Testing framework for GRASS GIS

Title:

Testing framework for GRASS GIS

Student:

Vaclav Petras, ​North Carolina State University, Open Source Geospatial Research and Education Laboratory

Organization:

​OSGeo - Open Source Geospatial Foundation

Mentors:

​Sören Gebbert, ​Helena Mitasova

GSoC link:

​abstract

Abstract

GRASS GIS is one of the core projects in the OSGeo Foundation. GRASS provides wide range of geospatial analyses including raster and vector analyses and image processing. However, there is no system for regular testing of it's algorithms. To ensure software quality and reliability, a standardized way of testing needs to be introduced. This project will implement a testing framework which can be used for writing and running tests of GRASS GIS modules, C/C++ libraries and Python libraries.

Introduction

GRASS GIS is one of the core projects in the OSGeo Foundation and is used by several other free and open source projects to perform geoprocessing tasks. The software quality and reliability is crucial. Thus, proper testing is needed. So far, the testing was done manually by both developers and users. This is questionable in terms of test coverage and frequency of the tests and moreover, it is inconvenient. This project will implement a testing framework which can be used for writing and running tests for GRASS GIS. This will be beneficial not only for the quality of GRASS GIS but also for everyday development of GRASS GIS because it will help to identify problems with the new code at the time when the change is done.

Background

There was already several attempts to establish testing infrastructure for GRASS GIS, namely ​quality assessment and monitoring mailing list which is inactive for several years, then older test suite which was never integrated into GRASS GIS itself, and most recently a ​test suite proposal which was trying to interpret shell scripts as test cases. Also, an experience​ with usage of Python ​doctest at different circumstances shows that this solution is not applicable everywhere.

These previous experiences give us a clear idea what is not working (e.g. tests outside main source code), what is overcomplicated (e.g. reimplementing shell) and what is oversimplified (e.g. shell scripts without clear set up and tear down steps), and point us to the direction of an implementation which will be most efficient (general but simple enough), integrated in GRASS source code, and which will be accepted by the GRASS development team. The long preceding discussions also showed what is necessary to have in the testing framework and what should be left out.

The idea

The purpose of this project is to develop a general mechanism which would be applicable for testing GRASS modules, libraries or workflows with different data sets. Tests will be part of GRASS main source code, cross-platform, and as easy to write and run as possible. The testing framework will enable the use of different testing data sets because different test cases might need special data. The testing framework will be implemented in Python and based on testing tools included in standard Python distribution (most notably ​unittest) which will not bring a new dependency but also it will avoid writing everything from scratch. The usage of Makefile system will be limited to triggering the test or tests with the right parameters for particular location in the source tree, everything else will be implemented in Python to ensure maximum re-usability, maintainability, and availability across platforms.

This project will focus on building infrastructure to test modules, C/C++ libraries (using ctypes interface), and Python libraries. It is expected that testing of Python GUI code will be limited to pure Python parts. The focus will be on the majority of GRASS modules and functionality while special cases such as rendering, creation of locations, external data sources and databases, and downloading of extensions from GRASS Addons will be left for future work. Moreover, this project will not cover tests of graphical user interface, server side automatic testing (e.g. commit hooks), using testing shell scripts or C/C++ programs, and testing of internal functions in C/C++ code (e.g. static functions in libraries and functions in modules). Creation of HTML, XML, or other rich outputs will not be completely solved but the implementation will consider the need for a presentation of test results. Finally, writing the tests for particular parts will not be part of this project, however several sample tests for different parts of code, especially modules, will be written to test the testing framework.

