- 1 Overview
- 2 When to run the regression suite and when to add new tests
- 3 Where the regression test is run
- 4 How to run the regression tests manually
- 5 Accessing regression test results
- 6 How regression testing works
- 7 Filtering Image Differences
- 8 Tips on writing regression tests
- 9 What is tested
- 10 Rebaselining Test Results
- 11 Troubleshooting
- 12 Skeleton for future content
- 13 Compiler Warning Regression Testing
VisIt has a large and continually growing test suite. VisIt's test suite involves a combination of shell scripts and python scripts in trunk/test, raw data and data generation sources in trunk/data and of course the VisIt sources themselves. Regression tests are run on a nightly basis.
When to run the regression suite and when to add new tests
The current development policy is that testing is not necessary prior to an update of changes to the code. Developers are free to update files as long as they are also committed to fixing any tests that fail on the night following their updates. This policy may be revisited as more developers get involved as it may get increasingly difficult to disambiguate failures in a given night's test run caused by the possibly numerous updates in the preceding day. Nonetheless, developers do often, as a matter of course, run a portion of the test suite prior to updating the code just as a sanity check. Again, this is not necessary but sometimes recommended.
Another recommended though not strictly enforced practice is to add tests as functionality is added. For example, if a developer adds a new database plugin, then it is appropriate to also add some tests for that plugin. Adding tests and running the test suite is described below.
Where the regression test is run
The regression suite is run on LLNL's pascal machine. Pascal runs the TOSS3 operating system, which is a flavor of Linux. If you are going to run the regression suite yourself you should run on a similar system or there will be differences due to numeric precision issues.
The regression suite is run on Pascal using a cron job that checks out VisIt source code, builds it, and then runs the tests.
How to run the regression tests manually
The regression suite relies on having a working VisIt build and test data installed on your local computer. The test suite exists in the visit/src/test directory and is invoked by a script called runtest. To obtain a full help listing, run:
cd visit/src/test ./runtest -h
Since the directory reorganization that accompanied the change to using GitHub, the runtest script needs to know 3 things. First, the baseline directory where various tests store their image and text baselines must be provided. This can be given using relative paths using the -b argument. Next, the test suite needs to know where to locate data files. Use the -d argument for this. Next, the test suite needs to know where to find the visit script. The path may vary depending on whether an out of source build was used. Finally, the test suite can be passed an optional list of test script names to execute. Without specific test scripts, the test suite will attempt to run all tests.
cd visit/src/test ./runtest -b ../test/baseline -d ../../data -e /path/to/bin/visit [filenames]
Once the test suite has run, the results can be found in an html directory. Open html/index.html in a web browser to view the test suite results.
Accessing regression test results
You may access the nightly test suite results by accessing this site: http://portal.nersc.gov/project/visit/ .
In the event of failure on the nightly run
If any tests fail, all developers who updated the code from the last time all tests successfully passed will receive an email indicating what failed. In addition, failed results should be available on the web.
All logged tests are available at:
If the results fail to post, the visit group on NERSC's systems may be over quota. If you have a NERSC account you can check usage by sshing to NERSC and running the following command:
How regression testing works
The workhorse script that manages the testing is 'runtest' in trunk/test. Tests can be run in a variety of ways called *modes*. For example, VisIt's nightly testing is run in serial, parallel and scalable,parallel modes. At one time, we also ran in a purify mode (and in the future we will run in a valgrind mode). On weekends, we ran in a dynamic load balance mode (dlb) and HDF5 mode. Each of these modes represents a fundamental and relatively global change in the way VisIt is doing business under the covers during its testing. For example, the difference between parallel and scalable,parallel modes is whether the scalable renderer is being used to render images. In the parallel mode, rendering is done in the viewer. In scalable,parallel mode, it is done, in parallel, on the engine and images from each processor are compsited. Typically, the entire test suite is run in each mode specified by the regression test policy.
There are a number of command-line options to the 'runtest' script. 'runtest -help' will give some information about these options. Until we are able to get re-baselined on the systems available outside of LLNL firewalls, options enabling some filtering of image differences will be very useful. Use of these options on platforms other than the currently adopted testing platform (edge.llnl.gov) will facilitate filtering big differences (and probably real bugs that have been introduced) from differences due to platform where tests are run. See the section on filtering image differences.
