TSTL is a "little" language that makes it easy to test software. This implementation targets Python. There is also a (beta) Java version, at https://github.com/flipturnapps/TSTL-Java.
TSTL produces a simple, universal interface for test generators to use -- it essentially turns a definition of valid tests into a graph with transitions, backtrack, replay, automatic reduction, and code coverage support.
Some of you may be asking: "How does TSTL differ from the Hypothesis
https://hypothesis.readthedocs.io/en/latest/ testing tool?" There are a few
answers. First, TSTL is probably much less polished than Hypothesis,
right now! More importantly, however, Hypothesis and TSTL both
generate tests, but they are primarily intended to generate different
kinds of tests. Hypothesis is in what we consider the QuickCheck
family: if you have a function f that takes as input a list, a
string, or something more complex, Hypothesis is very likely what you
want to use. If you have a set of functions, f, g, and h, and
they don't just return things, but modify invisible system state (but
also return things that may be inputs to other functions), you may
want TSTL. You can do state-based sequence-of-method-calls testing
with Hypothesis, but it may be easier with TSTL, and it's what TSTL is
built for. So, if you're testing a sorting implementation, Hypothesis
is almost certainly much better. If you're testing something like a
file system, you might want to look into TSTL. If you're testing a
parser that takes a string as input, both tools might be useful,
depending on your situation.
The similarity is that both TSTL and Hypothesis don't look like traditional unit testing. They instead let you define the idea of a valid input (either some data values, or in TSTL a sequence of method calls and assignments that more resembles a traditional do-some-stuff-and-then-check-it unit test) and assert general properties about the behavior of a system under valid input.
For more details on TSTL, the best starting point is a comprehensive journal paper in STTT: https://github.com/agroce/tstl/blob/master/doc/papers/STTTjournal/maintstl.pdf. There are also NASA Formal Methods (NFM) and International Symposium on Software Testing and Analsysis (ISSTA) 2015 papers at http://www.cs.cmu.edu/~agroce/nfm15.pdf and http://www.cs.cmu.edu/~agroce/issta15.pdf, with some implementation details or concepts that are not present in the more up-to-date and complete paper.
The NFM and ISSTA papers use an early version of TSTL syntax, which marks pools and TSTL constructs with % signs. "Modern" TSTL uses <> by default, though if for some reason you need <> in your code (and to prepare for a future C++ version) this can be turned off and only % supported.
Note that documentation below is preliminary. A better guide to usage might be to examine the examples directory and see real TSTL test harnesses. The generators directory includes some simple but useful testing tools for finding bugs in systems defined in TSTL. Only the random tester is extensively tested and supports all TSTL features well at this point. Reading src/randomtester.py provides considerable guidance in how to (efficiently) use TSTL in a generic testing tool, with TSTL providing an interface to the underlying application/library to be tested.
git clone https://github.com/agroce/tstl.git
cd tstl
python setup.py install
# Might need to do a sudo on the last step if not using virtualenv
Note that TSTL expects a Python 2.7 version, not Python 3.0. Eventually we'll release a 3.0+ version, but right now the Python center-of-mass seems to still be in 2.7. Also, you probably want to install (pip will do the trick) Ned Batchelder's coverage.py (https://coverage.readthedocs.io).
The documentation below is preliminary, generated by some early adapters of TSTL, and may not perfectly match the current version. Use at your own risk. Code in examples directories should be up to date.
TSTL installs a few standard tools: the TSTL compiler itself, tstl (technically all
you really need to do something useful); a random tester generator
tstl_rt; a tool for producing standalone tests, tstl_standalone;
a tool for replaying TSTL test files tstl_replay; a tool for
delta-debugging and normalization of TSTL tests, tstl_reduce; and a tool for running a set of tests as a regression, tstl_regress. The
tstl_reduce tool is not essential, since the random tester performs
delta debugging and alpha conversion by default (you have to add
--normalize to also get normalization of tests, however).
The simplest usage is to go to a directory with a .tstl file, and type:
tstl mytstlfile.tstl
tstl_rt
The first line compiles mytstlfile.tstl and produces sut.py, which the random tester will automatically import and test. Both tstl and the random tester take a variety of command line options, which --help will show.