Project plan

date	proposed task
2014-05-19 - 2014-05-23 (week 01)	Designing a basic template for the test case and interface of test suite class(es)
2014-05-26 - 2014-05-30 (week 02)	Basic implementation
2014-06-02 - 2014-06-06 (week 03)	Dealing with evaluation and comparison of textual and numerical outputs
2014-06-09 - 2014-06-13 (week 04)	Dealing with evaluation and comparison of map outputs and other outputs
2014-06-16 - 2014-06-20 (week 05)	Re-writing some existing tests using testing framework
2014-06-23 - 2014-06-27 (week 06)	Testing of what was written so far and evaluating current design and implementation
June 23	Mentors and students can begin submitting mid-term evaluations
June 27	Mid-term evaluations deadline
2014-06-30 - 2014-07-04 (week 07)	Integration with GRASS source code, documentation and build system
2014-07-07 - 2014-07-11 (week 08)	Implementation of location switching
2014-07-14 - 2014-07-18 (week 09)	Dealing with evaluation and comparison of so far unresolved outputs
2014-07-21 - 2014-07-25 (week 10)	Implementing the basic test results reports
2014-07-28 - 2014-08-01 (week 11)	Re-writing some other existing tests using testing framework
2014-08-04 - 2014-08-08 (week 12)	Writing documentation of framework internals and guidelines how to write tests
2014-08-11 - 2014-08-15 (week 13)	Polish the code and documentation
August 11	Suggested 'pencils down' date. Take a week to scrub code, write tests, improve documentation, etc.
2014-08-18 - 2014-08-22 (week 14)	Submit evaluation and code to Google
August 18	Firm 'pencils down' date. Mentors, students and organization administrators can begin submitting final evaluations to Google.
August 22	Final evaluation deadline
August 22	Students can begin submitting required code samples to Google

Design of testing API and implementation

import unittest
import grass.pygrass.modules as gmodules

# alternatively, these can be private to module with setter and getter
# or it can be in a class
USE_VALGRIND = False


class GrassTestCase(unittest.TestCase):
    """Base class for GRASS test cases."""

    def run_module(self, module):
        """Method to run the module. It will probably use some class or instance variables"""
        # get command from pygrass module
        command = module.make_cmd()
        # run command using valgrind if desired and module is not python script
        # see also valgrind notes at be end of this section
        if is_not_python_script(command[0]) and USE_VALGRIND:
            command = ['valgrind', '--tool=...', '--xml=...', '--xml-file=...'] + command
        # run command
        # store valgrind output (memcheck has XML output to a file)
        # store module return code, stdout and stderr, how to distinguish from valgrind?
        # return code, stdout and stderr could be returned in tuple
    
    def assertRasterMap(self, actual, reference, msg=None):
        # e.g. g.compare.md5 from addons
        # uses msg if provided, generates its own if not,
        # or both if self.longMessage is True (unittest.TestCase.longMessage)
        # precision should be considered too (for FCELL and DCELL but perhaps also for CELL)
        # the actual implementation will be in separate module, so it can be reused by doctests or elsewhere
        # this is necessary considering the number and potential complexity of functions
        # and it is better design anyway

        if check sums not equal:
            self.fail(...)  # unittest.TestCase.fail

class SomeModuleTestCase(GrassTestCase):
    """Example of test case for a module."""
    
    def test_flag_g(self):
        """Test to validate the output of r.info using flag "g"
        """
        # Configure a r.info test 
        module = gmodules.Module("r.info", map="test", flags="g", run_=False)

        self.run_module(module=module)
        # it is not clear where to store stdout and stderr
        self.assertStdout(actual=module.stdout, reference="r_info_g.ref")
        
    def test_something_complicated(self):
        """Test something which has several outputs
        """
        # Configure a r.info test 
        module = gmodules.Module("r.complex", rast="test", vect="test", flags="p", run_=False)
        
        (ret, stdout, stderr) = self.run_module(module=module)
        self.assertEqual(ret, 0, "Module should have suceed but return code is not 0")
        self.assertStdout(actual=stdout, reference="r_complex_stdout.ref")
        self.assertRasterMap(actual=module.rast, reference="r_complex_rast.ref")
        self.assertVectorMap(actual=module.vect, reference="r_complex_vect.ref")

Compared to suggestion in ticket:2105#comment:4 it does not solve everything in test_module (run_module) function but it uses self.assert* similarly to unittest.TestCase which (syntactically) allows to check more then one thing.

Finding and running the test modules

Test modules/scripts must have module/package character. This applies for both unittests and doctests, no exceptions. To have the possibility of import, all the GRASS Python libraries shouldn't do anything fancy at import time. For example, doctests currently don't work with grass.script unless you call a set of functions​ to deal with function _ (underscore) because of installing translate function as buildin _ function while _ function is used also in doctest. (This is fixed for GUI but still applies to Python libraries.)

