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404 lines
15 KiB
Markdown
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Writing tests for Tor: an incomplete guide
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==========================================
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Tor uses a variety of testing frameworks and methodologies to try to
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keep from introducing bugs. The major ones are:
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1. Unit tests written in C and shipped with the Tor distribution.
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2. Integration tests written in Python and shipped with the Tor
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distribution.
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3. Integration tests written in Python and shipped with the Stem
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library. Some of these use the Tor controller protocol.
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4. System tests written in Python and SH, and shipped with the
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Chutney package. These work by running many instances of Tor
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locally, and sending traffic through them.
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5. The Shadow network simulator.
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How to run these tests
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----------------------
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### The easy version
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To run all the tests that come bundled with Tor, run `make check`.
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To run the Stem tests as well, fetch stem from the git repository,
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set `STEM_SOURCE_DIR` to the checkout, and run `make test-stem`.
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To run the Chutney tests as well, fetch chutney from the git repository,
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set `CHUTNEY_PATH` to the checkout, and run `make test-network`.
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To run all of the above, run `make test-full`.
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To run all of the above, plus tests that require a working connection to the
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internet, run `make test-full-online`.
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### Running particular subtests
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The Tor unit tests are divided into separate programs and a couple of
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bundled unit test programs.
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Separate programs are easy. For example, to run the memwipe tests in
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isolation, you just run `./src/test/test-memwipe`.
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To run tests within the unit test programs, you can specify the name
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of the test. The string ".." can be used as a wildcard at the end of the
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test name. For example, to run all the cell format tests, enter
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`./src/test/test cellfmt/..`. To run
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Many tests that need to mess with global state run in forked subprocesses in
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order to keep from contaminating one another. But when debugging a failing test,
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you might want to run it without forking a subprocess. To do so, use the
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`--no-fork` option with a single test. (If you specify it along with
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multiple tests, they might interfere.)
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You can turn on logging in the unit tests by passing one of `--debug`,
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`--info`, `--notice`, or `--warn`. By default only errors are displayed.
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Unit tests are divided into `./src/test/test` and `./src/test/test-slow`.
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The former are those that should finish in a few seconds; the latter tend to
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take more time, and may include CPU-intensive operations, deliberate delays,
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and stuff like that.
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### Finding test coverage
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Test coverage is a measurement of which lines your tests actually visit.
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When you configure Tor with the `--enable-coverage` option, it should
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build with support for coverage in the unit tests, and in a special
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`tor-cov` binary.
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Then, run the tests you'd like to see coverage from. If you have old
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coverage output, you may need to run `reset-gcov` first.
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Now you've got a bunch of files scattered around your build directories
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called `*.gcda`. In order to extract the coverage output from them, make a
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temporary directory for them and run `./scripts/test/coverage ${TMPDIR}`,
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where `${TMPDIR}` is the temporary directory you made. This will create a
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`.gcov` file for each source file under tests, containing that file's source
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annotated with the number of times the tests hit each line. (You'll need to
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have gcov installed.)
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You can get a summary of the test coverage for each file by running
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`./scripts/test/cov-display ${TMPDIR}/*` . Each line lists the file's name,
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the number of uncovered lines, the number of uncovered lines, and the
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coverage percentage.
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For a summary of the test coverage for each _function_, run
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`./scripts/test/cov-display -f ${TMPDIR}/*`.
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### Comparing test coverage
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Sometimes it's useful to compare test coverage for a branch you're writing to
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coverage from another branch (such as git master, for example). But you
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can't run `diff` on the two coverage outputs directly, since the actual
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number of times each line is executed aren't so important, and aren't wholly
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deterministic.
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Instead, follow the instructions above for each branch, creating a separate
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temporary directory for each. Then, run `./scripts/test/cov-diff ${D1}
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${D2}`, where D1 and D2 are the directories you want to compare. This will
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produce a diff of the two directories, with all lines normalized to be either
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covered or uncovered.
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To count new or modified uncovered lines in D2, you can run:
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./scripts/test/cov-diff ${D1} ${D2}" | grep '^+ *\#' | wc -l
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What kinds of test should I write?
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----------------------------------
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Integration testing and unit testing are complementary: it's probably a
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good idea to make sure that your code is hit by both if you can.
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If your code is very-low level, and its behavior is easily described in
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terms of a relation between inputs and outputs, or a set of state
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transitions, then it's a natural fit for unit tests. (If not, please
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consider refactoring it until most of it _is_ a good fit for unit
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tests!)
