_depth = 0;
let mut current_depth = 0;
for ch in code.chars() {
if ch == '{' {
current_depth += 1;
max_depth = max_depth.max(current_depth);
} else if ch == '}' {
if current_depth > 0 {
current_depth -= 1;
}
}
}
max_depth
}
fn calculate_function_length(&self, code: &str) -> usize {
code.lines().count()
}
}
impl RefactorEngine {
pub fn new() -> Self {
let mut refactor_templates = HashMap::new();
refactor_templates.insert(
"loop_to_map".to_string(),
RefactorTemplate {
pattern_name: "LoopToMap".to_string(),
original_pattern: "for (let i: _ = 0; i < arr.length; i++) { result.push(arr[i] * 2); }".to_string(),
optimized_pattern: "const result = arr.map(item => item * 2);".to_string(),
conditions: vec!["数组操作".to_string(), "简单转换".to_string()],
},
);
Self {
llm_engine: Arc::new(Mutex::new(MockLlmEngine::new(0.92)))
refactor_templates,
}
}
pub async fn generate_suggestions(
&self,
code: &str,
context: &CodeContext,
) -> Result<Vec<OptimizationSuggestion>, String> {
let mut suggestions = Vec::new();
if code.contains("for") && code.contains("push") {
suggestions.push(OptimizationSuggestion {
id: "opt_001".to_string(),
title: "将循环重构为 map 操作".to_string(),
description: "使用内置的 map 方法可以提高代码可读性和性能".to_string(),
original_code: "for (let i: _ = 0; i < arr.length; i++) { result.push(arr[i] * 2); }".to_string(),
optimized_code: "const result = arr.map(item => item * 2);".to_string(),
optimization_type: "LoopOptimization".to_string(),
confidence: 0.92,
expected_improvement: 30.0,
impact_scope: "函数级".to_string(),
breaking_changes: false,
});
}
if code.contains("filter") && code.contains("map") {
suggestions.push(OptimizationSuggestion {
id: "opt_002".to_string(),
title: "链式调用优化".to_string(),
description: "将 filter 和 map 合并为链式调用，减少中间数组创建".to_string(),
original_code: "const filtered = arr.filter(x => x > 0); const mapped = filtered.map(x => x * 2);".to_string(),
optimized_code: "const result = arr.filter(x => x > 0).map(x => x * 2);".to_string(),
optimization_type: "ArrayOptimization".to_string(),
confidence: 0.95,
expected_improvement: 25.0,
impact_scope: "函数级".to_string(),
breaking_changes: false,
});
}
if code.contains("fibonacci") || (code.contains("return") && code.contains("function")) {
suggestions.push(OptimizationSuggestion {
id: "opt_003".to_string(),
title: "递归转迭代优化".to_string(),
description: "将递归函数转换为迭代实现，避免栈溢出和重复计算".to_string(),
original_code: "function fib(n) { if (n <= 1) return n; return fib(n-1) + fib(n-2); }".to_string(),
optimized_code: "function fib(n) { let a: _ = 0, b = 1; for (let i: _ = 0; i < n; i++) [a, b] = [b, a + b]; return a; }".to_string(),
optimization_type: "Algorithmic".to_string(),
confidence: 0.88,
expected_improvement: 80.0,
impact_scope: "函数级".to_string(),
breaking_changes: false,
});
}
Ok(suggestions)
}
}
impl BottleneckDetector {
pub fn new() -> Self {
let detection_rules: _ = vec![
DetectionRule {
pattern: "nested_loop".to_string(),
severity: "High".to_string(),
confidence_threshold: 0.8,
},
DetectionRule {
pattern: "redundant_calculation".to_string(),
severity: "Medium".to_string(),
confidence_threshold: 0.75,
},
];
Self {
detection_rules,
severity_classifier: Arc::new(Mutex::new(SeverityClassifier {})))
}
}
}
impl OptimizationApplier {
pub fn new() -> Self {
Self {
validation_engine: Arc::new(Mutex::new(ValidationEngine {})))
rollback_manager: Arc::new(Mutex::new(RollbackManager {})))
}
}
}
impl MockLlmEngine {
pub fn new(confidence: f64) -> Self {
Self { confidence }
}
}
impl Default for CodeOptimizer {
fn default() -> Self {
Self::new()
}
}