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9 changes: 9 additions & 0 deletions .coderabbit.yaml
Original file line number Diff line number Diff line change
@@ -0,0 +1,9 @@
# Minimal configuration for getting started
language: "zh-CN"

reviews:
profile: "chill"
high_level_summary: true
auto_review:
enabled: true
drafts: false
120 changes: 120 additions & 0 deletions Calculator.java
Original file line number Diff line number Diff line change
@@ -0,0 +1,120 @@
/**
* 计算器类 - 包含数值计算和逻辑错误
*/
public class Calculator {

public static final double PI = 3.14; // 精度不够
private static int lastResult; // 应该用long或BigDecimal

Comment on lines +6 to +8
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🛠️ Refactor suggestion

使用 Math.PI 并修正结果类型的精度/并发语义

PI 精度不够;lastResult 使用 double 且具备可见性。

-    public static final double PI = 3.14; // 精度不够
-    private static int lastResult; // 应该用long或BigDecimal
+    public static final double PI = Math.PI;
+    private static volatile double lastResult;
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
public static final double PI = 3.14; // 精度不够
private static int lastResult; // 应该用long或BigDecimal
public static final double PI = Math.PI;
private static volatile double lastResult;
🤖 Prompt for AI Agents
In Calculator.java around lines 6-8, replace the hard-coded PI with Math.PI to
get full JVM precision, and fix lastResult's type and concurrency semantics: if
you need integer results use a long (e.g., private static volatile long
lastResult or AtomicLong), if you need arbitrary-precision numeric results use
BigDecimal and store it in a thread-safe holder (e.g.,
AtomicReference<BigDecimal> or manage access via synchronized methods); update
any usages accordingly.

// 除零问题
public double divide(double a, double b) {
return a / b; // 没有检查b是否为0
}
Comment on lines +9 to +12
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⚠️ Potential issue

除零检查

避免 NaN/Infinity 或静默错误。

-    public double divide(double a, double b) {
-        return a / b; // 没有检查b是否为0
-    }
+    public double divide(double a, double b) {
+        if (b == 0.0) throw new ArithmeticException("Division by zero");
+        return a / b;
+    }
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
// 除零问题
public double divide(double a, double b) {
return a / b; // 没有检查b是否为0
}
// 除零问题
public double divide(double a, double b) {
if (b == 0.0) throw new ArithmeticException("Division by zero");
return a / b;
}
🤖 Prompt for AI Agents
In Calculator.java around lines 9 to 12, the divide method performs a / b
without checking for b == 0 which can produce NaN/Infinity; add a guard that
checks whether b is zero (use Double.compare(b, 0.0) == 0 or Double.isZero
equivalently) and if so throw an appropriate runtime exception (e.g. new
ArithmeticException("Division by zero") or IllegalArgumentException) otherwise
return a / b; keep behavior deterministic for floating inputs and include a
clear error message.


// 整数除法精度丢失
public double percentage(int value, int total) {
return (value / total) * 100; // 整数除法会丢失精度
}
Comment on lines +14 to +17
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🛠️ Refactor suggestion

百分比整数除法丢失精度且缺少除零保护

-    public double percentage(int value, int total) {
-        return (value / total) * 100; // 整数除法会丢失精度
-    }
+    public double percentage(int value, int total) {
+        if (total == 0) throw new IllegalArgumentException("total cannot be zero");
+        return (value * 100.0) / total;
+    }
📝 Committable suggestion

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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
// 整数除法精度丢失
public double percentage(int value, int total) {
return (value / total) * 100; // 整数除法会丢失精度
}
// 整数除法精度丢失
public double percentage(int value, int total) {
if (total == 0) throw new IllegalArgumentException("total cannot be zero");
return (value * 100.0) / total;
}
🤖 Prompt for AI Agents
In Calculator.java around lines 14 to 17, the percentage method uses integer
division and lacks divide-by-zero protection; change the implementation to check
total == 0 and handle it (e.g., throw IllegalArgumentException or return a
defined value) and perform floating-point division by casting to double or using
100.0 (for example compute (value * 100.0) / total) so the result preserves
fractional precision.


