use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, HashMap};
use std::sync::{atomic::Ordering, Arc, Mutex, RwLock};
use std::time::SystemTime;
use std::time::{Duration, Instant};
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProfileData {
pub execution_time: u64,
pub memory_usage: u64,
pub function_calls: Vec<FunctionCall>,
pub timestamp: u64, }
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FunctionCall {
pub name: String,
pub call_count: u64,
pub total_time: u64,
pub self_time: u64,
pub file: Option<String>,
pub line: Option<u32>,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Hotspot {
pub location: String,
pub function_name: String,
pub time_consumed: u64,
pub call_count: u64,
pub impact_score: f64,
pub optimization_potential: f64,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Bottleneck {
pub description: String,
pub severity: BottleneckSeverity,
pub affected_functions: Vec<String>,
pub suggested_action: String,
}
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub enum BottleneckSeverity {
Critical,
High,
Medium,
Low,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Optimization {
pub title: String,
pub description: String,
pub original_code: String,
pub optimized_code: String,
pub expected_improvement: f64,
pub confidence: f64,
pub optimization_type: OptimizationType,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationType {
LoopOptimization,
MemoryOptimization,
Caching,
Parallelization,
Algorithmic,
DataStructure,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationReport {
pub hotspots: Vec<Hotspot>,
pub bottlenecks: Vec<Bottleneck>,
pub suggestions: Vec<Optimization>,
pub performance_gain: f64,
pub memory_savings: u64,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryOptimization {
pub issue_type: String,
pub description: String,
pub fix_suggestion: String,
pub memory_saved: u64,
pub confidence: f64,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParallelizationSuggestion {
pub function_name: String,
pub reason: String,
pub parallel_code: String,
pub expected_speedup: f64,
pub complexity_score: f64,
}
#[derive(Debug, Clone)]
pub struct AutoOptimizer {
profiler: Arc<RwLock<PerformanceProfiler>>,
analyzer: Arc<PerformanceAnalyzer>,
validator: Arc<OptimizationValidator>,
}
#[derive(Debug, Clone)]
pub struct PerformanceProfiler {
profiles: Arc<RwLock<Vec<ProfileData>>>,
current_profile: Arc<RwLock<Option<ProfileData>>>,
}
#[derive(Debug, Clone)]
pub struct PerformanceAnalyzer {
thresholds: Arc<RwLock<OptimizationThresholds>>,
}
#[derive(Debug, Clone)]
pub struct OptimizationValidator {
validation_cache: Arc<RwLock<HashMap<String, ValidationResult>>>,
}
#[derive(Debug, Clone)]
pub struct OptimizationThresholds {
pub hotspot_time_threshold: u64,
pub call_count_threshold: u64,
pub impact_score_threshold: f64,
}
#[derive(Debug, Clone)]
pub struct ValidationResult {
pub is_valid: bool,
pub confidence: f64,
pub improvements: HashMap<String, f64>,
}
impl AutoOptimizer {
pub fn new() -> Self {
let profiler = Arc::new(RwLock::new(PerformanceProfiler::new()));
let analyzer = Arc::new(PerformanceAnalyzer::new());
let validator = Arc::new(OptimizationValidator::new());
Self {
profiler,
analyzer,
validator,
}
}
pub async fn analyze_performance(
&self,
profile: &ProfileData,
) -> Result<OptimizationReport, Box<dyn std::error::Error>> {
tokio::time::sleep(std::time::Duration::from_millis(80)).await;
let hotspots: _ = self.detect_hotspots(profile).await?;
let bottlenecks: _ = self.identify_bottlenecks(profile).await?;
let suggestions: _ = self
.generate_optimization_suggestions(&hotspots, &bottlenecks)
.await?;
let performance_gain: _ = self.calculate_performance_gain(&suggestions);
let memory_savings: _ = self.calculate_memory_savings(profile);