The easiest way to understand TSTL may be to examine examples/AVL/avlnew.tstl (https://github.com/agroce/tstl/blob/master/examples/AVL/avlnew.tstl), which is a simple example file in the latest language format (easier to read) (the file avlblocks.tstl has this same harness in a different format).
These are pretty full-featured testers for an AVL tree class. You can write something very quick and fairly effective with just a few lines of code, however:
@import avl
pool: <int> 3
pool: <avl> 2
property: <avl>.check_balanced()
<int> := <[1..20]>
<avl> := avl.AVLTree()
<avl>.insert(<int>)
<avl>.delete(<int>)
<avl>.find(<int>)
<avl>.display()
This says that there are two kinds of "things" involved in our
AVL tree implementation testing: int and avl. We define (in
Python, almost) how to create these things, and what we can do with
these things, and then TSTL produces sequences that match our
definition. It also checks that all AVL trees, at all times, are
properly balanced. If we wanted, as in avlnew.tstl, we could also
make sure that our AVL tree "acts like" a set --- when we insert
something, we can find that thing, and when we delete something, we
can no longer find it.
If we test this (or avlnew.tstl) for 30 seconds, something like this will appear:
~/tstl/examples/AVL$ tstl_rt --timeout 30
Random testing using config=Config(swarmSwitch=None, verbose=False, fastQuickAnalysis=False, failedLogging=None, maxtests=-1, greedyStutter=False, exploit=None, seed=None, generalize=False, localize=False, uncaught=False, speed='FAST', internal=False, normalize=False, highLowSwarm=None, replayable=False, essentials=False, quickTests=False, coverfile='coverage.out', uniqueValuesAnalysis=False, swarm=False, ignoreprops=False, total=False, swarmLength=None, noreassign=False, profile=False, full=False, multiple=False, relax=False, swarmP=0.5, stutter=None, running=False, compareFails=False, nocover=False, swarmProbs=None, gendepth=None, quickAnalysis=False, exploitCeiling=0.1, logging=None, html=None, keep=False, depth=100, throughput=False, timeout=30, output=None, markov=None, startExploit=0)
12 [2:0]
-- < 2 [1:0]
---- < 1 [0:0] L
---- > 5 [0:0] L
-- > 13 [1:-1]
---- > 14 [0:0] L
set([1, 2, 5, 12, 13, 14])
...
11 [2:0]
-- < 5 [1:0]
---- < 1 [0:0] L
---- > 9 [0:0] L
-- > 14 [1:-1]
---- > 18 [0:0] L
set([1, 5, 9, 11, 14, 18])
STOPPING TEST DUE TO TIMEOUT, TERMINATED AT LENGTH 17
STOPPING TESTING DUE TO TIMEOUT
80.8306709265 PERCENT COVERED
30.0417540073 TOTAL RUNTIME
236 EXECUTED
23517 TOTAL TEST OPERATIONS
10.3524413109 TIME SPENT EXECUTING TEST OPERATIONS
0.751145362854 TIME SPENT EVALUATING GUARDS AND CHOOSING ACTIONS
18.4323685169 TIME SPENT CHECKING PROPERTIES
28.7848098278 TOTAL TIME SPENT RUNNING SUT
0.179262161255 TIME SPENT RESTARTING
0.0 TIME SPENT REDUCING TEST CASES
224 BRANCHES COVERED
166 STATEMENTS COVERED
For many (but not all!) programs, a more powerful alternative to simple random testing is to use swarm testing, which restricts the actions in each individual test (e.g., insert but no delete, or find but no inorder traversals) (see http://www.cs.cmu.edu/~agroce/issta12.pdf).
