TSTL is a domain-specific language (DSL) and set of tools to support automated generation of tests for software. This implementation targets Python. You define (in Python) a set of components used to build up a test, and any properties you want to hold for the tested system, and TSTL generates tests for your system. TSTL supports test replay, test reduction, and code coverage analysis, and includes push-button support for some sophisticated test-generation methods. In other words, TSTL is a property-based testing tool.

What is property based testing? Property-based testing is testing that relies not on developers specifying results for specific inputs or call sequences, but on more general specification of behavior, combined with automatic generation of many tests to make sure that the general specification holds. For more on property-based testing see:

• https://fsharpforfunandprofit.com/posts/property-based-testing/

• https://hypothesis.works/articles/what-is-property-based-testing/

• https://github.com/trailofbits/deepstate (a tool mixing symbolic analysis and fuzzing with property-based testing, for C and C++, with design somewhat informed by TSTL)

TSTL has been used to find and fix real faults in real code, including ESRI's ArcPy (http://desktop.arcgis.com/en/arcmap/latest/analyze/arcpy/what-is-arcpy-.htm), sortedcontainers (https://github.com/grantjenks/sorted_containers), gmpy2 (https://github.com/aleaxit/gmpy), sympy (http://www.sympy.org/en/index.html), pyfakefs (https://github.com/jmcgeheeiv/pyfakefs), Python itself (https://bugs.python.org/issue27870), the Solidity compiler (https://github.com/ethereum/solidity), a Solidity static analysis tool (https://github.com/crytic/slither), the Vyper compiler (e.g. vyperlang/vyper#1658), and even OS X.

You can grab a recent tstl most easily using pip. pip install tstl should work fine. If you want something even more recent you can do:

git clone https://github.com/agroce/tstl.git
cd tstl
python setup.py install

For code coverage, you will also need to install Ned Batchelder's coverage.py tool; pip install coverage is all that is needed.

To get an idea of how TSTL operates, let's try a toy example. We will use TSTL to solve a simple "puzzle" to see if it is possible to generate the integer value 510 using only a few lines of Python code, using only a small set of operations (add 4, subtract 3, multiply by 3, and produce a power of two) starting from 0.

1. Create a file called nutshell.tstl with the following content:

@import math

# A line beginning with an @ is just python code.

pool: <int> 5

# A pool is a set of values we'll produce and use in testing.
# We need some integers, and we'll let TSTL produce up to 5 of them.
# The name is a variable name, basically, but often will be like a
# type name, showing how the value is used.

<int> := 0
<int> += 4
<int> -= 3
<int> *= 3
{OverflowError} <int> := int(math.pow(2,<int>))

# These are actions, basically single lines of Python code.
# The big changes from normal Python are:
# 1. := is like Python assignment with =, but also tells TSTL this
# assignment _initializes_ a value.
# 2. <int> is a placeholder meaning _any_ int value in the pool.
# 3. {OverflowError} means that we want to ignore if this line of
# Python produces an uncaught OverflowError exception.

# A test in TSTL is a sequence of actions.  So, given the above, one
# test would be:
#
# int3 = 0
# int4 = 0
# int3 *= 3
# int4 += 4
# int3 = 0
# int2 = int(math.pow(2,int4))
# int2 -= 3

# As you can see, the actions can appear in any order, but every
# pool variable is first initialized by some := assignment.
# Similarly, TSTL may use pool variables in an arbitrary order;
# thus we never see int0 or int1 used, here, by chance.

# The size of the int pool determines how many different ints can
# appear in such a test.  You can think of it as TSTL's "working
# memory."  If you have a pool size of 1, and an action like
# foo(<int>,<int>) you'll always call foo with the same value for both
# parameters -- like foo(int0,int0).  You should always have a pool
# size at least as large as the number of times you use a pool in a
# single action.  More is often better, to give TSTL more ability to
# bring back in earlier computed values.

property: <int> != 510

# property: expresses an invariant of what we are testing.  If the
# boolean expression evaluates to False, the test has failed.

As in normal Python, # indicates a comment. Comment lines are below the TSTL code being described.

2. Type tstl nutshell.tstl.
3. Type tstl_rt --normalize --output nutshell.test.

This should, in a few seconds, find a way to violate the property (produce the value 510), find a maximally-simple version of that "failing test", and produce a file nutshell.test that contains the test. If we had omitted the {OverflowError} TSTL would either have found a way to produce 510, or (less likely) would have found a way to produce an overflow in the pow call: either would be considered a failure.