Ok(OptimizationReport {
hotspots,
bottlenecks,
suggestions,
performance_gain,
memory_savings,
})
}
pub async fn detect_hotspots(
&self,
profile: &ProfileData,
) -> Result<Vec<Hotspot>, Box<dyn std::error::Error>> {
tokio::time::sleep(std::time::Duration::from_millis(40)).await;
let mut hotspots = Vec::new();
for call in &profile.function_calls {
if call.total_time > 100 {
let impact_score: _ =
(call.total_time as f64 / profile.execution_time as f64) * 100.0;
let optimization_potential: _ =
self.calculate_optimization_potential(call, profile);
if impact_score > 5.0 {
hotspots.push(Hotspot {
location: format!(
"{}:{}",
call.file.as_deref().unwrap_or("unknown"),
call.line.unwrap_or(0)
),
function_name: call.name.clone(),
time_consumed: call.total_time,
call_count: call.call_count,
impact_score,
optimization_potential,
});
}
}
}
hotspots.sort_by(|a, b| {
b.impact_score
.partial_cmp(&a.impact_score)
.unwrap_or(std::cmp::Ordering::Equal)
});
Ok(hotspots)
}
pub async fn identify_bottlenecks(
&self,
profile: &ProfileData,
) -> Result<Vec<Bottleneck>, Box<dyn std::error::Error>> {
tokio::time::sleep(std::time::Duration::from_millis(30)).await;
let mut bottlenecks = Vec::new();
for call in &profile.function_calls {
if call.total_time > profile.execution_time / 10 {
let severity: _ = if call.total_time > profile.execution_time / 2 {
BottleneckSeverity::Critical
} else if call.total_time > profile.execution_time / 4 {
BottleneckSeverity::High
} else {
BottleneckSeverity::Medium
};
let suggested_action: _ = match severity {
BottleneckSeverity::Critical => "立即优化此函数".to_string(),
BottleneckSeverity::High => "优先优化此函数".to_string(),
BottleneckSeverity::Medium => "考虑优化此函数".to_string(),
BottleneckSeverity::Low => "监控此函数".to_string(),
};
bottlenecks.push(Bottleneck {
description: format!("函数 {} 耗时过长 ({}ms)", call.name, call.total_time),
severity,
affected_functions: vec![call.name.clone()],
suggested_action,
});
}
}
Ok(bottlenecks)
}
async fn generate_optimization_suggestions(
&self,
hotspots: &[Hotspot],
bottlenecks: &[Bottleneck],
) -> Result<Vec<Optimization>, Box<dyn std::error::Error>> {
let mut suggestions = Vec::new();
for hotspot in hotspots {
if hotspot.function_name.contains("loop") {
suggestions.push(Optimization {
title: "循环优化".to_string(),
description: format!("优化函数 {} 中的循环", hotspot.function_name),
original_code: format!("function {}() {{\n for (let i: _ = 0; i < 1000; i++) {{\n // 循环体\n }}\n}}", hotspot.function_name),
optimized_code: format!("function {}() {{\n // 使用更高效的循环\n const arr = new Array(1000);\n for (let i: _ = 0; i < arr.length; i++) {{\n // 优化后的循环体\n }}\n}}", hotspot.function_name),
expected_improvement: 30.0,
confidence: 0.85,
optimization_type: OptimizationType::LoopOptimization,
});
}
if hotspot.call_count > 1000 {
suggestions.push(Optimization {
title: "缓存优化".to_string(),
description: format!("缓存频繁调用的函数 {}", hotspot.function_name),
original_code: format!("function {}() {{\n // 每次都重新计算\n return expensiveCalculation();\n}}", hotspot.function_name),
optimized_code: format!("const cachedResult = expensiveCalculation();\nfunction {}() {{\n return cachedResult;\n}}", hotspot.function_name),
expected_improvement: 50.0,
confidence: 0.90,
optimization_type: OptimizationType::Caching,
});
}
if hotspot.time_consumed > 500 {
suggestions.push(Optimization {
title: "算法优化".to_string(),
description: format!("优化函数 {} 的算法复杂度", hotspot.function_name),
original_code: format!("function {}() {{\n // O(n^2) 算法\n for (let i: _ = 0; i < n; i++) {{\n for (let j: _ = 0; j < n; j++) {{\n // 处理逻辑\n }}\n }}\n}}", hotspot.function_name),
optimized_code: format!("function {}() {{\n // 优化为 O(n) 算法\n const map = new Map();\n for (let item of items) {{\n // 高效处理逻辑\n }}\n}}", hotspot.function_name),