There are a number of different categories of tests. The test categories are the names of all the directories under trunk/test/tests. The .py files in this directory tree are all the actual test driver files that drive VisIt's CLI and generate images and text to compare with baselines. In addition, the trunk/test/Testing.py file defines a number of helper Python functions that facilitate testing including two key functions; Test() for testing image outputs and TestText() for testing text outputs. Of course, all the .py files in trunk/test/tests subtree are excellent examples of test scripts.
When runtest finishes, it will have created a web-browseable HTML tree in the html directory. The actual image and text raw results will be in the current directory and difference images will be in the diff directory. The difference images are essentially binary bitmaps of the pixels that are different and not the actual pixel differences themselves. This is to facilitate identifying the location and cause of the differences.
Adding a test involves a) adding a .py file to the appropriate subdirectory in trunk/test/tests, b) adding the expected baselines to trunk/test/baseline and, depending on the test, c) adding any necessary input data files to trunk/data. Runtest will find your added .py files the next time it runs. So, you don't have to do anything special other than adding the .py file.
One subtlety about the current test modality is what we call 'mode specific baselines.' In theory, it should not matter what mode VisIt is run in to produce an image. The image should be identical across modes. In practice there is a long list of things that can contribute to a handful of pixel differences in the same test images run in different modes. This has lead to mode specific baselines. In the baseline directory, there are subdirectories with names corresponding to modes we currently run. When it becomes necessary to add a mode specific basline, the baseline file should be added to the appropriate baseline subdirectory.
In some cases, we skip a test in one mode but not in others. Or, we temporarily disable a test by skipping it until a given problem in the code is resolved. This is handled by the '-skip' argument to run test. Ordinarily, a list of the tests we currently skip is maintained and updated as necessary. Until we complete the transition to nersc, skipping of tests and managing the 'skip list' shall be deferred.
Filtering Image Differences
There are many ways of both compiling and running VisIt to produce image and textual outputs. In many cases, we expect the image or textual outputs to be about the same (though not always bit-wise exact matches) even if the manner in which they are generated varies dramatically. For example, we expect VisIt running on two different implementations of the GL library to produce by and large the same images. Or, we expect VisIt running in serial or parallel to produce the same images. Or we expect VisIt running on Ubuntu Linux to produce the same images as it would running on Mac OSX. We expect and therefore wish to ignore minor variations. But, we want to be alerted to major variations. So when any developer runs a test, we require some means of filtering out image differences we expect from those we are not expecting.
On the other hand, as we make changes to VisIt source code, we may either expect or not expect image outputs for specific testing scenarios to change in either minor or dramatic ways. For example, if we fix a bug leading to a serious image artifact that just happened to be overlooked when the original baseline image was committed, we could improve the image dramatically implying a large image difference and still expect such a difference. For example, maybe the Mesh plot had a bug where it doesn't obey the Mesh line color setting. If we fix that bug, the mesh line color will likely change dramatically. But, the resultant image is expected to change too. Therefore, have a set of baselines from which we compute exact differences is also important in tracking impact of code changes on VisIt behavior.
These two goals, running VisIt tests to confirm correct behavior in a wide variety of conditions where we expect minor but not major variations in outputs and running VisIt tests to confirm behavior as code is changed where we may or may not expect minor or major variations are somewhat complimentary.
It may make sense for developers to generate (though not ever commit) a complete and valid set of baselines on their target development platform and then use those (uncommitted) baselines to enable them to run tests and track code changes using an exact match methodology.