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If your code adds new externally visible functionality to Tor, it would
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be great to have a test for that functionality. That's where
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integration tests more usually come in.
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Unit and regression tests: Does this function do what it's supposed to?
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-----------------------------------------------------------------------
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Most of Tor's unit tests are made using the "tinytest" testing framework.
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You can see a guide to using it in the tinytest manual at
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https://github.com/nmathewson/tinytest/blob/master/tinytest-manual.md
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To add a new test of this kind, either edit an existing C file in `src/test/`,
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or create a new C file there. Each test is a single function that must
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be indexed in the table at the end of the file. We use the label "done:" as
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a cleanup point for all test functions.
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(Make sure you read `tinytest-manual.md` before proceeding.)
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I use the term "unit test" and "regression tests" very sloppily here.
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### A simple example
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Here's an example of a test function for a simple function in util.c:
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static void
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test_util_writepid(void *arg)
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{
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(void) arg;
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char *contents = NULL;
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const char *fname = get_fname("tmp_pid");
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unsigned long pid;
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char c;
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write_pidfile(fname);
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contents = read_file_to_str(fname, 0, NULL);
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tt_assert(contents);
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int n = sscanf(contents, "%lu\n%c", &pid, &c);
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tt_int_op(n, OP_EQ, 1);
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tt_int_op(pid, OP_EQ, getpid());
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done:
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tor_free(contents);
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}
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This should look pretty familiar to you if you've read the tinytest
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manual. One thing to note here is that we use the testing-specific
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function `get_fname` to generate a file with respect to a temporary
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directory that the tests use. You don't need to delete the file;
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it will get removed when the tests are done.
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Also note our use of `OP_EQ` instead of `==` in the `tt_int_op()` calls.
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We define `OP_*` macros to use instead of the binary comparison
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operators so that analysis tools can more easily parse our code.
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(Coccinelle really hates to see `==` used as a macro argument.)
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Finally, remember that by convention, all `*_free()` functions that
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Tor defines are defined to accept NULL harmlessly. Thus, you don't
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need to say `if (contents)` in the cleanup block.
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### Exposing static functions for testing
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Sometimes you need to test a function, but you don't want to expose
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it outside its usual module.
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To support this, Tor's build system compiles a testing version of
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each module, with extra identifiers exposed. If you want to
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declare a function as static but available for testing, use the
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macro `STATIC` instead of `static`. Then, make sure there's a
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macro-protected declaration of the function in the module's header.
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For example, `crypto_curve25519.h` contains:
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#ifdef CRYPTO_CURVE25519_PRIVATE
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STATIC int curve25519_impl(uint8_t *output, const uint8_t *secret,
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const uint8_t *basepoint);
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#endif
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The `crypto_curve25519.c` file and the `test_crypto.c` file both define
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`CRYPTO_CURVE25519_PRIVATE`, so they can see this declaration.
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### Mock functions for testing in isolation
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Often we want to test that a function works right, but the function to
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be tested depends on other functions whose behavior is hard to observe,
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or which require a working Tor network, or something like that.
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To write tests for this case, you can replace the underlying functions
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with testing stubs while your unit test is running. You need to declare
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the underlying function as 'mockable', as follows:
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MOCK_DECL(returntype, functionname, (argument list));
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and then later implement it as:
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MOCK_IMPL(returntype, functionname, (argument list))
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{
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/* implementation here */
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}
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For example, if you had a 'connect to remote server' function, you could
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declare it as:
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MOCK_DECL(int, connect_to_remote, (const char *name, status_t *status));
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When you declare a function this way, it will be declared as normal in
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regular builds, but when the module is built for testing, it is declared
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as a function pointer initialized to the actual implementation.
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In your tests, if you want to override the function with a temporary
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replacement, you say:
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MOCK(functionname, replacement_function_name);
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And later, you can restore the original function with:
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UNMOCK(functionname);
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For more information, see the definitions of this mocking logic in
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`testsupport.h`.
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### Okay but what should my tests actually do?
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We talk above about "test coverage" -- making sure that your tests visit
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every line of code, or every branch of code. But visiting the code isn't
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enough: we want to verify that it's correct.
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So when writing tests, try to make tests that should pass with any correct
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implementation of the code, and that should fail if the code doesn't do what
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it's supposed to do.
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You can write "black-box" tests or "glass-box" tests. A black-box test is
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one that you write without looking at the structure of the function. A
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glass-box one is one you implement while looking at how the function is
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implemented.
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In either case, make sure to consider common cases *and* edge cases; success
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cases and failure csaes.