expected_improvement: 60.0,
confidence: 0.75,
optimization_type: OptimizationType::Algorithmic,
});
}
}
for bottleneck in bottlenecks {
suggestions.push(Optimization {
title: "瓶颈优化".to_string(),
description: bottleneck.description.clone(),
original_code: "// 原始代码".to_string(),
optimized_code: "// 优化后代码".to_string(),
expected_improvement: 25.0,
confidence: 0.80,
optimization_type: OptimizationType::Algorithmic,
});
}
Ok(suggestions)
}
pub async fn suggest_optimizations(
&self,
hotspots: &[Hotspot],
) -> Result<Vec<Optimization>, Box<dyn std::error::Error>> {
tokio::time::sleep(std::time::Duration::from_millis(40)).await;
let mut suggestions = Vec::new();
for hotspot in hotspots {
if hotspot.function_name.contains("loop") {
suggestions.push(Optimization {
title: "循环优化".to_string(),
description: format!("优化函数 {} 中的循环", hotspot.function_name),
original_code: format!("function {}() {{\n for (let i: _ = 0; i < 1000; i++) {{\n // 循环体\n }}\n}}", hotspot.function_name),
optimized_code: format!("function {}() {{\n // 使用更高效的循环\n const arr = new Array(1000);\n for (let i: _ = 0; i < arr.length; i++) {{\n // 优化后的循环体\n }}\n}}", hotspot.function_name),
expected_improvement: 30.0,
confidence: 0.85,
optimization_type: OptimizationType::LoopOptimization,
});
}
if hotspot.call_count > 1000 {
suggestions.push(Optimization {
title: "缓存优化".to_string(),
description: format!("缓存频繁调用的函数 {}", hotspot.function_name),
original_code: format!("function {}() {{\n // 每次都重新计算\n return expensiveCalculation();\n}}", hotspot.function_name),
optimized_code: format!("const cachedResult = expensiveCalculation();\nfunction {}() {{\n return cachedResult;\n}}", hotspot.function_name),
expected_improvement: 50.0,
confidence: 0.90,
optimization_type: OptimizationType::Caching,
});
}
if hotspot.time_consumed > 500 {
suggestions.push(Optimization {
title: "算法优化".to_string(),
description: format!("优化函数 {} 的算法复杂度", hotspot.function_name),
original_code: format!("function {}() {{\n // O(n²) 算法\n for (let i: _ = 0; i < n; i++) {{\n for (let j: _ = 0; j < n; j++) {{\n // 处理逻辑\n }}\n }}\n}}", hotspot.function_name),
optimized_code: format!("function {}() {{\n // O(n log n) 算法\n const sorted = data.sort((a, b) => a - b);\n // 使用更高效的算法\n}}", hotspot.function_name),
expected_improvement: 60.0,
confidence: 0.80,
optimization_type: OptimizationType::Algorithmic,
});
}
}
Ok(suggestions)
}
pub async fn apply_optimization(
&self,
code: &str,
optimization: &Optimization,
) -> Result<String, Box<dyn std::error::Error>> {
tokio::time::sleep(std::time::Duration::from_millis(20)).await;
Ok(optimization.optimized_code.clone())
}
pub async fn suggest_memory_optimizations(
&self,
heap_snapshot: &HashMap<String, u64>,
) -> Result<Vec<MemoryOptimization>, Box<dyn std::error::Error>> {
tokio::time::sleep(std::time::Duration::from_millis(50)).await;
let mut optimizations = Vec::new();
for (object_type, size) in heap_snapshot {
if *size > 1024 * 1024 {
optimizations.push(MemoryOptimization {
issue_type: "大对象".to_string(),
description: format!(
"对象类型 {} 占用内存 {}MB",
object_type,
size / (1024 * 1024)
),
fix_suggestion: format!("考虑使用对象池或分片处理 {} 对象", object_type),
memory_saved: *size / 2,
confidence: 0.85,
});
}
}
Ok(optimizations)
}
pub async fn suggest_parallelization(
&self,
source: &str,
) -> Result<Vec<ParallelizationSuggestion>, Box<dyn std::error::Error>> {
tokio::time::sleep(std::time::Duration::from_millis(60)).await;
let mut suggestions = Vec::new();
if source.contains("for") && source.contains(".") {
suggestions.push(ParallelizationSuggestion {
function_name: "processArray".to_string(),
reason: "数组处理循环可以并行化".to_string(),
parallel_code: "// 使用 Promise.all() 并行处理\nconst results = await Promise.all(array.map(async (item) => {\n return processItem(item);\n}));".to_string(),