In this section, we describe the image difference we compute, their meaning and the command-line options that control them. Note that in order to utilize the human perceptual metrics, the python color-math library (http://code.google.com/p/python-colormath/) must be available.
|Metric||What it means|
|total pixels||count of all pixels in the test image|
|plot pixels||count of all pixels touched by plot(s) in the test image|
|coverage||percent of all pixels that are plot pixels (plot pixels / total pixels). Test images in which plots occupy a small portion of the total image are fraught with peril and should be avoided to begin with. Images with poor coverage are more likely to produce false positives (e.g. passes that should have failed) or to exhibit somewhat random differences as test scenario is varied.|
|dmax / dmaxp||maximum raw numerical / human perceptual difference in any color (R,G or B) channel or intensity (average of R, G, B colors). A good first try in filtering image differences is a dmax setting of 1. That will admit variations of 1 in any R, G or B channel or in intensity. However, for line-based plots like the mesh plot, due to differences in the way lines of the plot get scanned into pixels, this metric can fail miserably.|
|dmed / dmedp||median value of raw numerical / human perceptual differences over all color channels and intensity|
When running the test suite on platforms other than the currently adopted baseline platform or when running tests in modes other than the standard modes, a couple of options will be very useful; '-pixdiff' and '-avgdiff'. The pixdiff option allows one to specify a tolerance on the percentage of *non*background* pixels that are different. The avgdiff option allows one to specify a second tolerance for the case when the pixdiff tolerance is exceeded. The avgdiff option specifies the maximum average (intensity) difference difference allowed averaged over all pixels that are different.
Tips on writing regression tests
- Except in cases where annotations are being specifically tested, remember to call TurnOffAllAnnotations() as one of the first actions in your test script. Otherwise, you can wind up producing images containing machine-specific annotations which will produce differences on other platforms.
- When writing tests involving text differences and file pathnames, be sure that all pathnames in the text strings passed to TestText() are absolute. Internally, VisIt testing system will filter these out and replace the machine-specific part of the path with 'VISIT_TOP_DIR' to facilitate comparison with baseline text. In fact, the .txt files that get generated in the 'current' dir will have been filtered and all pathnames modified to have VISIT_TOP_DIR in them.
- Here is a table of python tests scripts which serve as examples of some interesting and lesser known VisIt/Python scripting practices:
|Script||What it demonstrates|
|tests/faulttolerant/savewindow.py||- uses python exceptions|
|tests/databases/itaps.py||- uses OpenDatabase with specific plugin
- uses SIL restriction via names of sets
|tests/databases/silo.py||- uses OpenDatabase with virtual database and a specific timestep|
|tests/rendering/scalable.py||- uses OpenComputeEngine to launch a parallel engine|
|tests/rendering/offscreensave.py||- uses Test() with alternate save window options|
|tests/databases/xform_precision.py:||- uses test-specific enviornment variable settings|
What is tested
Presently, the GUI is not tested. Testing exercises the viewer, mdserver, engine and cli but not the GUI.
Rebaselining Test Results
A python script, rebase.py, at the top of the tests dir tree can be used to rebaseline large numbers of results. In particular, this script enables a developer to rebase test results without requiring access to the test platform where testing is performed. This is becase the PNG files uploaded (e.g. posted) to VisIt's test results dashboard are suitable for using as baseline results. To use this script, run ./rebase.py --help. The output is repeated below for convenience...
Usage: rebase.py args [test-file1 test-file2 ...] where args specify the category, test .py filename, mode and date tag (of the posted html results) and test-file1, etc. are either the names or file glob(s) of tests to rebaseline. If no files or file globs are specified then all results from the specified test .py file are rebased. Note that if you choose to re-baseline a whole series of files which may include skips or actual passes, then it will discover there are no *current* results posted for those cases and then simply take the already existing baseline result. Sometimes, its easiest to use rebase.py on a whole series and then selectively revert the ones you didn't want to rebase prior to committing them. Note: This will NOT HANDLE rebaselining of files in mode-specific Examples... To rebaseline *all* files from oldsilo test from date tag 2018-04-07-09:12 ./rebase.py -c databases -p oldsilo -m serial -d '2018-04-07-09:12' To rebaseline silo_00.png & silo_01.png files from oldsilo test from same tag ./rebase.py -c databases -p oldsilo -m serial -d '2018-04-07-09:12' 'oldsilo_0[0-1].png' To be interactively prompted upon each file to rebaseline from oldsilo test ./rebase.py -c databases -p oldsilo -m serial -d '2018-04-07-09:12' --prompt Options: -h, --help show this help message and exit -c CATEGORY, --category=CATEGORY [Required] Specify test category -p PYFILE, --pyfile=PYFILE [Required] Specify test py filename without the .py extension -m MODE, --mode=MODE [Required] Specify test mode -d DATETAG, --datetag=DATETAG [Required] Specify the VisIt test result date tag (e.g. '2018-04-07-09:12') from which to draw new baselines --prompt [Optional] Prompt before copying each file
Once you've completed using rebase.py to update image baselines, don't forget to commit your changes back to the repository.
