In software development, ensuring that applications run smoothly under normal conditions isn’t enough. Systems often face extreme workloads, concurrent processing, and unexpected spikes in demand. This is where stress testing comes into play.

Stress testing helps uncover performance bottlenecks, concurrency issues, and system stability problems that might not be evident under standard usage.

In this blog, we’ll dive deep into what stress testing is, why it’s important, and how it can help identify concurrency issues in multi-threaded applications. We’ll also explore how to fix race conditions using atomic variables in Kotlin and discuss best practices for stress testing.

What Is Stress Testing?

Stress testing is a technique used to evaluate how an application behaves under extreme conditions. This could involve:

• High CPU usage
• Memory exhaustion
• Concurrent execution of multiple threads
• Processing large amounts of data

The goal is to identify points of failure, performance degradation, or unexpected behavior that might not surface under normal conditions.

Key Objectives of Stress Testing

Detect Concurrency Issues (Race Conditions, Deadlocks, Thread Starvation)

• Ensures that shared resources are managed correctly in a multi-threaded environment.

Measure System Stability Under High Load

• Determines if the application remains functional and doesn’t crash or slow down under stress.

Identify Performance Bottlenecks

• Highlights areas where performance can degrade when the system is heavily loaded.

Ensure Correctness in Edge Cases

• Helps expose unpredictable behaviors that don’t appear during regular execution.

Concurrency Issues in Multi-Threaded Applications

Concurrency bugs are notoriously difficult to detect because they often appear only under high load or specific timing conditions. One of the most common issues in concurrent programming is race conditions.

Example: Race Condition in Kotlin

Consider a shared counter variable accessed by multiple threads:

var sharedCount = 0

fun main() {
    val workers = List(1000) {
        Thread { sharedCount++ }
    }
    
    workers.forEach { it.start() }
    workers.forEach { it.join() }
    
    println(sharedCount) // Unpredictable result
}

Why Is This Problematic?

• The sharedCount++ operation is not atomic (it consists of three steps: read, increment, and write).
• Multiple threads may read the same value, increment it, and write back an incorrect value.
• Due to context switching, some increments are lost, leading to an unpredictable final result.

Expected vs. Actual Output

Expected Result (In Ideal Case): 1000

but in most cases, 

Actual Result (Most Cases): Less than 1000 due to lost updates.

Detecting This Issue with a Stress Test

To reliably expose the race condition, increase the number of threads and iterations:

var sharedCount = 0

fun main() {
    val workers = List(10000) {
        Thread {
            repeat(100) { sharedCount++ }
        }
    }
    
    workers.forEach { it.start() }
    workers.forEach { it.join() }
    
    println(sharedCount) // Unpredictable, usually much less than 1,000,000
}

How to Fix This? Using Atomic Variables

To ensure correctness, Kotlin provides AtomicInteger, which guarantees atomicity of operations.

import java.util.concurrent.atomic.AtomicInteger

val sharedCount = AtomicInteger(0)

fun main() {
    val workers = List(10000) {
        Thread {
            repeat(100) { sharedCount.incrementAndGet() }
        }
    }

    workers.forEach { it.start() }
    workers.forEach { it.join() }

    println(sharedCount.get()) // Always 1,000,000
}

Why Does AtomicInteger Work?

• incrementAndGet() is atomic, meaning it ensures that updates occur without interference from other threads.
• No values are lost, and the result is always deterministic and correct.

Other Common Stress Testing Scenarios

Deadlocks

A deadlock occurs when two or more threads are blocked forever, each waiting for the other to release a resource.

Example: Deadlock Scenario

val lock1 = Any()
val lock2 = Any()

fun main() {
    val thread1 = Thread {
        synchronized(lock1) {
            Thread.sleep(100)
            synchronized(lock2) {
                println("Thread 1 acquired both locks")
            }
        }
    }

    val thread2 = Thread {
        synchronized(lock2) {
            Thread.sleep(100)
            synchronized(lock1) {
                println("Thread 2 acquired both locks")
            }
        }
    }

    thread1.start()
    thread2.start()

    thread1.join()
    thread2.join()
}

Result: The program will hang indefinitely because each thread is waiting for the other to release a lock.

Solution: Always acquire locks in a consistent order to prevent circular waiting and potential deadlocks. If possible, use timeouts or lock hierarchies to further minimize the risk.

Best Practices for Stress Testing

Test Under High Load

• Simulate thousands or millions of concurrent operations to uncover hidden issues.

Use Thread-Safe Data Structures

• Kotlin provides AtomicInteger, ConcurrentHashMap, and CopyOnWriteArrayList for safer multi-threading.

Monitor Performance Metrics

• Use profiling tools like VisualVM or Kotlin Coroutines Debugging tools to track CPU, memory, and execution time during stress tests.

Run Tests Repeatedly

• Some concurrency bugs appear only occasionally, so rerun tests multiple times.

Conclusion

Stress testing is a crucial technique for ensuring software stability, performance, and correctness under extreme conditions. It helps identify concurrency issues like race conditions and deadlocks that might not be obvious during normal execution.

By using atomic variables and thread-safe practices, developers can write more reliable multi-threaded applications. If you’re building high-performance or concurrent software, incorporating stress testing in your workflow will save you from unexpected failures and unpredictable behavior in production.