// 数值溢出风险
public int factorial(int n) {
if (n == 0)
return 1;
return n * factorial(n - 1); // 可能栈溢出,int溢出
}
Comment on lines +19 to +24
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⚠️ Potential issue

factorial 递归与溢出风险:改用迭代并溢出检测

使用 Math.multiplyExact 提前抛出溢出异常。

-    public int factorial(int n) {
-        if (n == 0)
-            return 1;
-        return n * factorial(n - 1); // 可能栈溢出,int溢出
-    }
+    public int factorial(int n) {
+        if (n < 0) throw new IllegalArgumentException("n must be >= 0");
+        int result = 1;
+        for (int i = 2; i <= n; i++) {
+            result = Math.multiplyExact(result, i);
+        }
+        return result;
+    }
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
// 数值溢出风险
public int factorial(int n) {
if (n == 0)
return 1;
return n * factorial(n - 1); // 可能栈溢出,int溢出
}
// 数值溢出风险
public int factorial(int n) {
if (n < 0) throw new IllegalArgumentException("n must be >= 0");
int result = 1;
for (int i = 2; i <= n; i++) {
result = Math.multiplyExact(result, i);
}
return result;
}
🤖 Prompt for AI Agents
In Calculator.java around lines 19 to 24, replace the recursive factorial with
an iterative implementation that uses a loop and Math.multiplyExact to detect
integer overflow; compute result in a local int accumulator, on each iteration
call Math.multiplyExact(accumulator, i) to let it throw ArithmeticException on
overflow and propagate or wrap as needed, and remove the recursive call to avoid
stack overflow.


// 浮点数比较问题
public boolean isEqual(double a, double b) {
return a == b; // 不应该直接比较浮点数
}
Comment on lines +26 to +29
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🛠️ Refactor suggestion

浮点比较使用容差

-    public boolean isEqual(double a, double b) {
-        return a == b; // 不应该直接比较浮点数
-    }
+    public boolean isEqual(double a, double b) {
+        final double EPS = 1e-9;
+        return Math.abs(a - b) <= EPS;
+    }
📝 Committable suggestion

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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
// 浮点数比较问题
public boolean isEqual(double a, double b) {
return a == b; // 不应该直接比较浮点数
}
// 浮点数比较问题
public boolean isEqual(double a, double b) {
final double EPS = 1e-9;
return Math.abs(a - b) <= EPS;
}
🤖 Prompt for AI Agents
In Calculator.java around lines 26 to 29, the isEqual method currently compares
doubles with == which is unsafe; replace it with a tolerance-based comparison
such as returning Math.abs(a - b) <= EPSILON where EPSILON is a small constant
(e.g. 1e-9) or accept a caller-provided epsilon parameter; ensure EPSILON is a
private static final double and document its value and rationale.


// 复杂的方法,职责不单一
public String calculateAndFormat(double num1, double num2, String operation) {
double result = 0;
String symbol = "";

// 应该使用switch或策略模式
if (operation.equals("add")) {
result = num1 + num2;
symbol = "+";
} else if (operation.equals("subtract")) {
result = num1 - num2;
symbol = "-";
} else if (operation.equals("multiply")) {
result = num1 * num2;
symbol = "*";
} else if (operation.equals("divide")) {
if (num2 != 0) {
result = num1 / num2;
symbol = "/";
} else {
return "Error: Division by zero";
}
} else {
return "Invalid operation";
}