Doctests (inside normal module code, in separate documentation, or doctests created just for the purpose of testing, see ​explanation of different doctest use cases) will be wrapped as unittest test cases (in the testsuite directory). There is a ​standard way to do it. Everything requires the possibility to import safely (without side effects).

unittest default implementation expects tests to not only by importable but really on path, so we would need to add the tested module on path beforehand in case we will use the classes as they are. In theory, it should be enough to add the directory containing file to the sys.path. This would add the advantage of invoking even separate test methods (testmodule.TestCase.test_method). It seems that simple sys.path.insert(0, '.') (if we are in directory with the test) and than import with name of the module works. On the other hand, the test discovery does much more tricks to get everything right, so it might be more robust than just adding directory to sys.path.

# file: gunittest/grass_main_test_runner.py
from main import main
sys.path.insert(0, '.')
main()
# simplified)

# run only one particular test method
cd lib/python/temporal(/testsuite)
python .../gunittest/grass_main_test_runner.py unittests_register.TestRegisterFunctions.test_absolute_time_strds_2

An alternative is to use test discovery even for cases when we know what we are looking for. The most easy way how to run tests in one file would be to use test discovery with a file name as pattern and file path as search directory (which limits the discovery just the one particular file). The disadvantage is that it is not possible to run individual test methods (as in the case of direct imports from path).

# invoke test module/script using test discovery but run only one module/script
python .../gunittest/grass_main_test_runner.py discover -s lib/python/temporal/ -p unittests_register.py

GRASS modules which are Python scripts have one or more dots in the file name. This prevents them from being imported and so from being used in the tests directly. They itself can run themselves as tests but the testing framework is not able to run them using unittest's methods. Similarly, tests cannot load the functions from the file because this would require import. Doctest requires importable files too, so using it instead of unittest to test functions inside a script will not help.

Dealing with process termination

There is no easy way how to test that (GRASS) fatal errors are invoked when appropriate. Even if the test method (test_*) itself would run in separate process (not only the whole script) the process will be ended without proper reporting of the test result (considering we want detailed test results). However, since this applies also to fatal errors invoked by unintentional failure and to fatal errors, it seems that it will be necessary to invoke the test methods (test_*) in a separate process to at least finish the other tests and not break the final report. This might be done by function decorator so that we don't invoke new process for each function but only for those who need it (the ones using things which use ctypes).

How the individual test methods are invoked

This describes how unittest is doing it and we want to use as many classes (directly or by inheriting) from unittest as possible, so it will be probably also our system.

Loading of tests (pseudo code):

# finds all test methods of a given test case and creates test suite
# test suite class will get a list of instances of test case class, each with one method name (test method)
# testCaseNames contains test method names
def TestLoader.loadTestsFromTestCase(testCaseClass):
    testCaseNames = filter(isTestMethod, dir(testCaseClass))
    loaded_suite = self.suiteClass(map(testCaseClass, testCaseNames))

# finds all test cases in a module/script
def TestLoader.loadTestsFromModule(module):
    for each object in module:
        if isinstance(obj, type) and issubclass(obj, case.TestCase):
            tests.append(self.loadTestsFromTestCase(obj))
    suiteClass = suite.TestSuite
    tests = self.suiteClass(tests)

# finds all tests (by direct import of what was specified or test discovery)
# passes found tests to test runner
def TestProgram.runTests():
    self.test = self.testLoader.loadTestsFromModule(self.module)
    self.result = testRunner.run(self.test)

# triggers the top level test suite
def TextTestRunner.run(test):
    test(result)

Running of tests (pseudo code):

# invokes the separate tests
# does setup and teardown for class
# note that test suite can contain test suite or test case instances
def TestSuite.run(result):
    for each test:
        test(result)

# runs the test method and adds the information to the result
# does setup and teardown for an test case instance
def TestCase.run(result):
     ...

Analyzing module run using Valgrind or others

Modules (or perhaps any tests) can run with valgrind (probably --tool=memcheck). This could be done on the level of testing classes but the better option is to integrate this functionality (optional running with valgrind) into PyGRASS, so it could be easily usable through it. Environmental variable (GRASS_PYGRASS_VALGRIND) or additional option valgrind_=True (similarly to overwrite) would invoke module with valgrind (works for both binaries and scripts). Additional options can be passed to valgrind using valgrind's environmental variable $VALGRIND_OPTS. Output would be saved in file to not interfere with module output.

