tor-parallel-relay-conn/doc/HACKING/WritingTests.md

Writing tests for Tor: an incomplete guide

Tor uses a variety of testing frameworks and methodologies to try to keep from introducing bugs. The major ones are:

1. Unit tests written in C and shipped with the Tor distribution.

2. Integration tests written in Python and shipped with the Tor distribution.

3. Integration tests written in Python and shipped with the Stem library. Some of these use the Tor controller protocol.

4. System tests written in Python and SH, and shipped with the Chutney package. These work by running many instances of Tor locally, and sending traffic through them.

5. The Shadow network simulator.

How to run these tests

The easy version

To run all the tests that come bundled with Tor, run make check.

To run the Stem tests as well, fetch stem from the git repository, set STEM_SOURCE_DIR to the checkout, and run make test-stem.

To run the Chutney tests as well, fetch chutney from the git repository, set CHUTNEY_PATH to the checkout, and run make test-network.

To run all of the above, run make test-full.

To run all of the above, plus tests that require a working connection to the internet, run make test-full-online.

Running particular subtests

The Tor unit tests are divided into separate programs and a couple of bundled unit test programs.

Separate programs are easy. For example, to run the memwipe tests in isolation, you just run ./src/test/test-memwipe.

To run tests within the unit test programs, you can specify the name of the test. The string ".." can be used as a wildcard at the end of the test name. For example, to run all the cell format tests, enter ./src/test/test cellfmt/... To run

Many tests that need to mess with global state run in forked subprocesses in order to keep from contaminating one another. But when debugging a failing test, you might want to run it without forking a subprocess. To do so, use the --no-fork option with a single test. (If you specify it along with multiple tests, they might interfere.)

You can turn on logging in the unit tests by passing one of --debug, --info, --notice, or --warn. By default only errors are displayed.

Unit tests are divided into ./src/test/test and ./src/test/test-slow. The former are those that should finish in a few seconds; the latter tend to take more time, and may include CPU-intensive operations, deliberate delays, and stuff like that.

Finding test coverage

Test coverage is a measurement of which lines your tests actually visit.

When you configure Tor with the --enable-coverage option, it should build with support for coverage in the unit tests, and in a special tor-cov binary.

Then, run the tests you'd like to see coverage from. If you have old coverage output, you may need to run reset-gcov first.

Now you've got a bunch of files scattered around your build directories called *.gcda. In order to extract the coverage output from them, make a temporary directory for them and run ./scripts/test/coverage ${TMPDIR}, where ${TMPDIR} is the temporary directory you made. This will create a .gcov file for each source file under tests, containing that file's source annotated with the number of times the tests hit each line. (You'll need to have gcov installed.)

You can get a summary of the test coverage for each file by running ./scripts/test/cov-display ${TMPDIR}/* . Each line lists the file's name, the number of uncovered lines, the number of uncovered lines, and the coverage percentage.

For a summary of the test coverage for each function, run ./scripts/test/cov-display -f ${TMPDIR}/*.

Comparing test coverage

Sometimes it's useful to compare test coverage for a branch you're writing to coverage from another branch (such as git master, for example). But you can't run diff on the two coverage outputs directly, since the actual number of times each line is executed aren't so important, and aren't wholly deterministic.

Instead, follow the instructions above for each branch, creating a separate temporary directory for each. Then, run ./scripts/test/cov-diff ${D1} ${D2}, where D1 and D2 are the directories you want to compare. This will produce a diff of the two directories, with all lines normalized to be either covered or uncovered.

To count new or modified uncovered lines in D2, you can run:

./scripts/test/cov-diff ${D1} ${D2}" | grep '^+ *\#' | wc -l

What kinds of test should I write?

Integration testing and unit testing are complementary: it's probably a good idea to make sure that your code is hit by both if you can.

If your code is very-low level, and its behavior is easily described in terms of a relation between inputs and outputs, or a set of state transitions, then it's a natural fit for unit tests. (If not, please consider refactoring it until most of it is a good fit for unit tests!)

If your code adds new externally visible functionality to Tor, it would be great to have a test for that functionality. That's where integration tests more usually come in.

Unit and regression tests: Does this function do what it's supposed to?

Most of Tor's unit tests are made using the "tinytest" testing framework. You can see a guide to using it in the tinytest manual at

https://github.com/nmathewson/tinytest/blob/master/tinytest-manual.md

To add a new test of this kind, either edit an existing C file in src/test/, or create a new C file there. Each test is a single function that must be indexed in the table at the end of the file. We use the label "done:" as a cleanup point for all test functions.