4. Type tstl_replay nutshell.test --verbose.

This will replay the test you just created.

5. Comment out (using # as usual in Python code) the line <int> -= 3. Now try running tstl_rt.

The core idea of TSTL is to define a set of possible steps in a test, plus properties describing what can be considered a test failure, and let TSTL find out if there exists a sequence of actions that will produce a test failure. The actions may be function or method calls, or steps that assemble input data (for example, building up a string to pass to a parser), or, really, anything you can do with Python.

TSTL installs a few standard tools: the TSTL compiler itself, tstl; a random test 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.

You can do most of what you'll need with just the commands tstl, tstl_rt, tstl_replay, and tstl_reduce.

• tstl <filename.tstl> compiles a .tstl file into an sut.py interface for testing
• tstl_rt runs random testing on the sut.py in the current directory, and dumps any discovered faults into .test files
• tstl_replay <filename.test> runs a saved TSTL test, and tells you if it passes or fails; with --verbose it provides a fairly detailed trace of the test execution
• tstl_reduce <filename.test> <newfilename.tstl> takes <filename.test> runs reduction and normalization on it to produce a shorter, easier to understand test, and saves the output as <newfilename.tstl>.

All of these tools offer a large number of configuration options; --help will produce a list of supported options for all TSTL tools.

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.

avlnew.tstl creates a pretty full-featured tester 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, how to create these things, and what we can do with these things, and then TSTL produces sequences of actions, that is tests, that match our definition. TSTL 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.

Note that we start with "raw Python" to import the avl module, the SUT. While TSTL supports using from, aliases, and wildcards in imports, you should always import the module(s) under test with a simple import. This allows TSTL to identify the code to be tested and automatically provide coverage, static analysis-aided testing methods, and proper module management. Utility code in the standard library, on the other hand, can be imported any way you wish.

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://agroce.github.io/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. Without --output the test is still saved, but the name is based on the process ID of tstl_rt. In either case, you can easily re-run a saved test, even without converting to a standalone test, using tstl_replay <testname>, and reduce it using tstl_reduce. The --verbose flag is useful for replay, since it will show you exactly what happens during a test.

~/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_replay failure_norm.test --verbose
...
~/tstl/examples/AVL$ tstl_standalone failure_norm.test failure.py
~/tstl/examples/AVL$ python failure.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.

The most important thing missing from the simplified version of the AVL harness above is the use of TSTL's powerful differential testing capabilities.

When a TSTL pool is defined as a "ref" pool, as in the full avlnew.tstl version, that means the pool will actually have two versions. The first works just like a normal TSTL pool. The second pool is a "copy" of the original pool, that is created by running the same Python code as the original version, with one key difference: some of the code will be automatically modified so that the "copy" can use a different underlying implementation. In the avlnew.tstl file, the code necessary for using a reference is quite small:

pool: <avl> 3 OPAQUE REF

reference: avl.AVLTree ==> set
reference: insert ==> add
reference: delete ==> discard
reference: find ==> __contains__

reference: METHOD(inorder) ==> CALL(items)
reference: METHOD(display) ==> CALL(print)

compare: find
compare: inorder

The pool is marked as a "REF" pool (TSTL is not case sensitive for such designations). The actual introduction of differential testing is accomplished largely by the "reference:" parts of the harness. The first of these changes the call to the AVLTree constructor to a call to Python's built-in set implementation. The next three change the methods for AVLTrees to the appropriate methods for sets. The ones using METHOD and CALL transform methods of AVLTree into calls to functions on sets. Finally the two "compare:" lines tell TSTL that the return values for the actions find and inorder and their reference equivalents should be compared for equality, and failure should result if the results don't match.

