Alberto Savoia的这篇散文恰好回答了这个问题(以一种非常有趣的方式!):

http://www.artima.com/forums/flat.jsp?forum=106&thread=204677

Testivus On Test Coverage Early one morning, a programmer asked the great master: “I am ready to write some unit tests. What code coverage should I aim for?” The great master replied: “Don’t worry about coverage, just write some good tests.” The programmer smiled, bowed, and left. ... Later that day, a second programmer asked the same question. The great master pointed at a pot of boiling water and said: “How many grains of rice should I put in that pot?” The programmer, looking puzzled, replied: “How can I possibly tell you? It depends on how many people you need to feed, how hungry they are, what other food you are serving, how much rice you have available, and so on.” “Exactly,” said the great master. The second programmer smiled, bowed, and left. ... Toward the end of the day, a third programmer came and asked the same question about code coverage. “Eighty percent and no less!” Replied the master in a stern voice, pounding his fist on the table. The third programmer smiled, bowed, and left. ... After this last reply, a young apprentice approached the great master: “Great master, today I overheard you answer the same question about code coverage with three different answers. Why?” The great master stood up from his chair: “Come get some fresh tea with me and let’s talk about it.” After they filled their cups with smoking hot green tea, the great master began to answer: “The first programmer is new and just getting started with testing. Right now he has a lot of code and no tests. He has a long way to go; focusing on code coverage at this time would be depressing and quite useless. He’s better off just getting used to writing and running some tests. He can worry about coverage later.” “The second programmer, on the other hand, is quite experience both at programming and testing. When I replied by asking her how many grains of rice I should put in a pot, I helped her realize that the amount of testing necessary depends on a number of factors, and she knows those factors better than I do – it’s her code after all. There is no single, simple, answer, and she’s smart enough to handle the truth and work with that.” “I see,” said the young apprentice, “but if there is no single simple answer, then why did you answer the third programmer ‘Eighty percent and no less’?” The great master laughed so hard and loud that his belly, evidence that he drank more than just green tea, flopped up and down. “The third programmer wants only simple answers – even when there are no simple answers … and then does not follow them anyway.” The young apprentice and the grizzled great master finished drinking their tea in contemplative silence.

根据代码的关键程度，75%-85%是一个很好的经验法则。 运输代码肯定应该比房屋公用事业等更彻底地测试。

我更喜欢做BDD，它使用自动化验收测试、可能还有其他集成测试和单元测试的组合。对我来说，问题是自动化测试套件作为一个整体的目标覆盖率应该是多少。

That aside, the answer depends on your methodology, language and testing and coverage tools. When doing TDD in Ruby or Python it's not hard to maintain 100% coverage, and it's well worth doing so. It's much easier to manage 100% coverage than 90-something percent coverage. That is, it's much easier to fill coverage gaps as they appear (and when doing TDD well coverage gaps are rare and usually worth your time) than it is to manage a list of coverage gaps that you haven't gotten around to and miss coverage regressions due to your constant background of uncovered code.

答案也取决于项目的历史。我发现上述方法只适用于从一开始就以这种方式管理的项目。我已经极大地改进了大型遗留项目的覆盖率，这样做是值得的，但是我从来没有发现回过头去填补每个覆盖率空白是可行的，因为旧的未经测试的代码不能很好地理解，不能正确和快速地完成这些工作。

我对这个难题的回答是，对可以测试的代码有100%的行覆盖率，对不能测试的代码有0%的行覆盖率。

我目前在Python中的做法是将.py模块分为两个文件夹:app1/和app2/，当运行单元测试时，计算这两个文件夹的覆盖率，并直观地检查(有朝一日我必须自动化)app1的覆盖率为100%，而app2的覆盖率为0%。

当/如果我发现这些数字与标准不同，我会调查并改变代码的设计，使覆盖率符合标准。

这意味着我可以建议实现库代码的100%行覆盖率。

我也偶尔检查app2/，看看我是否可以在那里测试任何代码，如果我可以，我将它移动到app1/

现在我不太担心总覆盖率，因为这取决于项目的规模，但通常情况下我看到的是70%到90%以上。

使用python，我应该能够设计一个烟雾测试，可以自动运行我的应用程序，同时测量覆盖率，并有希望获得100%的烟雾测试与单元测试数字的聚合。

我认为正确的代码覆盖率的最佳症状是单元测试帮助解决的具体问题的数量合理地对应于您创建的单元测试代码的大小。

85%是签入标准的一个很好的起点。

我可能会选择各种更高的发布标准——这取决于正在测试的子系统/组件的临界性。