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For example, consider testing this function:
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/** Remove all elements E from sl such that E==element. Preserve
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* the order of any elements before E, but elements after E can be
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* rearranged.
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*/
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void smartlist_remove(smartlist_t *sl, const void *element);
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In order to test it well, you should write tests for at least all of the
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following cases. (These would be black-box tests, since we're only looking
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at the declared behavior for the function:
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* Remove an element that is in the smartlist.
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* Remove an element that is not in the smartlist.
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* Remove an element that appears in the smartlist more than once.
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And your tests should verify that it behaves correct. At minimum, you should
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test:
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* That other elements before E are in the same order after you call the
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functions.
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* That the target element is really removed.
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* That _only_ the target element is removed.
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When you consider edge cases, you might try:
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* Remove an element from an empty list.
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* Remove an element from a singleton list containing that element.
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* Remove an element for a list containing several instances of that
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element, and nothing else.
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Now let's look at the implementation:
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void
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smartlist_remove(smartlist_t *sl, const void *element)
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{
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int i;
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if (element == NULL)
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return;
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for (i=0; i < sl->num_used; i++)
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if (sl->list[i] == element) {
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sl->list[i] = sl->list[--sl->num_used]; /* swap with the end */
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i--; /* so we process the new i'th element */
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sl->list[sl->num_used] = NULL;
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}
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}
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Based on the implementation, we now see three more edge cases to test:
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* Removing NULL from the list.
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* Removing an element from the end of the list
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* Removing an element from a position other than the end of the list.
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### What should my tests NOT do?
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Tests shouldn't require a network connection.
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Whenever possible, tests shouldn't take more than a second. Put the test
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into test/slow if it genuinely needs to be run.
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Tests should not alter global state unless they run with `TT_FORK`: Tests
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should not require other tests to be run before or after them.
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Tests should not leak memory or other resources. To find out if your tests
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are leaking memory, run them under valgrind (see HelpfulTools.txt for more
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information on how to do that).
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When possible, tests should not be over-fit to the implementation. That is,
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the test should verify that the documented behavior is implemented, but
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should not break if other permissible behavior is later implemented.
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### Advanced techniques: Namespaces
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Sometimes, when you're doing a lot of mocking at once, it's convenient to
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isolate your identifiers within a single namespace. If this were C++, we'd
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already have namespaces, but for C, we do the best we can with macros and
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token-pasting.
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We have some macros defined for this purpose in `src/test/test.h`. To use
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them, you define `NS_MODULE` to a prefix to be used for your identifiers, and
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then use other macros in place of identifier names. See `src/test/test.h` for
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more documentation.
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Integration tests: Calling Tor from the outside
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-----------------------------------------------
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Some tests need to invoke Tor from the outside, and shouldn't run from the
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same process as the Tor test program. Reasons for doing this might include:
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* Testing the actual behavior of Tor when run from the command line
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* Testing that a crash-handler correctly logs a stack trace
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* Verifying that violating a sandbox or capability requirement will
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actually crash the program.
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* Needing to run as root in order to test capability inheritance or
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user switching.
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To add one of these, you generally want a new C program in `src/test`. Add it
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to `TESTS` and `noinst_PROGRAMS` if it can run on its own and return success or
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failure. If it needs to be invoked multiple times, or it needs to be
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wrapped, add a new shell script to `TESTS`, and the new program to
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`noinst_PROGRAMS`. If you need access to any environment variable from the
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makefile (eg `${PYTHON}` for a python interpreter), then make sure that the
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makefile exports them.
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Writing integration tests with Stem
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-----------------------------------
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The 'stem' library includes extensive unit tests for the Tor controller
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protocol.
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For more information on writing new tests for stem, have a look around
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the `test/*` directory in stem, and find a good example to emulate. You
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might want to start with
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`https://gitweb.torproject.org/stem.git/tree/test/integ/control/controller.py`
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to improve Tor's test coverage.
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You can run stem tests from tor with `make test-stem`, or see
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`https://stem.torproject.org/faq.html#how-do-i-run-the-tests`.
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System testing with Chutney
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---------------------------
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The 'chutney' program configures and launches a set of Tor relays,
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authorities, and clients on your local host. It has a `test network`
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functionality to send traffic through them and verify that the traffic
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arrives correctly.
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You can write new test networks by adding them to `networks`. To add
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them to Tor's tests, add them to the `test-network` or `test-network-all`
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targets in `Makefile.am`.
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(Adding new kinds of program to chutney will still require hacking the
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code.)
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