确实很难!在我的(c++)单元测试中，我按照使用的并发模式将其分解为几个类别:

Unit tests for classes that operate in a single thread and aren't thread aware -- easy, test as usual. Unit tests for Monitor objects (those that execute synchronized methods in the callers' thread of control) that expose a synchronized public API -- instantiate multiple mock threads that exercise the API. Construct scenarios that exercise internal conditions of the passive object. Include one longer running test that basically beats the heck out of it from multiple threads for a long period of time. This is unscientific I know but it does build confidence. Unit tests for Active objects (those that encapsulate their own thread or threads of control) -- similar to #2 above with variations depending on the class design. Public API may be blocking or non-blocking, callers may obtain futures, data may arrive at queues or need to be dequeued. There are many combinations possible here; white box away. Still requires multiple mock threads to make calls to the object under test.

题外话:

在我所做的内部开发人员培训中，我教授了并发的支柱和这两种模式，作为思考和分解并发问题的主要框架。显然还有更先进的概念，但我发现这组基础知识可以帮助工程师摆脱困境。正如上面所描述的，它还会导致代码更具单元可测试性。

如果你正在测试简单的new Thread(runnable).run() 您可以模拟Thread来按顺序运行可运行对象

例如，如果被测试对象的代码像这样调用一个新线程

Class TestedClass {
    public void doAsychOp() {
       new Thread(new myRunnable()).start();
    }
}

然后模拟new Threads并按顺序运行runable参数会有所帮助

@Mock
private Thread threadMock;

@Test
public void myTest() throws Exception {
    PowerMockito.mockStatic(Thread.class);
    //when new thread is created execute runnable immediately 
    PowerMockito.whenNew(Thread.class).withAnyArguments().then(new Answer<Thread>() {
        @Override
        public Thread answer(InvocationOnMock invocation) throws Throwable {
            // immediately run the runnable
            Runnable runnable = invocation.getArgumentAt(0, Runnable.class);
            if(runnable != null) {
                runnable.run();
            }
            return threadMock;//return a mock so Thread.start() will do nothing         
        }
    }); 
    TestedClass testcls = new TestedClass()
    testcls.doAsychOp(); //will invoke myRunnable.run in current thread
    //.... check expected 
}

测试线程代码和非常复杂的系统的另一种方法是通过模糊测试。 它不是很好，也不能找到所有的东西，但它可能是有用的，而且操作简单。

引用:

Fuzz testing or fuzzing is a software testing technique that provides random data("fuzz") to the inputs of a program. If the program fails (for example, by crashing, or by failing built-in code assertions), the defects can be noted. The great advantage of fuzz testing is that the test design is extremely simple, and free of preconceptions about system behavior. ... Fuzz testing is often used in large software development projects that employ black box testing. These projects usually have a budget to develop test tools, and fuzz testing is one of the techniques which offers a high benefit to cost ratio. ... However, fuzz testing is not a substitute for exhaustive testing or formal methods: it can only provide a random sample of the system's behavior, and in many cases passing a fuzz test may only demonstrate that a piece of software handles exceptions without crashing, rather than behaving correctly. Thus, fuzz testing can only be regarded as a bug-finding tool rather than an assurance of quality.

确实很难!在我的(c++)单元测试中，我按照使用的并发模式将其分解为几个类别:

Unit tests for classes that operate in a single thread and aren't thread aware -- easy, test as usual. Unit tests for Monitor objects (those that execute synchronized methods in the callers' thread of control) that expose a synchronized public API -- instantiate multiple mock threads that exercise the API. Construct scenarios that exercise internal conditions of the passive object. Include one longer running test that basically beats the heck out of it from multiple threads for a long period of time. This is unscientific I know but it does build confidence. Unit tests for Active objects (those that encapsulate their own thread or threads of control) -- similar to #2 above with variations depending on the class design. Public API may be blocking or non-blocking, callers may obtain futures, data may arrive at queues or need to be dequeued. There are many combinations possible here; white box away. Still requires multiple mock threads to make calls to the object under test.