~/tstl/examples/AVL$ tstl_rt --timeout 30 --swarm
Random testing using config=Config(swarmSwitch=None, verbose=False, fastQuickAnalysis=False, failedLogging=None, maxtests=-1, greedyStutter=False, exploit=None, seed=None, generalize=False, localize=False, uncaught=False, speed='FAST', internal=False, normalize=False, highLowSwarm=None, replayable=False, essentials=False, quickTests=False, coverfile='coverage.out', uniqueValuesAnalysis=False, swarm=True, ignoreprops=False, total=False, swarmLength=None, noreassign=False, profile=False, full=False, multiple=False, relax=False, swarmP=0.5, stutter=None, running=False, compareFails=False, nocover=False, swarmProbs=None, gendepth=None, quickAnalysis=False, exploitCeiling=0.1, logging=None, html=None, keep=False, depth=100, throughput=False, timeout=30, output=None, markov=None, startExploit=0)
11 [2:0]
-- < 7 [1:0]
...
STOPPING TEST DUE TO TIMEOUT, TERMINATED AT LENGTH 94
224 BRANCHES COVERED
166 STATEMENTS COVERED
Here, the method is not very important; simple random testing does a
decent job covering the AVL tree code in just 60 seconds. If we
introduce a bug by removing the self.rebalance() call on line 205 of
avl.py, either method will quickly report a failing test case,
automatically reduced. By default, the random tester will run the test
in a verbose mode to show in more detail what happens during the execution
that causes a failure.
~/tstl/examples/AVL$ tstl_rt --timeout 30
Random testing using config=Config(swarmSwitch=None, verbose=False, fastQuickAnalysis=False, failedLogging=None, maxtests=-1, greedyStutter=False, exploit=None, seed=None, generalize=False, localize=False, uncaught=False, speed='FAST', uniqueValuesAnalysis=False, normalize=False, silentFail=False, noAlphaConvert=False, replayable=False, essentials=False, quickTests=False, coverfile='coverage.out', swarm=False, internal=False, total=False, progress=False, swarmLength=None, noreassign=False, profile=False, full=False, multiple=False, timedProgress=30, relax=False, swarmP=0.5, stutter=None, highLowSwarm=None, readQuick=False, verboseActions=False, running=False, ignoreProps=False, compareFails=False, nocover=False, swarmProbs=None, gendepth=None, quickAnalysis=False, exploitCeiling=0.1, computeFeatureStats=False, logging=None, html=None, keep=False, noExceptionMatch=False, depth=100, showActions=False, throughput=False, timeout=30, output='failure.26816.test', markov=None, startExploit=0)
11 [2:0]
-- < 8 [1:0]
---- < 4 [0:0] L
---- > 9 [0:0] L
-- > 18 [1:1]
---- < 15 [0:0] L
set([4, 8, 9, 11, 15, 18])
PROPERLY VIOLATION
ERROR: (<type 'exceptions.AssertionError'>, AssertionError(), <traceback object at 0x1032bf4d0>)
TRACEBACK:
File "/Users/alex/tstl/examples/AVL/sut.py", line 7960, in check
assert self.p_avl[0].check_balanced()
Original test has 98 steps
REDUCING...
Failed to reduce, increasing granularity to 4
Reduced test length to 73
Failed to reduce, increasing granularity to 4
Reduced test length to 55
Failed to reduce, increasing granularity to 4
Reduced test length to 41
Failed to reduce, increasing granularity to 4
Reduced test length to 31
Failed to reduce, increasing granularity to 4
Reduced test length to 24
Failed to reduce, increasing granularity to 4
Failed to reduce, increasing granularity to 8
Reduced test length to 20
Failed to reduce, increasing granularity to 4
Failed to reduce, increasing granularity to 8
Reduced test length to 17
Failed to reduce, increasing granularity to 4
Failed to reduce, increasing granularity to 8
Reduced test length to 14
Failed to reduce, increasing granularity to 4
Failed to reduce, increasing granularity to 8
Reduced test length to 13
Failed to reduce, increasing granularity to 4
Failed to reduce, increasing granularity to 8
Reduced test length to 11
Failed to reduce, increasing granularity to 4
Failed to reduce, increasing granularity to 8
Failed to reduce, increasing granularity to 11
Reduced test has 11 steps
REDUCED IN 1.02356314659 SECONDS
Alpha converting test...