Using the universalmutator tool, you can easily investigate the power of your TSTL test harnesses to find bugs. For example, if we wanted to see how effective 10 seconds of random testing using our AVL harness is, we could do this:

~/tstl/examples/AVL$ pip install universalmutator
~/tstl/examples/AVL$ tstl avlnew.tstl
Generating harness core using config=Config(tstl='avlnew.tstl', stats=False, checkFailureDeterminism=False, pylib=False, defaultReplay=False, forceRefExceptionMatch=False, enumerateEnabled=False, forceStrongRefExceptionMatch=False, classname='sut', version=False, noCover=False, debug=False, output='sut.py', noReload=False, ignoreAngles=False, coverReload=False, coverInit=False)
~/tstl/examples/AVL$ mkdir mutants
~/tstl/examples/AVL$ mutate avl.py --mutantDir mutants
*** UNIVERSALMUTATOR ***
MUTATING WITH RULES: universal.rules, python.rules
1497 MUTANTS GENERATED BY RULES
...
768 VALID MUTANTS
591 INVALID MUTANTS
138 REDUNDANT MUTANTS
~/tstl/examples/AVL$ analyze_mutants avl.py "tstl_rt --timeout 10" --mutantDir mutants
ANALYZING avl.py
COMMAND: ** ['tstl_rt --timeout 10'] **
#1: [0.0s 0.0% DONE]
  mutants/avl.mutant.89.py NOT KILLED
  RUNNING SCORE: 0.0
#2: [10.43s 0.13% DONE]
  mutants/avl.mutant.507.py KILLED IN 0.689107179642
  RUNNING SCORE: 0.5
#3: [11.12s 0.26% DONE]
  mutants/avl.mutant.312.py KILLED IN 2.01887583733
  RUNNING SCORE: 0.666666666667
#4: [13.14s 0.39% DONE]
  mutants/avl.mutant.165.py NOT KILLED
  RUNNING SCORE: 0.5
...

This reports the score for mutants that our testing doesn't even cover, which may be of little interest if we only target part of an API. And, frankly, there are easier ways to see which code isn't covered than using mutation testing! So we may want to limit our analysis to code actually covered by the AVL harness, using a generously large timeout:

~/tstl/examples/AVL$ tstl_rt --timeout 120 --internal >& avlnew.lines
~/tstl/examples/AVL$ check_covered avl.py avlnew.lines coveredmutants.txt --tstl --mutantDir mutants
~/tstl/examples/AVL$ analyze_mutants avl.py "tstl_rt --timeout 10" --mutantDir mutants --fromFile coveredmutants.txt
ANALYZING avl.py
COMMAND: ** ['tstl_rt --timeout 10'] **
#1: [0.0s 0.0% DONE]
  mutants/avl.mutant.17.py KILLED IN 0.574796915054
  RUNNING SCORE: 1.0
#2: [0.58s 0.14% DONE]
  mutants/avl.mutant.186.py KILLED IN 0.581020116806
  RUNNING SCORE: 1.0
#3: [1.16s 0.28% DONE]
  mutants/avl.mutant.692.py NOT KILLED
  RUNNING SCORE: 0.666666666667
...

Sometimes just doing tstl_rt or even tstl_rt --swarm isn't enough. There are other options for improving testing. A particularly powerful one in many cases is using the size of functions in terms of lines-of-code to guide testing. To do this, you first let TSTL determine the sizes:

tstl_rt --generateLOC sut.loc --timeout 120 --noCover

(the --noCover is needed because LOC uses instrumentation that interacts badly with coverage instrumentation).

Then you use that generated file to guide testing:

tstl_rt --biasLOC sut.loc

It's also a good idea, for faster testing (since the power of random testing is partly in generating huge numbers of tests every minute), to turn off code coverage collection with --noCover. This isn't so great if you are looking to see if your tests cover your code well, but for pedal-to-the-metal bug-hunting, it is often the way to go.

There are other things that can improve testing. The --profileProbs option gathers information on how often each action in the TSTL harness has been taken during testing, and linearly biases random action choice towards actions that have been taken fewer times. This slows down test generation substantially in most cases, but for many programs (especially complex ones) it also dramatically improves code coverage and fault detection, by exploring hard-to-hit actions, and spending less time generating input data vs. running the SUT. In these cases the loss in test throughput produced by attempts to take likely-disabled actions is much more than compensated for by an improvement in test quality. Because both rely on setting action probabilities, --profileProbs and --biasLOC are unfortunately not compatible.

For some programs, the structure of the harness file slows down test generation, and the --useDependencies option can improve test throughput by a factor of 2-10x. However, for most programs this option slows down test generation by roughly a factor of two.