We may want to use also some (runtime checking) tools other than valgrind, for example clang/LLVM sanitizers (as for example ​Python does) or profiling (these would be different for C/C++ and Python). However, it is unclear how to handle more than one tool as well as it is unclear how to store the results for any of these (including valgrind) because one test can have multiple module calls (or none), module calls can be indirect (function in Python lib which calls a module or module calling module) and there is no standard way in unittest to pass additional test details.

PyGRASS or specialized PyGRASS module runner (function) in testing framework can have function, global variable, or environmental variable which would specify which tool should run a module (if any) and what are the parameters (besides the possibility to set parameters by environmental variable defined by the tool). The should ideally be separated from the module output and go to a file in the test output directory (and it could be later linked from/included into the main report).

Having output from many modules can be confusing (e.g. we run r.info before actually running our module). It would be ideal if it would be possible to specify which modules called in the test should run with valgrind or other tool. API for this may, however, interfere with the API for global settings of running with these tools.

It is not clear if valgrind would be applied even for library tests. This would require to run the testing process with valgrind. But since it needs to run separately anyway, this can be done. In case we would like to run with valgrind test function (test_*), the testing framework would have to contain the valgrind running function anyway. The function would run the test function as subprocess (which is anyway necessary to deal with process termination). The advantage of integration into PyGRASS wouldn't be so big in this case. But even in the case of separate function for running subprocess, a PyGRASS Module class will be used to pass the parameters.

Other source code analyses

Concerning static source code analysis, it seems that it is better to do it separately because it depends more on source code files or, in case of Python, modules and packages while tests seems to have their own structure. Also static source code analysis is not related to testing itself, although it is, for sure, part of quality assurance. However, it must be noted that some part of analysis can be one test case as suggested by ​pep8 documentation (test fails if code style requirements are not fulfilled). This could integrate pep8 with testing framework in an elegant way but it would require each testsuite to explicitly request this check.

For the static analysis of Python code, there is several tools commonly (i.e. in repositories of Linux distributions) available, most notably pep8, Pylint, and Flake8. Table of tools and their features (June 2014):

tool	focus	HTML report	imports modules
​pep8	style (​PEP8)	no	no
​Pylint	errors and style	yes	​no
​PyChecker	errors		yes
​PyFlakes	errors
​McCabe complexity checker	code ​cyclomatic complexity
​Flake8	combines PyFlakes, pep8, and mccabe	yes

The determining code coverage of the tests must be done during testing. Speaking about Python, some more advanced testing frameworks includes code coverage tools in them, namely ​coverage. Since unittest does not include coverage, GRASS will have to incorporate coverage into its testing framework. The basic usage is rather simple. If we consider that all is running in one Python process, we can just put the following (rather messy) code into some module which will be loaded before the tests will start (the main invoking script would be of course the best place):

import coverage
cov = coverage.coverage(omit="*testsuite*")
cov.start()

def endcov(cov):
    cov.stop()
    cov.html_report(directory='testcodecoverage')

import atexit
atexit.register(endcov, cov)

However, the HTML reports needs some polishing (especially the names of modules and paths) and moreover, the reports will be spitted by running different tests as separate processes. It it possible to link to these subreports and it might be even advantageous. But what is really an issue is running separate methods as separate processes, so the report is split in the way that it we have two (or more) reports, each of them reporting incomplete coverage but if merged together the coverage might be full. The merging seems like a challenge and would be probably hard on the level of HTML, so this would require using more low level API which is of course much harder. The default HTML report would be separate from the testing report, however this is not an issue and it might be even possible to add some links (with percentages) at proper places into the testing report.

Dependencies

Dependencies on other tests

The test runner needs to know if the dependencies are fulfilled, hence if the required modules and library tests were successful. So there must be a databases that keeps track of the test process. For example, if the raster library test fails, then all raster test will fail, such a case should be handled. The tests would need to specify the dependencies (there might be even more test dependencies then dependencies of the tested code).

Alternatively, we can ignore dependency issues. We can just let all the tests fail if dependency failed (without us checking that dependency) and this would be it. By tracking dependencies you just save time and you make the result more clear. Fail of one test in the library, or one test of a module does not mean that the test using it was using the broken feature, so it can be still successful (e.g. failed test vector library 3D capabilities and module accessing just 2D geometries). Also not all tests of dependent code have to use that dependency (e.g. particular interpolation method).