Mesa stub issue
IMPORTANT NOTE: After the cmake transition, there is no mesa-stub issue because the viewer does not compile in a stub for mesa since doing so was non-portable. Thus, if you are using the svn trunk version of VisIt, you cannot run into this issue. This section is being preserved for 1.12.x versions of VisIt.
If all of your tests fail, you have likely run into the Mesa stub issue. The regression suite is set up to do "screen captures", but default VisIt cannot do screen captures in "-nowin" mode. If you run a test with the "-verbose" command and see:
Rendering window 1... VisIt: Message - Rendering window 1... VisIt: Warning - Currently, you cannot save images when in nowin mode using screen capture and Mesa has been stubbed out in the viewer. Either disable screen capture, or rebuild without the Mesa stub library. Note that the Mesa stub library was in place to prevent compatibility problems with some graphics drivers. Saving window 1...
then you have gotten bit by this problem.
You can correct it by running configure with:
In fact, the typical configure line on davinci is:
./configure CXXFLAGS=-g MAKE=gmake --enable-parallel --enable-visitmodule --enable-viewer-mesa-stub=no --enable-buildall
IMPORTANT NOTE: this will not automatically touch the files that need to be recompiled. Your best bet is to touch viewer/main/*.C and recompile that directory.
You can test the Mesa stub issue with:
% visit -cli -nowin >>> sw = SaveWindowAttributes() >>> sw.screenCapture = 1 >>> SetSaveWindowAttributes(sw) >>> SaveWindow()
If VisIt complains about an empty window, you do *not* have a Mesa stub issue and you *can* run regression tests. If it complain about Mesa stubs, then you *do* have the issue and you *can't* run regression tests.
PIL on MacOS X
If you attempt to execute runtest and it gives errors indicating that it assumed the test crashed then you might have problems with your PIL installation. These manifest as an error with text like "The _imaging C module is not installed", which can be obtained if you add the -v argument to runtest.
PIL, as installed by build_visit, can pick up an invalid jpeg library on certain systems. If you run python -v and then try to import _imaging then Python will print out the reason that the library failed to import. This can often be due to missing jpeg library symbols. It is also possible to observe this situation even when libjpeg is available in /sw/lib but is compiled for a different target architecture (e.g. not x86_64) that what build_visit is using. The effect of this is that when _imaging.so library is linked, there is an error message saying saying something like...
ld: warning: ignoring file /opt/local/lib/libz.dylib, file was built for x86_64 which is not the architecture being linked (i386): /opt/local/lib/libz.dylib ld: warning: ignoring file /sw/lib/libjpeg.dylib, file was built for i386 which is not the architecture being linked (x86_64): /sw/lib/libjpeg.dylib
. Later, when Python trys to import _imaging module, the dlopen fails due to unresolved jpeg symbol. Either way, the best solution the following:
- Build your own jpeg library
- Edit PIL's setup.py, setting JPEG_ROOT=libinclude("/path/to/my/jpeg")
- python ./setup.py build
- Look through the console output for the command that links the _imaging.so library and paste it back into the console as a new command. Edit the command so it uses /path/to/my/jpeg/lib/libjpeg.a instead of the usual -L/path -ljpeg business so it really picks up your jpeg library.
- python ./setup.py install
That is a painful process to be sure but it should be enough to produce a working PIL on Mac.