Because the utility of these options varies so widely, it is best to simply try them out. For --profileProbs you should see a large increase in code coverage if it is effective for your testing problem (probably accompanied by a large drop in the number of tests generated), and for --useDependencies you should see a large increase in the number of tests/test operations performed.

You can also try a "genetic algorithms" approach guided by coverage, that exploits "high coverage" tests:

tstl_rt --exploit 0.8 --Pmutate 0.5

Adding --reducePool sometimes also improves the performance of this method.

You can tune the exploit and mutate parameters to see if they improve results. You can even combine lines-of-code bias or profile-based probabilities with the exploit approach and/or swarm testing. Unfortunately, using --exploit does mean you can't get away with --noCover to avoid the overhead of computing code coverage.

We're working on a way to get TSTL to perform experiments automatically and advise you of the best configuration for testing a given harness.

To get a set of very fast "regression tests" you can run tstl_rt for a long time in a good configuration with the --quickTests option, and generate a set of very short tests with high code coverage.

TSTL supports automated fault localization. If you have a harness that finds a bug, you might get some insight into the nature of that bug by running something like:

tstl-rt --localize --multiple

This will run TSTL for an hour, generate a number of failing test cases (if your bug can be found relatively easily in an hour), and then report on the 20 most-likely-faulty statements and branches in the code under test. Some of this code may be involved in things like printing assertion values, or error handling for the fault, but there's a good chance you'll find the buggy code in the localization results, in our experience. In fact, a five minute run will suffice for good localization, often, if five minutes is sufficient to find your bug a few times. Note that results are much worse if you have more than one bug!

One way to use swarm testing effectively is to apply directed swarm testing, an approach where data on how swarm interacts with code coverage is used to boost coverage of rarely covered statements and branches.

To do this, run your initial testing using tstl_rt (for an hour or more) with the options:

• --swarm
• --saveSwarmCoverage <filename1>
• --fullCoverage <filename2>

This will test your program, and produce two files of interest. <filename2> will contain a simple text file with branches and statements covered, ranked by the number of times they were covered. This is basically a simplified version of the kind of output you may be familiar with from gcov or other tools. You can look at this file, and identify interesting looking, but rarely-covered, code.

Then, take the identifier (the full string, including parenthesis) of the interesting code and use the tstl_directedswarm tool like this:

tstl_directedswarm <filename1> "<coverage target>" <probFile>

(<filename1> is the file you produced in the original run of swarm testing.) This will try to figure out which actions help ("trigger") and hinder ("suppress") coverage of the target code, and produce a file (probFile) with probabilities for use in more focused swarm testing. If the tool doesn't identify any triggers or suppressors, try running again with the --confidence option and a number less than 0.95; the lower the confidence required, the more likely you are to find triggers and suppressors, but the less likely they are to be meaningful -- you can try slowly lowering until you get some results. Then run testing again, like this:

tstl_rt --swarm --swarmProbs <probFile>

You should usually be able to cover the rarely-covered code well this way. Since covering rarely covered code often uncovers interesting new never-seen-before code, you may want to repeat this process once you've explored the rarely-covered code from your intial run. You can, of course, store swarm coverage and full coverage stats for the focused runs of TSTL, and keep exploring.

A more systematic way to go about directed swarm testing is to try:

tstl_analyzeswarm <filename1> <prefix> --cutoff 0.5

to generate triggers and suppressors for ALL coverage targets hit during a run, grouped into equivalence classes (targets with the same set of triggers and suppressors) and ranked by the least-hit target in each equivalence class. The output will be stored in files beginning with <prefix>: a set of files named <prefix>.N.probs that can used with --swarmProbs, and a single .class file, with all the targets, triggers, suppressors, and minimum frequencies for classes, plus pointers to the related probability files. Just iterating through the generated probability files for the classes for the rarest targets is a good way to go about directed swarm testing. The 0.5 above can be any cutoff, above which targets hit by at least that fraction of tests are considered well-tested and ignored. Setting this as low as 0.01 can work well, for initial runs producing a large number of tests.