The simplest way to implement parallel dependency checking would be to have a file lock (e.g., ​Cross-platform API for flock-style file locking), so that only a single test runner has read and write access to the test status text file. Tests can run in parallel and have to wait until the file is unlocked. Consequently the test runner should not crash so that the file lock is always removed.

Anyway, dependency checking may be challenging if we allow parallel testing. Not allowing parallel testing makes the test status database really simple, it's a text file that will be parsed by the test runner for each test script execution and extended with a new entry at the end of the test run. Maybe at least the library test shouldn't be executed in parallel (something might be in the make system already).

Logs about the test state can be used to generate a simple test success/fail overview.

Dependencies of tested code

Modules such as G7:r.in.lidar (depends on libLAS) or G7:v.buffer (depends on GEOS) are not build if the dependencies are not fulfilled. It might be good to have some special indication that the dependency is missing. Or, this might be also leaved as task of test author who can implement special test function which will just check the presence of the module. Thus, the fact that the tests failed (probably) because of missing dependency would be visible in the test report.

Dependencies of testing code

The testing code could use some third party tools to compare results of GRASS to results of these tools. This of course might be very different set of dependencies than the GRASS itself code has. For example, we can compare result of some computation to result of a function available in ​SciPy. These test will probably be not allowed. If the tests would be allowed the most simple thing is probably check the dependencies in setUp or setUpClass methods. In case of import, it would need to be done once again in the actual test method (or we can assign the necessary imported classes to attributes).

# loading the dependency in setUp method and using it later
def setUp(self):
    # will raise import error which will cause error in test (as opposed to test failure)
    from scipy import something
    # save this for later user in test method(s)
    self.something = something

def test_some_test(self):
    a = ...
    b = self.something(params, ...)
    self.assertEqual(a, b, "The a result differs from the result of the function from scipy")

Reports from testing

Everything should go (primarily) to files and directories with some defined structure. Something would have to gather information from files and build some main pages and summary pages. The advantage of having everything in files is that it might be more robust and that it can easily run in parallel. However, gathering of information afterwards can be challenging. Files are really the only option how to integrate valgrind outputs.

There is TextTestRunner in unittest, the implementation will start from there. For now, the testing framework will focus on HTML output. However, the goal is something like GRASSTestRunner which could do multiple outputs simultaneously (in the future) namely HTML, XML (there might be some reusable XML schemes for testing results) and TXT (might be enriched by some reStructuredText or Markdown or really plain). Some (simple) text (summary) should go in to standard output in parallel to output to files. The problem with standard output is that functions and modules which we will use to prepare data or test results will be outputting to standard stdout and stderr and it would be a lot of work to catch and discard all this output.

It is not clear if the results should be organized by test functions (test_*) or only by test scripts (modules, test cases).

The structure of report will be based on the source code structure (something can be separate pages, something just page content):

• lib
  • gis
  • gmath
  • ... (other libraries)
• raster
  • r.slope.aspect
  • ... (other modules)
• vector
  • v.edit (testsuite directory is at this level)
    • test script/module
      • TestCase class
        • test_ function/method
          • standard output
          • error output
          • ... (other details)

There will be (at least one) top level summary page with percentages and links to subsections.

It is not clear how to deal with libraries with subdirectories (such as lib/vector/vedit/) and groups of modules (such as raster/simwe/). Will each module have separate tests? Will the common library be tested (by programs/modules in case of C/C++, by standard tests in case of Python)? The rule of thumb would be to put all directories with testsuite directory on the same level. This is robust for any level of nesting and for directories having testsuite directory together with subdirectories having their own testsuite directories. In this system, a testsuite directory would be the only element and its attribute is the original directory in the code where it was discovered. Any higher level pages in report would have reconstruct the structure from these attributes. The advantage is that we can introduce different reconstructions with different simplifications, for example we can have lib (except for lib/python), lib/python, vector, etc. or we can have just everything on the same level, or just a selection (raster modules or everything useful for vectors from lib, lib/python and both raster and vector modules). However, this does not mean that files for the report cannot be stored in the directory tree which will be a copy of directory tree where the test were found. This is quite straightforward way to store the path where the testsuite directory is from. The script preparing the report can then find all testsuite (or testsuite-result or testresult) directories in this new directory tree and handle the test results as described above.