Here is a slightly easier way that I (Cyrus) was able to get PIL working on OSX:
- Build your own jpeg library
- Edit PIL's setup.py, do not modify JPEG_ROOT, instead directly edit the darwin case:
elif sys.platform == "darwin": add_directory(library_dirs, "/path/to/your/jpeg/v8/i386-apple-darwin10_gcc-4.2/lib") add_directory(include_dirs, "/path/to/your/jpeg/v8/i386-apple-darwin10_gcc-4.2/include") # attempt to make sure we pick freetype2 over other versions add_directory(include_dirs, "/sw/include/freetype2")
- python setup.py build
- python setup.py install
Skeleton for future content
Mode specific baselines
Compiler Warning Regression Testing
The ultimate aim of compiler warning testing is to improve the quality of the code by averting would-be problems. However, in the presence of an already robust, run-time test suite, compiler warnings more often than not alert us to potential problems and not necessarily any real bugs that manifest for users.
Totally eliminating compiler warnings is a good goal. But, it is important to keep in mind that that goal is really only indirectly related to improving code quality. Its also important to keep in mind that all warnings are not equal nor are all compilers equal to the task of detecting and reporting them. For example, an unused variable warning in a code block may be a potential code maintenance nuisance but will not in any way manifest as a bug for a user.
As developers, when we fix warnings we typically take action by adjusting code. But, we are doing so in response to one compiler's (often myopic) view of the code and typically not to any real bug encountered by a user. We need to take care the the adjustments we make lead to improved quality. In particular, adjusting code for no other purpose except to silence a given compiler warning seems an unproductive exercise. Besides, there are many other options for managing unhelpful compiler warnings apart from adjusting actual code.
Finally, we're introducing compiler warning checking into a code that has been developed for many years by many developers without having payed significant attention to this issue. As of this writing, the existing code generates thousands of warnings. To make matters worse, we are dialing up compiler options to report as many warnings as possible. This leads to two somewhat distinct problems. One is to resolve warning issues in the existing code. The other, and the more important long term goal, is to prevent further warning issues from being introduced into the code.
If we take the appraoch that we must achieve the first before we can start on the second, we wind up holding our long term goal hostage to the laborious and resource intensive task of addressing existing warning issues. Or, we hold a gun to everyone's head to drop whatever they are doing and spend time addressing existing warnings to eliminate noise from useful warnings.
But, we don't have to do either of these. Instead, we can add logic to our regression testing framework to detect the introduction of new warning issues apart from existing warnings and then only fail the test when new warnings are introduced.
Here's how it works. A new unit test was added, test/tests/unit/compiler_warnings.py. That test checks for the existence of a file make.err just above the src, test and data dirs (thats because thats where the regressiontest_edge shell script puts it). If ../make.err is not found, the test immediately exits with the skip error code indication. It is assumed that ../make.err was produced from the current source code with compiler warnings dialed up (e.g. -Wall -Wextra -pedantic) and stderr output from an entire clean build of the source is captured with a version of make supporing the --output-sync=lines option (or make was not run with a -j option).
The compiler_warnings.py python script examines make.err for lines containing warning. For each source file that produces a warning, a count of all warnings produced by the file is computed. A text string result suitable for input to the TestText method of VisIt's regression testing framework is assembled. Source filenames are sorted and then emitted along with their warning counts. The resulting text string is also a JSON string. It is this single text result that is checked for changes. Note that any changes, up or down, in compiler warning counts for any source file, as well as introduction or elimination of a source file from compiler warning list, will result in a test failure.
If enough files were changed in the previous day's work, it's conceivable changes from multiple developer's commits will result in changes (some improvements and some not) to various lines of this text output. Improvements should be re-baselined. Non-improvements should be checked and fixed.
To re-basline the warning count for a given source file, simply edit the compiler_warnings_by_file.txt file as appropriate. Its structure is designed for easy editing with any text editor.
To fix a new warning, there are several options. The first is to adjust the code that generated the warning. Its probably something minor and probably should be fixed. However, if the warning is itself unhelpful and fixing it will not improve the code, you can add the warning to a skip list. There is a file, compiler_warning_skips.json which contains skips for specific source files and skips for all (e.g. global) source files. This json file is read in as a python dictionary. You can simply cut the text for the warning that gets posted in the html to this file. Finally, as a last resort, you can also elect to bump up the warning count for the given source file. But, these later actions should be taken with care and perhaps vetted with other developers first.