int0 = 1 # STEP 0
avl0 = avl.AVLTree() # STEP 1
avl0.insert(int0) # STEP 2
int0 = 6 # STEP 3
avl0.insert(int0) # STEP 4
int0 = 8 # STEP 5
avl0.insert(int0) # STEP 6
int1 = 20 # STEP 7
avl0.insert(int1) # STEP 8
int1 = 1 # STEP 9
avl0.delete(int1) # STEP 10
SAVING TEST AS failure.26816.test
FINAL VERSION OF TEST, WITH LOGGED REPLAY:
int0 = 1 # STEP 0
ACTION: int0 = 1
int0 = None : <type 'NoneType'>
=> int0 = 1 : <type 'int'>
==================================================
avl0 = avl.AVLTree() # STEP 1
ACTION: avl0 = avl.AVLTree()
avl0 = None : <type 'NoneType'>
avl_REF0 = None : <type 'NoneType'>
=> avl0 = <avlbug2.AVLTree instance at 0x10311edd0> : <type 'instance'>
REFERENCE ACTION: avl_REF0 = set()
=> avl_REF0 = set([]) : <type 'set'>
==================================================
avl0.insert(int0) # STEP 2
ACTION: avl0.insert(int0)
int0 = 1 : <type 'int'>
avl0 = <avlbug2.AVLTree instance at 0x10311edd0> : <type 'instance'>
avl_REF0 = set([]) : <type 'set'>
REFERENCE ACTION: avl_REF0.add(int0)
=> avl_REF0 = set([1]) : <type 'set'>
==================================================
int0 = 6 # STEP 3
ACTION: int0 = 6
int0 = 1 : <type 'int'>
=> int0 = 6 : <type 'int'>
==================================================
avl0.insert(int0) # STEP 4
ACTION: avl0.insert(int0)
int0 = 6 : <type 'int'>
avl0 = <avlbug2.AVLTree instance at 0x10311edd0> : <type 'instance'>
avl_REF0 = set([1]) : <type 'set'>
REFERENCE ACTION: avl_REF0.add(int0)
=> avl_REF0 = set([1, 6]) : <type 'set'>
==================================================
int0 = 8 # STEP 5
ACTION: int0 = 8
int0 = 6 : <type 'int'>
=> int0 = 8 : <type 'int'>
==================================================
avl0.insert(int0) # STEP 6
ACTION: avl0.insert(int0)
int0 = 8 : <type 'int'>
avl0 = <avlbug2.AVLTree instance at 0x10311edd0> : <type 'instance'>
avl_REF0 = set([1, 6]) : <type 'set'>
REFERENCE ACTION: avl_REF0.add(int0)
=> avl_REF0 = set([8, 1, 6]) : <type 'set'>
==================================================
int1 = 20 # STEP 7
ACTION: int1 = 20
int1 = None : <type 'NoneType'>
=> int1 = 20 : <type 'int'>
==================================================
avl0.insert(int1) # STEP 8
ACTION: avl0.insert(int1)
int1 = 20 : <type 'int'>
avl0 = <avlbug2.AVLTree instance at 0x10311edd0> : <type 'instance'>
avl_REF0 = set([8, 1, 6]) : <type 'set'>
REFERENCE ACTION: avl_REF0.add(int1)
=> avl_REF0 = set([8, 1, 20, 6]) : <type 'set'>
==================================================
int1 = 1 # STEP 9
ACTION: int1 = 1
int1 = 20 : <type 'int'>
=> int1 = 1 : <type 'int'>
==================================================
avl0.delete(int1) # STEP 10
ACTION: avl0.delete(int1)
int1 = 1 : <type 'int'>
avl0 = <avlbug2.AVLTree instance at 0x10311edd0> : <type 'instance'>
avl_REF0 = set([8, 1, 20, 6]) : <type 'set'>
REFERENCE ACTION: avl_REF0.discard(int1)
=> avl_REF0 = set([8, 20, 6]) : <type 'set'>
==================================================
ERROR: (<type 'exceptions.AssertionError'>, AssertionError(), <traceback object at 0x10369c128>)
TRACEBACK:
File "/Users/alex/tstl/examples/AVL/sut.py", line 7960, in check
assert self.p_avl[0].check_balanced()
STOPPING TESTING DUE TO FAILED TEST
79.552715655 PERCENT COVERED
2.22598695755 TOTAL RUNTIME
15 EXECUTED
1498 TOTAL TEST OPERATIONS
0.408244371414 TIME SPENT EXECUTING TEST OPERATIONS
0.0258889198303 TIME SPENT EVALUATING GUARDS AND CHOOSING ACTIONS
0.706946611404 TIME SPENT CHECKING PROPERTIES
1.11519098282 TOTAL TIME SPENT RUNNING SUT
0.00753235816956 TIME SPENT RESTARTING
1.03021097183 TIME SPENT REDUCING TEST CASES
220 BRANCHES COVERED
164 STATEMENTS COVERED
Using --output, the failing test can be saved to a named file, and with the standalone.py
utility, converted into a completely standalone test case that does
not require TSTL itself.