In the directory with the test result, there will be subdirectories for each test script/module with subdirectories for TestCase classes with subdirectories for their test_ functions/methods. This most nested directory will contain all test details. The passed/failed test result can be stored here but it will be stored for sure stored on the more upper levels.

We can introduce a rule that each test script/module can contain only one TestCase class which would simplify the tree. However, this is probably not needed because both the implementation and the representation can deal with this easily.

A page for testsuite will be the "central" page of the report. There will be list (table) of all test scripts/modules, their TestCase classes, and their test_ functions/methods with basic info and links to details. Considering amount of details there will be probably a separate page for each test_ function/method.

Details for one test (not all have to be implemented):

• standard output and standard error output of tests
  • it might be hard to split if more than one module is called (same applies to functions)
  • might be good to connect them since they are sometimes synchronized (e.g., in case of G7:g.list)
• Valgrind output or output by another tool used for running a modules in test
  • might be from one or more modules
• the tested code
  • code itself with e.g., ​Pygments or links to Doxygen documentation
  • it might be unclear what code to actually include (you can see names of modules, function, you know in which directory test suite was)
• the testing code to see what exactly was tested and failed
• documentation of testing method and TestCase class (extracted docstrings)
• related commit/revision number or ticket number
  • in theory, each commit or closed ticket should have a test which proves that it works
  • this can be part of test method (test_*) docstring and can be extracted by testing framework
• pictures generated from maps for tests which were not successful (might be applied also to other types but this all is really a bonus)

Generally, the additional data can be linked or included directly (e.g. with some folding in HTML). This needs to be investigated.

Each test (or whatever is generating output) will generate an output file which will be possible to include directly in the final HTML report (by link or by including it into some bigger file). Test runner which is not influenced by fatal errors and segmentation faults has to take care of the (HTML) file generation. The summary pages will be probably done by some reporter or finalizer script. The output of one standalone test script (which can be invoked by itself) will have (nice) usable output (this can or even should be reused in the main report).

Comparing results

We must deal especially with GRASS specific files such as raster maps. We consider that comparison of simple things such as strings and individual numbers is already implemented by ​unittest.

Data types to be checked:

• raster map
  • composite? reclassified map?
  • color table included
• vector map
• 3D raster map
• color table
• SQL table
• file

Most of the outputs can be checked with different numerical precision.

We must have a simple guideline how to generate reference data. The output of G7:r.info for example is hardly comparable. It contains time stamps and user names that will differ for each test and platform. In addition the precision may be an issues between platforms for metadata values (min, max, coordinates). Hence the reference output must be parseable. One solution is, in case of metadata output (G7:r.info, G7:t.info, temporal modules in general), to require the output to be in shell format so that the reference check is able compare only these parts that contain values that will not change between test platforms. Alternatively we can use ​internal functions of doctest which are able compare texts where part of the text is ​ellipsis.

If we want to be able to test really all possible cases such as error outputs, it seems necessary to check that the error messages are saying what they should be saying, however this conflicts with the idea of testing under different locales (different locales would be still possible but the language would be always English). This might be solved by using regular expressions, doctest matching functions (with ellipses) or parsing just relevant part of the output.

Resources:

• ​http://grasswiki.osgeo.org/wiki/Test_Suite#Modules

Naming conventions

Methods with tests must start with test_ to be recognized by the ​unittest framework (with default setting but there is no reason to not keep this convention). This method is called test method.

The test methods testing one particular topic are in one test case (TestCase class child). From another point of view, one test case class contains all tests which requires the same setup and teardown.

Names of files with tests should not contain dots (except for the .py suffix) because the ​unittest.TestLoader.discover function requires that module names are importable (i.e. are valid Python identifiers). One file is a module in Python terminology, so we can say test module to denote a file with tests. However, we want these files to be executable, so this might lead also to the name test script.

A test suite is a group of test cases or more generally tests (test methods, test cases and other test suites in one or more files). A unittest's TestSuite class is defined similarly.

Name for directory with test is "testsuite". It also fits to how unittest is using this term (set of test cases and other test suites). "test" and "tests" is simpler and you can see it, for example ​in Python, but might be too general. "unittest" would confuse with the module unittest.