(Make sure you read tinytest-manual.md before proceeding.)

I use the term "unit test" and "regression tests" very sloppily here.

A simple example

Here's an example of a test function for a simple function in util.c:

static void
test_util_writepid(void *arg)
{
  (void) arg;

  char *contents = NULL;
  const char *fname = get_fname("tmp_pid");
  unsigned long pid;
  char c;

  write_pidfile(fname);

  contents = read_file_to_str(fname, 0, NULL);
  tt_assert(contents);

  int n = sscanf(contents, "%lu\n%c", &pid, &c);
  tt_int_op(n, OP_EQ, 1);
  tt_int_op(pid, OP_EQ, getpid());

done:
  tor_free(contents);
}

This should look pretty familiar to you if you've read the tinytest manual. One thing to note here is that we use the testing-specific function get_fname to generate a file with respect to a temporary directory that the tests use. You don't need to delete the file; it will get removed when the tests are done.

Also note our use of OP_EQ instead of == in the tt_int_op() calls. We define OP_* macros to use instead of the binary comparison operators so that analysis tools can more easily parse our code. (Coccinelle really hates to see == used as a macro argument.)

Finally, remember that by convention, all *_free() functions that Tor defines are defined to accept NULL harmlessly. Thus, you don't need to say if (contents) in the cleanup block.

Exposing static functions for testing

Sometimes you need to test a function, but you don't want to expose it outside its usual module.

To support this, Tor's build system compiles a testing version of each module, with extra identifiers exposed. If you want to declare a function as static but available for testing, use the macro STATIC instead of static. Then, make sure there's a macro-protected declaration of the function in the module's header.

For example, crypto_curve25519.h contains:

#ifdef CRYPTO_CURVE25519_PRIVATE
STATIC int curve25519_impl(uint8_t *output, const uint8_t *secret,
                       const uint8_t *basepoint);
#endif

The crypto_curve25519.c file and the test_crypto.c file both define CRYPTO_CURVE25519_PRIVATE, so they can see this declaration.

STOP! Does this test really test?

When writing tests, it's not enough to just generate coverage on all the lines of the code that you're testing: It's important to make sure that the test really tests the code.

For example, here is a bad test for the unlink() function (which is supposed to remove a file).

static void
test_unlink_badly(void *arg)
{
  (void) arg;
  int r;

  const char *fname = get_fname("tmpfile");

  /* If the file isn't there, unlink returns -1 and sets ENOENT */
  r = unlink(fname);
  tt_int_op(n, OP_EQ, -1);
  tt_int_op(errno, OP_EQ, ENOENT);

  /* If the file DOES exist, unlink returns 0. */
  write_str_to_file(fname, "hello world", 0);
  r = unlink(fnme);
  tt_int_op(r, OP_EQ, 0);

done:
  tor_free(contents);
}

This test might get very high coverage on unlink(). So why is it a bad test? Because it doesn't check that unlink() actually removes the named file!

Remember, the purpose of a test is to succeed if the code does what it's supposed to do, and fail otherwise. Try to design your tests so that they check for the code's intended and documented functionality as much as possible.

Mock functions for testing in isolation

Often we want to test that a function works right, but the function to be tested depends on other functions whose behavior is hard to observe, or which require a working Tor network, or something like that.

To write tests for this case, you can replace the underlying functions with testing stubs while your unit test is running. You need to declare the underlying function as 'mockable', as follows:

MOCK_DECL(returntype, functionname, (argument list));

and then later implement it as:

MOCK_IMPL(returntype, functionname, (argument list))
{
   /* implementation here */
}

For example, if you had a 'connect to remote server' function, you could declare it as:

MOCK_DECL(int, connect_to_remote, (const char *name, status_t *status));

When you declare a function this way, it will be declared as normal in regular builds, but when the module is built for testing, it is declared as a function pointer initialized to the actual implementation.

In your tests, if you want to override the function with a temporary replacement, you say:

MOCK(functionname, replacement_function_name);

And later, you can restore the original function with:

UNMOCK(functionname);

For more information, see the definitions of this mocking logic in testsupport.h.

Okay but what should my tests actually do?

We talk above about "test coverage" -- making sure that your tests visit every line of code, or every branch of code. But visiting the code isn't enough: we want to verify that it's correct.