// 格式化逻辑也在这里
String formatted;
if (result == (int) result) {
formatted = String.valueOf((int) result);
} else {
formatted = String.format("%.2f", result);
}

lastResult = (int) result; // 可能丢失精度

return num1 + " " + symbol + " " + num2 + " = " + formatted;
}

// 无限循环风险
public double sqrt(double number) {
if (number < 0)
return Double.NaN;

double guess = number / 2;
double previous;

do {
previous = guess;
guess = (guess + number / guess) / 2;
} while (Math.abs(guess - previous) > 0.0001); // 可能无限循环

return guess;
}

// 静态方法访问实例变量
public static int getLastResult() {
return lastResult; // 线程安全问题
}

// 缺少输入验证
public double power(double base, int exponent) {
double result = 1;
for (int i = 0; i < exponent; i++) { // 没有处理负指数
result *= base;
}
return result;
}
Comment on lines +91 to +98
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🛠️ Refactor suggestion

power 支持负指数并使用快速幂

提升准确性与性能。

-    public double power(double base, int exponent) {
-        double result = 1;
-        for (int i = 0; i < exponent; i++) { // 没有处理负指数
-            result *= base;
-        }
-        return result;
-    }
+    public double power(double base, int exponent) {
+        if (exponent == 0) return 1.0;
+        boolean neg = exponent < 0;
+        long e = Math.abs((long) exponent);
+        double res = 1.0, b = base;
+        while (e > 0) {
+            if ((e & 1L) == 1L) res *= b;
+            b *= b;
+            e >>= 1;
+        }
+        return neg ? 1.0 / res : res;
+    }
🤖 Prompt for AI Agents
In Calculator.java around lines 91 to 98, the current power method lacks input
validation, doesn't handle negative exponents and uses an O(n) loop; replace it
with a fast exponentiation (exponentiation by squaring) algorithm that runs in
O(log |exponent|), support negative exponents by computing reciprocal when
exponent < 0, and validate inputs by throwing IllegalArgumentException if base
== 0 and exponent < 0 (to avoid division by zero); ensure exponent==0 returns 1
and handle large exponents using a long/abs conversion for the loop variable to
avoid overflow.


// 魔法数字
public double calculateCircleArea(double radius) {
return 3.14159 * radius * radius; // 应该使用常量
}

// 不一致的返回类型处理
public Object calculate(String expression) {
try {
if (expression.contains("+")) {
String[] parts = expression.split("\\+");
return Double.parseDouble(parts[0]) + Double.parseDouble(parts[1]);
} else if (expression.contains("-")) {
String[] parts = expression.split("-");
return Double.parseDouble(parts[0]) - Double.parseDouble(parts[1]);
}
return "Unsupported operation";
} catch (Exception e) {
return null; // 不一致的错误处理
}
}
}
179 changes: 179 additions & 0 deletions ConcurrentTask.java
Original file line number Diff line number Diff line change
@@ -0,0 +1,179 @@
import java.util.*;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;

/**
* 并发任务处理类 - 包含线程安全和并发问题
*/
public class ConcurrentTask {

private static int counter = 0; // 线程不安全的计数器
private List<String> results = new ArrayList<>(); // 非线程安全集合
private Map<String, Object> cache = new HashMap<>(); // 非线程安全缓存

Comment on lines +10 to +13
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⚠️ Potential issue

共享状态非线程安全:使用并发集合/原子类型

  • counter 应使用 AtomicInteger/LongAdder;
  • results 使用线程安全集合;
  • cache 使用 ConcurrentHashMap。
-    private static int counter = 0; // 线程不安全的计数器
-    private List<String> results = new ArrayList<>(); // 非线程安全集合
-    private Map<String, Object> cache = new HashMap<>(); // 非线程安全缓存
+    private static final java.util.concurrent.atomic.AtomicInteger counter = new java.util.concurrent.atomic.AtomicInteger(0);
+    private final List<String> results = java.util.Collections.synchronizedList(new ArrayList<>());
+    private final java.util.concurrent.ConcurrentMap<String, Object> cache = new java.util.concurrent.ConcurrentHashMap<>();
📝 Committable suggestion

