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。

一个简单的测试模式可以用于一些(不是所有!)用例，就是多次重复相同的测试。例如，假设你有一个方法:

def process(input):
    # Spawns several threads to do the job
    # ...
    return output

创建一堆测试:

process(input1) -> expect to return output1
process(input2) -> expect to return output2
...

现在将每个测试运行多次。

如果流程的实现包含一个微小的错误(例如死锁、竞态条件等)，出现的概率为0.1%，那么运行1000次测试，则该错误至少出现一次的概率为64%。运行测试10000次，得到>99%的概率。

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()
    );
}

Pete Goodliffe有一个关于线程代码单元测试的系列。

是很困难的。我采用了更简单的方法，尽量将线程代码从实际测试中抽象出来。皮特确实提到了我分手的方式是错误的但我要么是正确的，要么就是我很幸运。

等待在帮助您编写确定性单元测试时也很有用。它允许您等待系统中的某个状态更新。例如:

await().untilCall( to(myService).myMethod(), greaterThan(3) );

or

await().atMost(5,SECONDS).until(fieldIn(myObject).ofType(int.class), equalTo(1));

它还支持Scala和Groovy。

await until { something() > 4 } // Scala example

我在测试多线程代码时也遇到了严重的问题。然后我在Gerard Meszaros的“xUnit测试模式”中找到了一个非常酷的解决方案。他描述的模式被称为Humble object。

基本上，它描述了如何将逻辑提取到独立的、易于测试的组件中，该组件与环境解耦。在你测试了这个逻辑之后，你可以测试复杂的行为(多线程，异步执行，等等…)