So when writing tests, try to make tests that should pass with any correct implementation of the code, and that should fail if the code doesn't do what it's supposed to do.

You can write "black-box" tests or "glass-box" tests. A black-box test is one that you write without looking at the structure of the function. A glass-box one is one you implement while looking at how the function is implemented.

In either case, make sure to consider common cases and edge cases; success cases and failure csaes.

For example, consider testing this function:

/** Remove all elements E from sl such that E==element.  Preserve
 * the order of any elements before E, but elements after E can be
 * rearranged.
 */
void smartlist_remove(smartlist_t *sl, const void *element);

In order to test it well, you should write tests for at least all of the following cases. (These would be black-box tests, since we're only looking at the declared behavior for the function:

• Remove an element that is in the smartlist.
• Remove an element that is not in the smartlist.
• Remove an element that appears in the smartlist more than once.

And your tests should verify that it behaves correct. At minimum, you should test:

• That other elements before E are in the same order after you call the functions.
• That the target element is really removed.
• That only the target element is removed.

When you consider edge cases, you might try:

• Remove an element from an empty list.
• Remove an element from a singleton list containing that element.
• Remove an element for a list containing several instances of that element, and nothing else.

Now let's look at the implementation:

void
smartlist_remove(smartlist_t *sl, const void *element)
{
  int i;
  if (element == NULL)
    return;
  for (i=0; i < sl->num_used; i++)
    if (sl->list[i] == element) {
      sl->list[i] = sl->list[--sl->num_used]; /* swap with the end */
      i--; /* so we process the new i'th element */
      sl->list[sl->num_used] = NULL;
    }
}

Based on the implementation, we now see three more edge cases to test:

• Removing NULL from the list.
• Removing an element from the end of the list
• Removing an element from a position other than the end of the list.

What should my tests NOT do?

Tests shouldn't require a network connection.

Whenever possible, tests shouldn't take more than a second. Put the test into test/slow if it genuinely needs to be run.

Tests should not alter global state unless they run with TT_FORK: Tests should not require other tests to be run before or after them.

Tests should not leak memory or other resources. To find out if your tests are leaking memory, run them under valgrind (see HelpfulTools.txt for more information on how to do that).

When possible, tests should not be over-fit to the implementation. That is, the test should verify that the documented behavior is implemented, but should not break if other permissible behavior is later implemented.

Advanced techniques: Namespaces

Sometimes, when you're doing a lot of mocking at once, it's convenient to isolate your identifiers within a single namespace. If this were C++, we'd already have namespaces, but for C, we do the best we can with macros and token-pasting.

We have some macros defined for this purpose in src/test/test.h. To use them, you define NS_MODULE to a prefix to be used for your identifiers, and then use other macros in place of identifier names. See src/test/test.h for more documentation.

Integration tests: Calling Tor from the outside

Some tests need to invoke Tor from the outside, and shouldn't run from the same process as the Tor test program. Reasons for doing this might include:

• Testing the actual behavior of Tor when run from the command line
• Testing that a crash-handler correctly logs a stack trace
• Verifying that violating a sandbox or capability requirement will actually crash the program.
• Needing to run as root in order to test capability inheritance or user switching.

To add one of these, you generally want a new C program in src/test. Add it to TESTS and noinst_PROGRAMS if it can run on its own and return success or failure. If it needs to be invoked multiple times, or it needs to be wrapped, add a new shell script to TESTS, and the new program to noinst_PROGRAMS. If you need access to any environment variable from the makefile (eg ${PYTHON} for a python interpreter), then make sure that the makefile exports them.

Writing integration tests with Stem

The 'stem' library includes extensive unit tests for the Tor controller protocol.

For more information on writing new tests for stem, have a look around the test/* directory in stem, and find a good example to emulate. You might want to start with https://gitweb.torproject.org/stem.git/tree/test/integ/control/controller.py to improve Tor's test coverage.

You can run stem tests from tor with make test-stem, or see https://stem.torproject.org/faq.html#how-do-i-run-the-tests.

System testing with Chutney

The 'chutney' program configures and launches a set of Tor relays, authorities, and clients on your local host. It has a test network functionality to send traffic through them and verify that the traffic arrives correctly.

You can write new test networks by adding them to networks. To add them to Tor's tests, add them to the test-network or test-network-all targets in Makefile.am.

(Adding new kinds of program to chutney will still require hacking the code.)