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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
private static int counter = 0; // 线程不安全的计数器
private List<String> results = new ArrayList<>(); // 非线程安全集合
private Map<String, Object> cache = new HashMap<>(); // 非线程安全缓存
private static final java.util.concurrent.atomic.AtomicInteger counter = new java.util.concurrent.atomic.AtomicInteger(0);
private final List<String> results = java.util.Collections.synchronizedList(new ArrayList<>());
private final java.util.concurrent.ConcurrentMap<String, Object> cache = new java.util.concurrent.ConcurrentHashMap<>();
🤖 Prompt for AI Agents
In ConcurrentTask.java around lines 10 to 13, the fields use non-thread-safe
shared state: replace the int counter with an AtomicInteger or LongAdder, change
results to a thread-safe collection (e.g., Collections.synchronizedList or a
concurrent implementation like CopyOnWriteArrayList) and replace cache with a
ConcurrentHashMap; update all accesses to use the atomic/ concurrent APIs
(increment via atomic methods or LongAdder.add, iterate/modify collections in a
thread-safe manner) to eliminate race conditions.

// 竞态条件
public void incrementCounter() {
counter++; // 非原子操作
}

public int getCounter() {
return counter;
}
Comment on lines +15 to +21
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🛠️ Refactor suggestion

自增/读取非原子:改为原子操作

-    public void incrementCounter() {
-        counter++; // 非原子操作
-    }
+    public void incrementCounter() {
+        counter.incrementAndGet();
+    }
@@
-    public int getCounter() {
-        return counter;
-    }
+    public int getCounter() {
+        return counter.get();
+    }

Committable suggestion skipped: line range outside the PR's diff.

🤖 Prompt for AI Agents
In ConcurrentTask.java around lines 15 to 21 the counter++ and plain return are
non-atomic and can race; change the field to an AtomicInteger (import
java.util.concurrent.atomic.AtomicInteger), replace counter++ with
counter.incrementAndGet() (or counter.getAndIncrement() if post-value needed),
and return counter.get(); alternatively, if AtomicInteger is not acceptable,
make both methods synchronized to ensure atomic increment and safe read.


// 双重检查锁定的错误实现
private static volatile ConcurrentTask instance;

public static ConcurrentTask getInstance() {
if (instance == null) {
synchronized (ConcurrentTask.class) {
if (instance == null) {
instance = new ConcurrentTask(); // 正确,但有其他问题
}
}
}
return instance;
}

// 死锁风险
private final Object lock1 = new Object();
private final Object lock2 = new Object();

public void method1() {
synchronized (lock1) {
try {
Thread.sleep(100);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
synchronized (lock2) {
// 一些操作
}
}
}

public void method2() {
synchronized (lock2) {
try {
Thread.sleep(100);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
synchronized (lock1) {
// 一些操作
}
}
}
Comment on lines +37 to +65
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⚠️ Potential issue

死锁风险:统一加锁顺序

method1 与 method2 锁顺序相反,容易产生死锁。保持相同顺序获取锁。

-    public void method2() {
-        synchronized (lock2) {
-            try {
-                Thread.sleep(100);
-            } catch (InterruptedException e) {
-                Thread.currentThread().interrupt();
-            }
-            synchronized (lock1) {
-                // 一些操作
-            }
-        }
-    }
+    public void method2() {
+        synchronized (lock1) {
+            try {
+                Thread.sleep(100);
+            } catch (InterruptedException e) {
+                Thread.currentThread().interrupt();
+            }
+            synchronized (lock2) {
+                // 一些操作
+            }
+        }
+    }
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
// 死锁风险
private final Object lock1 = new Object();
private final Object lock2 = new Object();
public void method1() {
synchronized (lock1) {
try {
Thread.sleep(100);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
synchronized (lock2) {
// 一些操作
}
}
}
public void method2() {
synchronized (lock2) {
try {
Thread.sleep(100);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
synchronized (lock1) {
// 一些操作
}
}
}
// 死锁风险
private final Object lock1 = new Object();
private final Object lock2 = new Object();
public void method1() {
synchronized (lock1) {
try {
Thread.sleep(100);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
synchronized (lock2) {
// 一些操作
}
}
}
public void method2() {
synchronized (lock1) {
try {
Thread.sleep(100);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
synchronized (lock2) {
// 一些操作
}
}
}
🤖 Prompt for AI Agents
In ConcurrentTask.java around lines 37 to 65, method1 and method2 acquire locks
in opposite order, creating a deadlock risk; update method2 to acquire locks in
the same order as method1 (always synchronize on lock1 then lock2) or refactor
both methods to use a single canonical locking helper that orders locks
consistently (e.g., always obtain lock1 before lock2) so that both methods
follow identical lock acquisition order.


