Is mutation testing useful in practice

I've actually used your first approach with quite some success, but in a slightly different ways that I think would solve some of your problems:

1. Keep the entire schema and scripts for creating it in source control so that anyone can create the current database schema after a check out. In addition, keep sample data in data files that get loaded by part of the build process. As you discover data that causes errors, add it to your sample data to check that errors don't re-emerge.

2. Use a continuous integration server to build the database schema, load the sample data, and run tests. This is how we keep our test database in sync (rebuilding it at every test run). Though this requires that the CI server have access and ownership of its own dedicated database instance, I say that having our db schema built 3 times a day has dramatically helped find errors that probably would not have been found till just before delivery (if not later). I can't say that I rebuild the schema before every commit. Does anybody? With this approach you won't have to (well maybe we should, but its not a big deal if someone forgets).

3. For my group, user input is done at the application level (not db) so this is tested via standard unit tests.

Loading Production Database Copy:
This was the approach that was used at my last job. It was a huge pain cause of a couple of issues:

1. The copy would get out of date from the production version
2. Changes would be made to the copy's schema and wouldn't get propagated to the production systems. At this point we'd have diverging schemas. Not fun.

Mocking Database Server:
We also do this at my current job. After every commit we execute unit tests against the application code that have mock db accessors injected. Then three times a day we execute the full db build described above. I definitely recommend both approaches.

Code coverage is a measurement of how many lines/blocks/arcs of your code are executed while the automated tests are running.

Code coverage is collected by using a specialized tool to instrument the binaries to add tracing calls and run a full set of automated tests against the instrumented product. A good tool will give you not only the percentage of the code that is executed, but also will allow you to drill into the data and see exactly which lines of code were executed during a particular test.

Our team uses Magellan - an in-house set of code coverage tools. If you are a .NET shop, Visual Studio has integrated tools to collect code coverage. You can also roll some custom tools, like this article describes.

If you are a C++ shop, Intel has some tools that run for Windows and Linux, though I haven't used them. I've also heard there's the gcov tool for GCC, but I don't know anything about it and can't give you a link.

As to how we use it - code coverage is one of our exit criteria for each milestone. We have actually three code coverage metrics - coverage from unit tests (from the development team), scenario tests (from the test team) and combined coverage.

BTW, while code coverage is a good metric of how much testing you are doing, it is not necessarily a good metric of how well you are testing your product. There are other metrics you should use along with code coverage to ensure the quality.