并发是内存模型、硬件、缓存和代码之间复杂的相互作用。在Java的情况下，至少这样的测试主要由jcstress部分解决。众所周知，该库的创建者是许多JVM、GC和Java并发特性的作者。

但是即使是这个库也需要对Java内存模型规范有很好的了解，这样我们才能确切地知道我们在测试什么。但我认为这项工作的重点是微基准测试。不是庞大的业务应用。

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。

上周我花了大部分时间在大学图书馆学习并发代码的调试。核心问题是并发代码是不确定的。通常，学术调试可以分为三个阵营之一:

Event-trace/replay. This requires an event monitor and then reviewing the events that were sent. In a UT framework, this would involve manually sending the events as part of a test, and then doing post-mortem reviews. Scriptable. This is where you interact with the running code with a set of triggers. "On x > foo, baz()". This could be interpreted into a UT framework where you have a run-time system triggering a given test on a certain condition. Interactive. This obviously won't work in an automatic testing situation. ;)

现在，正如上面评论者所注意到的，您可以将并发系统设计成更确定的状态。然而，如果你做得不好，你就又回到了设计顺序系统的问题上。

我的建议是，专注于制定一个非常严格的设计协议，规定什么是线程，什么不是线程。如果你限制了你的接口，使元素之间的依赖最小化，那就容易多了。

祝你好运，继续解决这个问题。

近年来，在为几个项目编写线程处理代码时，我多次遇到过这个问题。我提供了一个迟来的答案，因为大多数其他答案虽然提供了替代方案，但实际上并没有回答关于测试的问题。我的答案是针对多线程代码没有替代方案的情况;为了完整性，我将讨论代码设计问题，但也将讨论单元测试。

编写可测试的多线程代码

首先要做的是将生产线程处理代码与所有执行实际数据处理的代码分开。这样，数据处理就可以作为单线程代码进行测试，多线程代码所做的唯一事情就是协调线程。

The second thing to remember is that bugs in multithreaded code are probabilistic; the bugs that manifest themselves least frequently are the bugs that will sneak through into production, will be difficult to reproduce even in production, and will thus cause the biggest problems. For this reason, the standard coding approach of writing the code quickly and then debugging it until it works is a bad idea for multithreaded code; it will result in code where the easy bugs are fixed and the dangerous bugs are still there.

相反，在编写多线程代码时，必须抱着一种从一开始就避免编写错误的态度来编写代码。如果您已经正确地删除了数据处理代码，线程处理代码应该足够小——最好只有几行，最坏也就几十行——这样您就有机会在不编写错误的情况下编写它，当然也不会编写很多错误，如果您了解线程，请慢慢来，并且小心。

为多线程代码编写单元测试

一旦尽可能仔细地编写了多线程代码，仍然值得为该代码编写测试。测试的主要目的与其说是测试高度依赖于时间的竞争条件错误(不可能重复测试这种竞争条件)，不如说是测试防止这种错误的锁定策略是否允许多个线程按预期进行交互。

To properly test correct locking behavior, a test must start multiple threads. To make the test repeatable, we want the interactions between the threads to happen in a predictable order. We don't want to externally synchronize the threads in the test, because that will mask bugs that could happen in production where the threads are not externally synchronized. That leaves the use of timing delays for thread synchronization, which is the technique that I have used successfully whenever I've had to write tests of multithreaded code.

If the delays are too short, then the test becomes fragile, because minor timing differences - say between different machines on which the tests may be run - may cause the timing to be off and the test to fail. What I've typically done is start with delays that cause test failures, increase the delays so that the test passes reliably on my development machine, and then double the delays beyond that so the test has a good chance of passing on other machines. This does mean that the test will take a macroscopic amount of time, though in my experience, careful test design can limit that time to no more than a dozen seconds. Since you shouldn't have very many places requiring thread coordination code in your application, that should be acceptable for your test suite.

Finally, keep track of the number of bugs caught by your test. If your test has 80% code coverage, it can be expected to catch about 80% of your bugs. If your test is well designed but finds no bugs, there's a reasonable chance that you don't have additional bugs that will only show up in production. If the test catches one or two bugs, you might still get lucky. Beyond that, and you may want to consider a careful review of or even a complete rewrite of your thread handling code, since it is likely that code still contains hidden bugs that will be very difficult to find until the code is in production, and very difficult to fix then.

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。

我曾经有过测试线程代码的不幸任务，这绝对是我写过的最难的测试。

在编写测试时，我使用委托和事件的组合。基本上，它都是关于使用PropertyNotifyChanged事件和WaitCallback或某种轮询的ConditionalWaiter。

我不确定这是否是最好的方法，但它对我来说是有效的。