The preparation of the test is called setup or set up and the cleaning after the test is called teardown or tear down. There are setUp, setUpClass, tearDown and tearDownClass methods in TestCase class.

It is not clear how to call a custom script, class or function which will invoke the tests. Test runner would be appropriate but there is also TestRunner class in unittest package. Also, a report is a document or set of documents, however the class in unittest representing or creating the report is called TestResult.

The package with GRASS-specific testing functions and classes can be called gunittest (since it is based on unittest), gtestsuite (testsuite is reserved for the directory with tests), gtest, test, tests or grasstest. This package will be part of GRASS main package, so import will look like (using gtest):

from grass import gtest
from grass.gtest import compare_rasters

Layout of directories and files

Test scripts are located in a dedicated directory within module or library directories. All reference files or additionally needed data should be located there.

The same directory as tested would work well for one or two Python files but not for number of reference files. In case of C/C++ this would mean mixing Python and C/C++ files in one directory which is strange. One directory in root with separate tree is something which would not work either because tests are not close enough to actual code to be often run and extended when appropriate.

Invoking tests

Test scripts will work when directly executed from command line and when executed from the make system. When tests will executed by make system they might be executed by a dedicated "test_runner" Python script to set up the environment. However, the environment can be set up also inside the test script and not setting the environment would be the default (or other way around since setting up a different environment would be safer).

To actually have separate processes is necessary in any case because only this makes testing framework robust enough to handle (GRASS) fatal error calls and segmentation faults.

Tests should be executable by itself (i.e. they should have main() function) to encourage running them often. This can be used by the framework itself rather then imports because it will simplify parallelization and outputs needs to go to files anyway (because of size) and we will collect everything from the files afterwards (so it does not matter if we will use process calls or imports).

Example run

cd lib/python/temporal

Now there are two options to run the tests. First execution by hand in my current location:

cd testsuite
for test in `ls *.py` ; do
    python ${test} >> /tmp/tgis_lib.txt 2>&1
done

The test output will be written to stdout and stderr (piped to a file in this case).

Second option is an execution by the make system (still in lib/python/temporal):

make tests

or

make test

which might be more standard solution.

Testing on MS Windows

On Linux and all other unix-like systems we expect that the test will be done only when you also compile GRASS by yourself. This cannot be expected on MS Windows because of complexity of compilation and lack of MS Windows-based GRASS developers. Moreover, because of experience with different failures on different MS Windows computers (depending not only on system version but also system settings) we need to enable tests for as many users (computers) as possible. Thus, the goal is that we can get to the state where users will be able to test GRASS on MS Windows.

Basically, testing is Python, so it should run. We can use make system but also Python-based test discovery (our or unittest's). Invoking the test script on a MS Windows by hand and by make system may work too. Test will be executed in the source tree in the same way as on Linux. The problem might be a different layout of distribution directory and source code. Also some of the test will rely on special testing programs/modules which on Linux could be compiled on the fly while on MS Windows this could be a huge issue (see more below).

Libraries are tested through ctypes, modules as programs, and the rest is mostly Python, so this should work in any case. However, there are several library tests that are executable programs (usually GRASS modules), for example in gmath, gpde, raster3d. These modules will be executed by testing framework inside testing functions (test_*). These modules are not compiled by default and are not part of the distribution. They need to be compiled in order to run the test. We can compile additional modules and put them to one separate directory in distribution, or we can have debug distribution with testing framework and these modules, or we can create a similar system as we have for addons (on MS Windows) to distribute these binaries. The modules could be compiled a prepared on server for download and they would be downloaded by testing framework or upon user request. The more complex think could be standard modules with some special option (e.g., --test not managed by GRASS parser) which would be available only with some #define TEST and they would not be compiled like this by default.

Locations, mapsets and data

The test scripts should not depend on specific mapsets for their run. In case of make system run, every test script will be executed in its own temporary mapset. Probably location will be copied for each test (testsuite) to keep the location clean and allow multiprocessing. In case of running by hand directly without make, tests will be executed in the current location and mapset which will allow users to test with their own data and projections.

Tests (suites, cases, or scripts/modules) itself can define in which location or locations they should be executed as a global variable (both for unit and doctests, doctests in they their unittest wrapper). The global variable will be ignored in case the test script is executed by hand. When executed by make system, the test can be executed in all locations specified by the global variable, or one location specified by make system (or generally forced from the top). In other words, the global variable specifies what the test want to do, not what it is capable to do because we want it to run in any location.