~/tstl/examples/AVL$ tstl_rt --timeout 30 --output failure.test
Random testing using config=Config(swarmSwitch=None, verbose=False, fastQuickAnalysis=False, failedLogging=None, maxtests=-1, greedyStutter=False, exploit=None, seed=None, generalize=False, localize=False, uncaught=False, speed='FAST', internal=False, normalize=False, highLowSwarm=None, replayable=False, essentials=False, quickTests=False, coverfile='coverage.out', uniqueValuesAnalysis=False, swarm=False, ignoreprops=False, total=False, swarmLength=None, noreassign=False, profile=False, full=False, multiple=False, relax=False, swarmP=0.5, stutter=None, running=False, compareFails=False, nocover=False, swarmProbs=None, gendepth=None, quickAnalysis=False, exploitCeiling=0.1, logging=None, html=None, keep=False, depth=100, throughput=False, timeout=30, output=None, markov=None, startExploit=0)
...
~/tstl/examples/AVL$ tstl_reduce failure.test failure_norm.test
REDUCING...
...
NORMALIZING...
...
~/tstl/examples/AVL$ tstl_standalone failure_norm.test failure.py
~/tstl/examples/AVL$ python failure_small.py
Traceback (most recent call last):
File "failure.py", line 98, in <module>
check()
File "failure.py", line 45, in check
assert avl2.check_balanced()
AssertionError
The final useful hint for getting started is that sometimes you may want to test something
(for example, a library implemented in C) where failing tests crash the Python interpreter. This is possible,
but requires some effort. First, run tstl_rt with the --replayable option. This causes the generator to
keep a file, currtest.test, in the directory you are running testing in: this file holds the current test. If the random tester crashes, this will include the action that caused the crash. In a few rare cases, the behavior of past tests is also relevant to a crash (reloading the module does not really reset state of the system -- e.g., interacting with hardware). For these cases, use --total and look at the file fulltest.test, which contains ALL actions ever performed by the random tester.
The currtest.test and fulltest.test files work just like normal TSTL files, and can be replayed with the replay utility or turned into standalone files. However, for test reduction and normalization to work correctly, they must be reduced by passing the --sandbox argument to tstl_reduce.
What about tests that fail by entering an infinite loop? The same technique as is used for crashes works. However, you need to run tstl_rt with a time limit (using ulimit if you are on UNIX-like systems, for example). The tstl_reduce utility provides a --timeout argument to handle such tests, but this only works on systems supporting ulimit, for now. In very rare cases, you might have a test execution lock up because, for example, the failure causes a read from standard input. If you hit this, contact me.
Finally, how do you integrate TSTL testing with more conventional approaches, e.g., pytest? The file test_tstl_regressions.py in the examples directory shows one way. If you add all your TSTL tests of interest to a tstl_tests directory under the directory where sut.py lives, you can make pytest run all your TSTL tests. Perhaps more interestingly, this file also wraps a simple caller that forces 60 seconds of random testing to be executed by pytest, as a sanity check. You can tweak the configuration of the random testing easily -- often, adding "--swarm" is a good idea.
There are no developer docs yet, which will hopefully change in the future.
The best shakedown test for tstl is to compile and run (using tstl_rt) the AVL
example. Removing any call to the balancing function in the avl.py
code should cause TSTL to produce a failing test case.