// 不正确的线程池使用
public void processItems(List<String> items) {
ExecutorService executor = Executors.newFixedThreadPool(10);

for (String item : items) {
executor.submit(() -> {
processItem(item);
results.add("Processed: " + item); // 非线程安全操作
});
}

// 没有shutdown和awaitTermination
}
Comment on lines +67 to +79
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⚠️ Potential issue

线程池未关闭且对非线程安全集合写入

需 shutdown/awaitTermination,并确保 results 线程安全(已在字段修复)。

     public void processItems(List<String> items) {
-        ExecutorService executor = Executors.newFixedThreadPool(10);
+        ExecutorService executor = Executors.newFixedThreadPool(10);
 
         for (String item : items) {
             executor.submit(() -> {
                 processItem(item);
                 results.add("Processed: " + item); // 非线程安全操作
             });
         }
 
-        // 没有shutdown和awaitTermination
+        executor.shutdown();
+        try {
+            if (!executor.awaitTermination(1, java.util.concurrent.TimeUnit.MINUTES)) {
+                executor.shutdownNow();
+            }
+        } catch (InterruptedException e) {
+            executor.shutdownNow();
+            Thread.currentThread().interrupt();
+        }
     }
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
// 不正确的线程池使用
public void processItems(List<String> items) {
ExecutorService executor = Executors.newFixedThreadPool(10);
for (String item : items) {
executor.submit(() -> {
processItem(item);
results.add("Processed: " + item); // 非线程安全操作
});
}
// 没有shutdown和awaitTermination
}
// 不正确的线程池使用
public void processItems(List<String> items) {
ExecutorService executor = Executors.newFixedThreadPool(10);
for (String item : items) {
executor.submit(() -> {
processItem(item);
results.add("Processed: " + item); // 非线程安全操作
});
}
executor.shutdown();
try {
if (!executor.awaitTermination(1, java.util.concurrent.TimeUnit.MINUTES)) {
executor.shutdownNow();
}
} catch (InterruptedException e) {
executor.shutdownNow();
Thread.currentThread().interrupt();
}
}
🤖 Prompt for AI Agents
In ConcurrentTask.java around lines 67 to 79, the method creates a fixed thread
pool and submits tasks that write to the (already fixed) thread-safe results
collection but never shuts down the ExecutorService; wrap task submission in a
try/finally, call executor.shutdown() after submitting, then call
executor.awaitTermination with a reasonable timeout, handle InterruptedException
by calling executor.shutdownNow(), restore the thread interrupt status and log
the interruption, and ensure any timeout/failure to terminate is logged so the
pool is not leaked.


private void processItem(String item) {
// 模拟处理
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
// 不正确的中断处理
}
}

// 不安全的缓存操作
public Object getCachedValue(String key) {
if (!cache.containsKey(key)) {
// 计算值的过程可能很耗时
Object value = computeExpensiveValue(key);
cache.put(key, value); // 竞态条件
return value;
}
return cache.get(key);
}
Comment on lines +90 to +99
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🛠️ Refactor suggestion