We should have dedicated test locations with different projections and identical map names. I wouldn't use the GRASS sample locations (NC, Spearfish) as test locations directly. We should have dedicated test locations with selected data. They can overlap with (let's say) NC but may contain less imagery but on the other hand some additional strange data. The complication are doctests which are documentation, so as a consequence they should use the intersection of NC sample and testing location. The only difference between the locations would be the projection, so it really makes difference only for projected, latlon and perhaps XY.

All data should be in PERMANENT mapset. The reason is that on the fly generated temporary mapsets will have only access to the PERMANENT mapset by default. Access to other mapsets would have to be explicitly set. This might be the case when user wants to use his or her own mapset. On the other hand, it might be advantageous to have maps in different mapsets and just allow the access to all these mapsets. User would have to do the same and would have to keep the same mapset structure (which might not be so advantageous) which is just slightly more complex then keep the same map names (which user must do in any case).

If multiple locations are allowed and we expect some maps being in the location such as elevation raster, it is not clear how to actually test the result such as aspect computed from elevation since the result (such as MD5 sum) will be different for each location/projection. This would mean that the checking/assert functions or tests themselves would have to handle different locations and moreover, this type of tests would always fail in the user provided location.

It is expected that any needed (geo-)data is located in the PERMANENT mapset when a specific location is requested. This applies for prepared locations. For running in the user location, it is not a requirement.

The created data will be deleted at the end of the test. The newly created mapset will be deleted (if it was created). If we would copy the whole location (advantageous e.g. for temporal things), the whole location will be deleted. The tests will be probably not required to delete all the created maps but they might be required to delete other files (or e.g., tables in database) if they created some.

User should be able to disable the removal of created data to be able to inspect them. This would be particularly advantageous for running tests by hand in some user specified location.

In case the mapset information is needed, then g.mapset -p (prints the current mapset) must be parsed within the test. An special case, a test of g.mapset -p itself will create a new mapset and then switch to the new one and test there.

Testing framework can have a function to check/test the current location (currently accessible mapsets) whether it contains all the required maps (according to their name).

All reference files (and perhaps also additional data) will be located in the testsuite directory. There can be also one global directory with additional data (e.g. data to import) which will be shared between test suites and exposed by the testing framework.

The reference checking in case of different locations (projections) can only be solved in the test itself. The test author has to implement a conditioned reference check. Alternatively, a function (e.g., def pick_the_right_reference(general_reference_name, location_name)) could be implemented to help with getting the right reference file (or perhaps value) because some naming conventions for reference files will be introduced anyway.

Testing framework design should allow us to make different decisions about how to solve data and locations questions.

Testing data will be available on server for download. The testing framework can download them if test is requested by user. The data can be saved in the user home directory and used next time. This may simplify things for users and also it will be clear for testing framework where to find testing data.

GSoC weekly reports

Week 01

1. What did you get done this week?

I discussed the design and implementation with mentor during a week. The result of discussions is on project wiki page (this page, link to version). I will add more in the next days.

The code will be probably placed in ​GRASS sandbox repository (​HTML browser), later it will be hopefully moved to ​GRASS trunk (​HTML browser). However, the discussion about where to put GSoC source code is still open (​gsoc preferred source location, ​at nabble).

2. What do you plan on doing next week?

I plan to implement some basic prototype of (part of) testing framework to see how the suggested design would look like in practice and if it needs further refinement. So far it is how I have it in my schedule.

3. Are you blocked on anything?

It is not clear to me how certain things in testing framework will work on MS Windows. I will discuss this later on the wiki page and ​grass-dev.

​report email

Week 02

1. What did you get done this week?

The original plan for this week was to do a basic implementation of testing framework. However, I decided to start from the implementation available in Python unittest. This allowed me to continue in work on design implementation problems. I also studied the unittest implementation to understand what are the places which will be different in GRASS testing framework.

2. What do you plan on doing next week?

The plan is to test unittest and doctest methods for evaluation and comparison of textual and numerical outputs (results) on GRASS use cases.

3. Are you blocked on anything?

During next week, I (we) will need to make a decision if the tests will be limited to English language environment (or even English locales) because this can influence significant part of textual output comparisons.

​report email