题外话:

在我所做的内部开发人员培训中，我教授了并发的支柱和这两种模式，作为思考和分解并发问题的主要框架。显然还有更先进的概念，但我发现这组基础知识可以帮助工程师摆脱困境。正如上面所描述的，它还会导致代码更具单元可测试性。

它并不完美，但我用c#写了这个帮助程序:

using System;
using System.Collections.Generic;
using System.Threading;
using System.Threading.Tasks;

namespace Proto.Promises.Tests.Threading
{
    public class ThreadHelper
    {
        public static readonly int multiThreadCount = Environment.ProcessorCount * 100;
        private static readonly int[] offsets = new int[] { 0, 10, 100, 1000 };

        private readonly Stack<Task> _executingTasks = new Stack<Task>(multiThreadCount);
        private readonly Barrier _barrier = new Barrier(1);
        private int _currentParticipants = 0;
        private readonly TimeSpan _timeout;

        public ThreadHelper() : this(TimeSpan.FromSeconds(10)) { } // 10 second timeout should be enough for most cases.

        public ThreadHelper(TimeSpan timeout)
        {
            _timeout = timeout;
        }

        /// <summary>
        /// Execute the action multiple times in parallel threads.
        /// </summary>
        public void ExecuteMultiActionParallel(Action action)
        {
            for (int i = 0; i < multiThreadCount; ++i)
            {
                AddParallelAction(action);
            }
            ExecutePendingParallelActions();
        }

        /// <summary>
        /// Execute the action once in a separate thread.
        /// </summary>
        public void ExecuteSingleAction(Action action)
        {
            AddParallelAction(action);
            ExecutePendingParallelActions();
        }

        /// <summary>
        /// Add an action to be run in parallel.
        /// </summary>
        public void AddParallelAction(Action action)
        {
            var taskSource = new TaskCompletionSource<bool>();
            lock (_executingTasks)
            {
                ++_currentParticipants;
                _barrier.AddParticipant();
                _executingTasks.Push(taskSource.Task);
            }
            new Thread(() =>
            {
                try
                {
                    _barrier.SignalAndWait(); // Try to make actions run in lock-step to increase likelihood of breaking race conditions.
                    action.Invoke();
                    taskSource.SetResult(true);
                }
                catch (Exception e)
                {
                    taskSource.SetException(e);
                }
            }).Start();
        }

        /// <summary>
        /// Runs the pending actions in parallel, attempting to run them in lock-step.
        /// </summary>
        public void ExecutePendingParallelActions()
        {
            Task[] tasks;
            lock (_executingTasks)
            {
                _barrier.SignalAndWait();
                _barrier.RemoveParticipants(_currentParticipants);
                _currentParticipants = 0;
                tasks = _executingTasks.ToArray();
                _executingTasks.Clear();
            }
            try
            {
                if (!Task.WaitAll(tasks, _timeout))
                {
                    throw new TimeoutException($"Action(s) timed out after {_timeout}, there may be a deadlock.");
                }
            }
            catch (AggregateException e)
            {
                // Only throw one exception instead of aggregate to try to avoid overloading the test error output.
                throw e.Flatten().InnerException;
            }
        }

        /// <summary>
        /// Run each action in parallel multiple times with differing offsets for each run.
        /// <para/>The number of runs is 4^actions.Length, so be careful if you don't want the test to run too long.
        /// </summary>
        /// <param name="expandToProcessorCount">If true, copies each action on additional threads up to the processor count. This can help test more without increasing the time it takes to complete.
        /// <para/>Example: 2 actions with 6 processors, runs each action 3 times in parallel.</param>
        /// <param name="setup">The action to run before each parallel run.</param>
        /// <param name="teardown">The action to run after each parallel run.</param>
        /// <param name="actions">The actions to run in parallel.</param>
        public void ExecuteParallelActionsWithOffsets(bool expandToProcessorCount, Action setup, Action teardown, params Action[] actions)
        {
            setup += () => { };
            teardown += () => { };
            int actionCount = actions.Length;
            int expandCount = expandToProcessorCount ? Math.Max(Environment.ProcessorCount / actionCount, 1) : 1;
            foreach (var combo in GenerateCombinations(offsets, actionCount))
            {
                setup.Invoke();
                for (int k = 0; k < expandCount; ++k)
                {
                    for (int i = 0; i < actionCount; ++i)
                    {
                        int offset = combo[i];
                        Action action = actions[i];
                        AddParallelAction(() =>
                        {
                            for (int j = offset; j > 0; --j) { } // Just spin in a loop for the offset.
                            action.Invoke();
                        });
                    }
                }
                ExecutePendingParallelActions();
                teardown.Invoke();
            }
        }