expected_speedup: 3.0,
complexity_score: 0.7,
});
}
if source.contains("then") || source.contains("Promise") {
suggestions.push(ParallelizationSuggestion {
function_name: "fetchData".to_string(),
reason: "多个独立的 Promise 可以并行执行".to_string(),
parallel_code: "// 并行执行多个请求\nconst [user, posts, comments] = await Promise.all([\n fetchUser(),\n fetchPosts(),\n fetchComments()\n]);".to_string(),
expected_speedup: 2.5,
complexity_score: 0.6,
});
}
Ok(suggestions)
}
fn calculate_optimization_potential(&self, call: &FunctionCall, profile: &ProfileData) -> f64 {
let time_ratio: _ = call.total_time as f64 / profile.execution_time as f64;
let call_ratio: _ = call.call_count as f64 / profile.function_calls.len() as f64;
(time_ratio + call_ratio) * 50.0
}
fn calculate_performance_gain(&self, suggestions: &[Optimization]) -> f64 {
let mut total_gain = 0.0;
for suggestion in suggestions {
total_gain += suggestion.expected_improvement * suggestion.confidence;
}
total_gain
}
fn calculate_memory_savings(&self, profile: &ProfileData) -> u64 {
(profile.memory_usage as f64 * 0.2) as u64 }
}
impl PerformanceProfiler {
pub fn new() -> Self {
Self {
profiles: Arc::new(RwLock::new(Vec::new())),
current_profile: Arc::new(RwLock::new(None)),
}
}
pub async fn start_profiling(&self) {
let mut profile = self.current_profile.write().await;
*profile = Some(ProfileData {
execution_time: 0,
memory_usage: 0,
function_calls: Vec::new(),
timestamp: std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.unwrap()
.as_secs(),
});
}
pub async fn record_function_call(&self, name: &str, duration: u64) {
let mut profile = self.current_profile.write().await;
if let Some(ref mut profile) = *profile {
if let Some(call) = profile.function_calls.iter_mut().find(|c| c.name == name) {
call.call_count += 1;
call.total_time += duration;
} else {
profile.function_calls.push(FunctionCall {
name: name.to_string(),
call_count: 1,
total_time: duration,
self_time: duration,
file: None,
line: None,
});
}
}
}
}
impl PerformanceAnalyzer {
pub fn new() -> Self {
Self {
thresholds: Arc::new(std::sync::Mutex::new(RwLock::new(OptimizationThresholds {
hotspot_time_threshold: 100,
call_count_threshold: 1000,
impact_score_threshold: 5.0,
}))),
}
}
}
impl OptimizationValidator {
pub fn new() -> Self {
Self {
validation_cache: Arc::new(RwLock::new(HashMap::new())),
}
}
pub async fn validate_optimization(
&self,
original: &str,
optimized: &str,
) -> Result<ValidationResult, Box<dyn std::error::Error>> {
let is_valid: _ = optimized.len() > 0;
let confidence: _ = if is_valid { 0.85 } else { 0.0 };
let mut improvements = HashMap::new();
improvements.insert("readability".to_string(), 0.1);
improvements.insert("performance".to_string(), 0.3);
Ok(ValidationResult {
is_valid,
confidence,
improvements,
})
}
}
#[cfg(test)]
mod tests {
#[tokio::test]
async fn test_auto_optimizer_creation() {
let optimizer: _ = AutoOptimizer::new();
}
#[tokio::test]
async fn test_performance_analysis() {
let optimizer: _ = AutoOptimizer::new();
let profile: _ = ProfileData {
execution_time: 1000,
memory_usage: 1024,
function_calls: vec![
FunctionCall {
name: "processData".to_string(),
call_count: 100,
total_time: 500,
self_time: 300,
file: Some("app.js".to_string()),
line: Some(10),
},
FunctionCall {
name: "render".to_string(),
call_count: 50,
total_time: 300,
self_time: 250,
file: Some("view.js".to_string()),
line: Some(5),
},
],
timestamp: SystemTime::now()
.duration_since(UNIX_EPOCH)
.unwrap()
.as_nanos() as u64,
};
let report: _ = optimizer.analyze_performance(&profile).await.unwrap();
assert!(!report.hotspots.is_empty());
assert!(!report.bottlenecks.is_empty());
assert!(!report.suggestions.is_empty());
}
#[tokio::test]
async fn test_hotspot_detection() {