缓存 check-then-act 竞态:使用 computeIfAbsent

避免重复计算与并发覆盖。

-    public Object getCachedValue(String key) {
-        if (!cache.containsKey(key)) {
-            // 计算值的过程可能很耗时
-            Object value = computeExpensiveValue(key);
-            cache.put(key, value); // 竞态条件
-            return value;
-        }
-        return cache.get(key);
-    }
+    public Object getCachedValue(String key) {
+        return cache.computeIfAbsent(key, this::computeExpensiveValue);
+    }
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
// 不安全的缓存操作
public Object getCachedValue(String key) {
if (!cache.containsKey(key)) {
// 计算值的过程可能很耗时
Object value = computeExpensiveValue(key);
cache.put(key, value); // 竞态条件
return value;
}
return cache.get(key);
}
// 不安全的缓存操作
public Object getCachedValue(String key) {
return cache.computeIfAbsent(key, this::computeExpensiveValue);
}
🤖 Prompt for AI Agents
In ConcurrentTask.java around lines 90 to 99, the current check-then-act cache
logic causes a race where multiple threads may compute and overwrite the same
value; replace the body to use an atomic cache.computeIfAbsent(key, k ->
computeExpensiveValue(k)) call (ensure cache is a
ConcurrentMap/ConcurrentHashMap) so the expensive computation runs only once per
key and insertion is thread-safe.


private Object computeExpensiveValue(String key) {
try {
Thread.sleep(2000); // 模拟耗时操作
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
return "Value for " + key;
}

// 生产者消费者问题
private Queue<String> queue = new LinkedList<>();
private boolean finished = false;

public void producer() {
for (int i = 0; i < 100; i++) {
queue.offer("Item " + i); // 非线程安全
}
finished = true; // 可见性问题
}
Comment on lines +110 to +119
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⚠️ Potential issue

生产者可见性与容器不安全:改用阻塞队列

建议切换为 BlockingQueue 与“毒丸”结束信号,简单可靠。

示例(字段需调整为 private final java.util.concurrent.BlockingQueue<String> queue = new java.util.concurrent.LinkedBlockingQueue<>(); 且移除 finished 标志):

-    public void producer() {
-        for (int i = 0; i < 100; i++) {
-            queue.offer("Item " + i); // 非线程安全
-        }
-        finished = true; // 可见性问题
-    }
+    public void producer() {
+        for (int i = 0; i < 100; i++) {
+            try { queue.put("Item " + i); }
+            catch (InterruptedException e) { Thread.currentThread().interrupt(); return; }
+        }
+        try { queue.put("__POISON__"); } catch (InterruptedException e) { Thread.currentThread().interrupt(); }
+    }

对应消费者:

-    public void consumer() {
-        while (!finished || !queue.isEmpty()) {
-            String item = queue.poll(); // 可能NPE
-            if (item != null) {
-                System.out.println("Consumed: " + item);
-            }
-        }
-    }
+    public void consumer() {
+        try {
+            while (true) {
+                String item = queue.take();
+                if ("__POISON__".equals(item)) break;
+                System.out.println("Consumed: " + item);
+            }
+        } catch (InterruptedException e) {
+            Thread.currentThread().interrupt();
+        }
+    }

Committable suggestion skipped: line range outside the PR's diff.

🤖 Prompt for AI Agents
In ConcurrentTask.java around lines 110 to 119, the producer uses a
non-thread-safe LinkedList and a boolean finished flag causing visibility and
safety issues; replace the Queue/finished pattern with a thread-safe
BlockingQueue (make it private final) and use a poison-pill sentinel value to
signal completion: have producers put() items into the BlockingQueue (handle
InterruptedException) and after all producers finish insert a single
poison-pill; consumers should use take() in a loop and exit when they receive
the poison-pill; remove the finished field and ensure proper interruption/error
handling around blocking operations.


public void consumer() {
while (!finished || !queue.isEmpty()) {
String item = queue.poll(); // 可能NPE
if (item != null) {
System.out.println("Consumed: " + item);
}
}
}

