I'm not a fan of unit testing. I'm just doing it because it's a requirement (and we still have no QA).
Why I'm not a fan? Because I'm doing my own tests. And probably it has a bias all over it. I know when it will have a successful run and I expect where it will fail. This is not the scenario that I wanted. (I know this is not a good testing scenario)
In my opinion, a programmer must write a good code. And leave the testing to the QA.
[1]: http://www.haskell.org/haskellwiki/Introduction_to_QuickChec...
This may sound complicated and seem like overkill, but I've actually found it easier to use this style of tests for much of my code. Having randomly generated inputs can help find edge cases I did not even consider when writing the code in the first place--this addresses the bias issue you are worried about.
I also find that this style of test results in concise, easy to read tests. The usual "hello world" example uses the reverse function for lists. We want to ensure that for all lists, reversing it twice does nothing. The test would look like this:
prop_reverseTwice ls = reverse (reverse ls) == ls
While real invariants are often more complex, I find they are still usually easy to read right from the code for the test.
Anyhow: you should really try QuickCheck or something like it for your language of choice.
However, in other cases, the usage is less obvious. For instance, suppose that you are writing, say a URL parser. What do you want QuickCheck to generate and check? If you generate too broadly, the invariant check becomes a parser itself, if you generate too specifically you might introduce bias again.
Amusingly, I also wrote some tree diff code in Haskell recently. However, I did it rather poorly so I'm going to have to rewrite it soon (probably some time during winter break). I definitely agree that algorithms like this are particularly well-suited for QuickCheck.
I always write good code, but somehow that code turns to shit after six months, and I promise no one touched it. So, I guess I don't need the tests when I'm writing the code but I need them badly when I maintain it.
I still try to avoid errors, but it's pretty much nigh impossible.
It is written by Kent Beck. He pretty much invented the concept of test-driven development. He's probably the most well-known advocate of developers writing their own tests there is.
What his answer demonstrates (to me) is that the conclusions he and the other XP guys arrived at - to write code in tiny chunks, each one preceded by a tiny failing test - is based entirely in pragmatism. He does it because it works better than anything else he's come across, including leaving the tests up to a dedicated QA team.
Except that it hasn't been shown to work better. Quoting from http://third-bit.com/blog/archives/4529.html: "If you ask its advocates for evidence, they’ll tell you why it has to be true; if you press them, you’ll be given anecdotes, and if you press harder, people will be either puzzled or hostile."
There is very little research which shows that TDD is better than other approaches. Perhaps a bit, for some cases, but not enough to be certain about its general applicability. (Or has this changed in the last three years since I researched this topic?)
I hate writing code in "tiny chunks, each one preceded by a tiny failing test." I prefer to use a small set of burn tests until I have the main structure in place. Only then do I do the tiny tests - through the high-level API where possible (so I can refactor later) - and I use coverage analysis to make sure I'm not missing any obvious tests.
Coverage analysis is not part of TDD, which is a shame. (Technically that's because TDD is a design methodology, while coverage analysis is part of testing. While TDD generates tests, these are not a complete set of tests. For example, security analysis and scalability are outside the scope of TDD.)
Oh good, evidence against is just as preachy and unscientific as the evidence for.
http://evidencebasedse.com/?q=taxonomy/term/2
Research suggests that unit testing has a very strong correlation with correct software. Research suggests TDD has no correlation with correct software, except for the fact that TDDers tend to write 2x more tests than non TDDers.
You don't have any, do you? So obviously I can now promote it as the new universal methodology.
(Or more directly, you think the argument is that there is evidence for and evidence against TDD. My argument is that there isn't enough evidence to make a conclusion.)
Given that this is a discourse, have you read any of the discussions of the topic of TDD? For example, "Making Software" (e.g Oram and Wilson) has an excellent chapter summarizing the research results. If not, then it's a rather unbalanced discourse, no? You pointed to a list of papers, but didn't describe what you wanted to discuss from them.
For example, some of the research compares test-first with test-last. I'm a proponent of test-during, and don't think that test-first and test-last are the only two ways to compare things.
You would need to compare teams of equal skill level who were working on very similar software over quite a long period of time.
What would the benchmarks be? Number of defects in production code? Time to ship? Severity of defects? Time spent debugging?
Besides, that my point, isn't it? Promotors of TDD argue that their personal intuition suffices, despite having little evidence to back their claims.
Aside from the "not my problem" vibe that gives off, I strongly disagree. QA is helpful for covering weird user interactions and writing tests against bugs discovered in production, but you as the programmer should know what your code is supposed to do while you're writing it. Best write tests then rather than six months later when you've forgotten what it was for.