        // Input: [1, 2, 3], 3
        // Ouput: [
        //          [1, 1, 1],
        //          [2, 1, 1],
        //          [3, 1, 1],
        //          [1, 2, 1],
        //          [2, 2, 1],
        //          [3, 2, 1],
        //          [1, 3, 1],
        //          [2, 3, 1],
        //          [3, 3, 1],
        //          [1, 1, 2],
        //          [2, 1, 2],
        //          [3, 1, 2],
        //          [1, 2, 2],
        //          [2, 2, 2],
        //          [3, 2, 2],
        //          [1, 3, 2],
        //          [2, 3, 2],
        //          [3, 3, 2],
        //          [1, 1, 3],
        //          [2, 1, 3],
        //          [3, 1, 3],
        //          [1, 2, 3],
        //          [2, 2, 3],
        //          [3, 2, 3],
        //          [1, 3, 3],
        //          [2, 3, 3],
        //          [3, 3, 3]
        //        ]
        private static IEnumerable<int[]> GenerateCombinations(int[] options, int count)
        {
            int[] indexTracker = new int[count];
            int[] combo = new int[count];
            for (int i = 0; i < count; ++i)
            {
                combo[i] = options[0];
            }
            // Same algorithm as picking a combination lock.
            int rollovers = 0;
            while (rollovers < count)
            {
                yield return combo; // No need to duplicate the array since we're just reading it.
                for (int i = 0; i < count; ++i)
                {
                    int index = ++indexTracker[i];
                    if (index == options.Length)
                    {
                        indexTracker[i] = 0;
                        combo[i] = options[0];
                        if (i == rollovers)
                        {
                            ++rollovers;
                        }
                    }
                    else
                    {
                        combo[i] = options[index];
                        break;
                    }
                }
            }
        }
    }
}

使用示例:

[Test]
public void DeferredMayBeBeResolvedAndPromiseAwaitedConcurrently_void0()
{
    Promise.Deferred deferred = default(Promise.Deferred);
    Promise promise = default(Promise);

    int invokedCount = 0;

    var threadHelper = new ThreadHelper();
    threadHelper.ExecuteParallelActionsWithOffsets(false,
        // Setup
        () =>
        {
            invokedCount = 0;
            deferred = Promise.NewDeferred();
            promise = deferred.Promise;
        },
        // Teardown
        () => Assert.AreEqual(1, invokedCount),
        // Parallel Actions
        () => deferred.Resolve(),
        () => promise.Then(() => { Interlocked.Increment(ref invokedCount); }).Forget()
    );
}

听着，要做到这一点并不容易。我正在做一个本来就是多线程的项目。事件来自操作系统，我必须并发地处理它们。

处理测试复杂的多线程应用程序代码的最简单方法是:如果它太复杂而无法测试，那么您做错了。如果您有一个单独的实例，其中有多个线程作用于它，并且您无法测试这些线程相互踩在一起的情况，那么您的设计需要重做。它既简单又复杂。

有许多方法可以为多线程编程，以避免线程同时通过实例运行。最简单的方法是使所有对象都是不可变的。当然，这通常是不可能的。因此，您必须在设计中确定线程与同一实例交互的地方，并减少这些地方的数量。通过这样做，您可以隔离多线程实际发生的几个类，从而降低测试系统的总体复杂性。

但是您必须意识到，即使这样做，您仍然不能测试两个线程相互践踏的每一种情况。要做到这一点，您必须在同一个测试中并发地运行两个线程，然后准确地控制它们在任何给定时刻执行的行。你能做的就是模拟这种情况。但这可能需要您专门为测试编写代码，这充其量是迈向真正解决方案的半步。

测试代码是否存在线程问题的最好方法可能是对代码进行静态分析。如果您的线程代码没有遵循有限的线程安全模式集，那么您可能会遇到问题。我相信VS中的代码分析确实包含了一些线程的知识，但可能不多。

看，就目前的情况来看(可能还会持续很长一段时间)，测试多线程应用程序的最佳方法是尽可能降低线程代码的复杂性。最小化线程交互的区域，尽可能地进行测试，并使用代码分析来识别危险区域。