// 资源竞争
private int sharedResource = 0;

public void updateResource(int value) {
// 非原子操作
sharedResource = sharedResource + value;
}

public int getResource() {
return sharedResource;
}
Comment on lines +130 to +140
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🛠️ Refactor suggestion

共享资源非原子更新

使用 AtomicInteger 或同步。

-    private int sharedResource = 0;
+    private final java.util.concurrent.atomic.AtomicInteger sharedResource = new java.util.concurrent.atomic.AtomicInteger(0);
@@
-    public void updateResource(int value) {
-        // 非原子操作
-        sharedResource = sharedResource + value;
-    }
+    public void updateResource(int value) {
+        sharedResource.addAndGet(value);
+    }
@@
-    public int getResource() {
-        return sharedResource;
-    }
+    public int getResource() {
+        return sharedResource.get();
+    }
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
// 资源竞争
private int sharedResource = 0;
public void updateResource(int value) {
// 非原子操作
sharedResource = sharedResource + value;
}
public int getResource() {
return sharedResource;
}
// 资源竞争
private final java.util.concurrent.atomic.AtomicInteger sharedResource = new java.util.concurrent.atomic.AtomicInteger(0);
public void updateResource(int value) {
sharedResource.addAndGet(value);
}
public int getResource() {
return sharedResource.get();
}
🤖 Prompt for AI Agents
In ConcurrentTask.java around lines 130 to 140 the sharedResource field is a
plain int and updateResource performs a non-atomic read-modify-write, causing
race conditions; change sharedResource to an AtomicInteger and replace the
non-atomic update with an atomic operation (e.g., addAndGet or getAndAdd) or,
alternatively, make updateResource and getResource synchronized to ensure
exclusive access — pick AtomicInteger for lower contention and use its atomic
methods for updates and reads.


// 不正确的同步
public synchronized void synchronizedMethod() {
// 在同步方法中调用其他对象的方法
String result = externalService.process("data");
results.add(result); // 外部调用可能耗时很长
}

Comment on lines +142 to +148
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⚠️ Potential issue

在同步方法内执行外部长耗时调用

缩小锁粒度,避免长时间占有锁。

-    public synchronized void synchronizedMethod() {
-        // 在同步方法中调用其他对象的方法
-        String result = externalService.process("data");
-        results.add(result); // 外部调用可能耗时很长
-    }
+    public void synchronizedMethod() {
+        String result = externalService.process("data"); // 先执行耗时操作
+        synchronized (results) {
+            results.add(result);
+        }
+    }
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
// 不正确的同步
public synchronized void synchronizedMethod() {
// 在同步方法中调用其他对象的方法
String result = externalService.process("data");
results.add(result); // 外部调用可能耗时很长
}
// 不正确的同步
public void synchronizedMethod() {
String result = externalService.process("data"); // 先执行耗时操作
synchronized (results) {
results.add(result);
}
}
🤖 Prompt for AI Agents
In ConcurrentTask.java around lines 142 to 148, the synchronizedMethod holds the
object's monitor while making an external potentially long-running call
(externalService.process) and then modifies shared state (results), which blocks
other threads; refactor so the external call runs outside the synchronized
region: call externalService.process before acquiring the lock, store its return
in a local variable, then enter a short synchronized block (or use a concurrent
collection/Lock) only to add the result to results; ensure null/exception
handling around the external call so the synchronized section is only used for
the minimal shared-state update.

private ExternalService externalService = new ExternalService();

class ExternalService {
public String process(String data) {
try {
Thread.sleep(5000); // 模拟网络调用
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
return "Processed: " + data;
}
}

// ThreadLocal内存泄漏
private static ThreadLocal<StringBuilder> threadLocalBuilder = new ThreadLocal<StringBuilder>() {
@Override
protected StringBuilder initialValue() {
return new StringBuilder();
}
};

public String buildString(String... parts) {
StringBuilder builder = threadLocalBuilder.get();
for (String part : parts) {
builder.append(part);
}
String result = builder.toString();
// 没有清理ThreadLocal
return result;
}
}
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