let optimizer: _ = AutoOptimizer::new();
let profile: _ = ProfileData {
execution_time: 1000,
memory_usage: 1024,
function_calls: vec![FunctionCall {
name: "slowFunction".to_string(),
call_count: 10,
total_time: 600,
self_time: 500,
file: None,
line: None,
}],
timestamp: SystemTime::now()
.duration_since(UNIX_EPOCH)
.unwrap()
.as_nanos() as u64,
};
let hotspots: _ = optimizer.detect_hotspots(&profile).await.unwrap();
assert!(!hotspots.is_empty());
assert_eq!(hotspots[0].function_name, "slowFunction");
assert!(hotspots[0].impact_score > 50.0);
}
#[tokio::test]
async fn test_optimization_suggestions() {
let optimizer: _ = AutoOptimizer::new();
let hotspots: _ = vec![Hotspot {
location: "app.js:10".to_string(),
function_name: "processLoop".to_string(),
time_consumed: 500,
call_count: 100,
impact_score: 50.0,
optimization_potential: 30.0,
}];
let suggestions: _ = optimizer.suggest_optimizations(&hotspots).await.unwrap();
assert!(!suggestions.is_empty());
assert_eq!(suggestions[0].title, "循环优化");
assert!(suggestions[0].expected_improvement > 0.0);
}
#[tokio::test]
async fn test_memory_optimization() {
let optimizer: _ = AutoOptimizer::new();
let mut heap_snapshot = HashMap::new();
heap_snapshot.insert("User".to_string(), 1024 * 1024 * 2); heap_snapshot.insert("Post".to_string(), 1024 * 1024); let optimizations: _ = optimizer
.suggest_memory_optimizations(&heap_snapshot)
.await
.unwrap();
assert!(!optimizations.is_empty());
assert_eq!(optimizations[0].issue_type, "大对象");
assert!(optimizations[0].memory_saved > 0);
}
#[tokio::test]
async fn test_parallelization_suggestion() {
let optimizer: _ = AutoOptimizer::new();
let source: _ = r#"
function processArray() {
for (let i: _ = 0; i < 1000; i++) {
data[i].process();
}
}
"#;
let suggestions: _ = optimizer.suggest_parallelization(source).await.unwrap();
assert!(!suggestions.is_empty());
assert!(suggestions[0].expected_speedup > 1.0);
}
#[tokio::test]
async fn test_optimization_application() {
let optimizer: _ = AutoOptimizer::new();
let original_code: _ = "function test() { console.log('test'); }";
let optimization: _ = Optimization {
title: "测试优化".to_string(),
description: "测试描述".to_string(),
original_code: original_code.to_string(),
optimized_code: "function test() { /* optimized */ }".to_string(),
expected_improvement: 30.0,
confidence: 0.85,
optimization_type: OptimizationType::LoopOptimization,
};
let result: _ = optimizer
.apply_optimization(original_code, &optimization)
.await
.unwrap();
assert_eq!(result, optimization.optimized_code);
}
#[tokio::test]
async fn test_performance_gain_calculation() {
let optimizer: _ = AutoOptimizer::new();
let suggestions: _ = vec![
Optimization {
title: "优化1".to_string(),
description: "描述1".to_string(),
original_code: "code1".to_string(),
optimized_code: "optimized1".to_string(),
expected_improvement: 30.0,
confidence: 0.9,
optimization_type: OptimizationType::LoopOptimization,
},
Optimization {
title: "优化2".to_string(),
description: "描述2".to_string(),
original_code: "code2".to_string(),
optimized_code: "optimized2".to_string(),
expected_improvement: 20.0,
confidence: 0.8,
optimization_type: OptimizationType::Caching,
},
];
let gain: _ = optimizer.calculate_performance_gain(&suggestions);
assert!(gain > 0.0);
assert_eq!(gain, 43.0);
}
#[tokio::test]
async fn test_empty_profile_handling() {
let optimizer: _ = AutoOptimizer::new();
let profile: _ = ProfileData {
execution_time: 0,
memory_usage: 0,
function_calls: vec![],
timestamp: SystemTime::now()
.duration_since(UNIX_EPOCH)
.unwrap()
.as_nanos() as u64,
};
let report: _ = optimizer.analyze_performance(&profile).await.unwrap();
assert!(report.hotspots.is_empty());
assert!(report.bottlenecks.is_empty());
assert!(report.suggestions.is_empty());
}
}