You can even use AFL (http://lcamtuf.coredump.cx/afl/) to generate TSTL tests. You need to install AFL itself and the python-afl pip package (or grab the code from github at https://github.com/jwilk/python-afl). Then you can fuzz using AFL in any directory with a compiled TSTL harness:

tstl_afl_fuzz --output <outputdir> --input <inputdir>

This will use some (usually good) default settings to first have TSTL generate some good starting tests for AFL to build on, then run AFL for a day on the SUT. A day may not be enough, so the same --timeout parameter is supported as by the TSTL random tester. You can also use swarm testing by adding --swarm. There are other, less frequently used, options as well. Failing tests generated by AFL will be stored as aflfail.<PID>.test in the current directory. One piece of advice: <outputdir> should probably be a ramdisk, unless you want to really hammer your SSD (don't even think about doing this on an actual hard drive).

You should also try the --persist option to tstl_afl_fuzz, which will often improve fuzzing speed by a large margin, and dramatically improve AFL results (since throughput is so critical); however, this is somewhat less well-tested than the non-persistent mode. With more testing, this will likely become the default setting, so you may want to jump ahead of the curve, and only run non-persistent if persistent mode seems to cause problems.

This is a powerful testing option, as it lets you use AFL's great heuristics to fuzz things that are at best highly inconvenient with just AFL. You can set up complex TSTL properties, mix grammar generation and API-call sequences, and do differential testing TSTL-style, but use AFL's tuned input generation methods. The main drawback is that AFL really expects much faster executables than TSTL is giving it, so you probably need to run for days to improve on what TSTL can do in an hour, unless your SUT is unusual. But it is certainly an attractive option for week-long heavy-duty testing when tstl_rt isn't finding any problems.

Note that if you don't use tstl_afl_fuzz but directly call py-afl-fuzz you probably (except on Mac OS, where memory limiting doesn't work anyway) need a large -m for TSTL to work.

Under the hood, thetstl_aflcommand takes a file of bytes and interprets every N bytes (N depends on how many actions your harness has) as the index of a TSTL action (modulo the number of actions), using sut.py as usual. When tstl_afl detects a failure it also produces a conventional TSTL test file under the name aflfail.<PID>.test. You can even use --swarm to interpret the first 4 bytes as a seed to control swarm testing, thus allowing AFL to use swarm testing; this has the drawback that the file will be interpreted incorrectly by other TSTL tools, unless you pass them the --aflswarm option. Most TSTL tools take an --afl option that indicates tests to be read in are in AFL format, and --aflswarm to indicate they are swarm tests.

tstl_afl is also useful for turning a single AFL byte file into a normal TSTL test file, using the --alwaysSave option, which dumps a TSTL test file in the current directory, created from the byte-based input.

There are also tools for converting large numbers of files to and from AFL format. tstl_toafl simply takes existing TSTL test files and converts them to AFL byte inputs, and tstl_fromafl does the expected opposite (and takes an argument indicating the files are in swarm format). tstl_aflcorpus randomly generates inputs that trigger novel SUT coverage to get AFL started, but it is usually easier to just generate quick tests with tstl_rt --quickTests and convert those with tstl_toafl. tstl_aflcorpus does allow using the AFL swarm format, however; just run it with --swarm. Because of the way the swarm format works, it is unfortunately currently not possible to extract a swarm format test from a standard TSTL test.

tstl_smallcheck is a special-purpose test generator that uses a depth-first-search to exhaustively generate tests up to a provided depth limit. The tool outputs coverage-increasing tests, and stops if it encounters a failure. This will seldom finish if the depth is more than 3 to 10 (at the most) steps, unless it hits a failure. If you run out of patience, you can interrupt the process with CTRL-C and the tool will save the discovered tests.

One way to get deeper "exhaustive" testing is to use the --recursive option to explore from coverage increasing tests, repeatedly up to a limited number of times, using the same depth as the original run (and a small initial depth).

If you want to collect all failing tests, not just stop at the first one, you'll need to use the --multiple option. Because of their small size and the presumed desire for exhaustive exploration (you used this tool, after all), this tool provides neither reduction nor normalization of covering tests or failures, to avoid any risk of slippage.

In addition to --recursive, you can use --visited or --visitedList to avoid re-visiting already explored states during the DFS; however, this requires some care. If the tool fails, or the tests don't seem valid/correct, you may want to recompile your harness with --defaultReplay, because state-based backtracking doesn't work. In many cases, due to the high cost of state comparison in this setting, keeping track of visited states may not even be very helpful.

Random testing using tstl_rt is probably almost always more effective than this approach, but tstl_smallcheck can provide guarantees that tstl_rt cannot, such as that no test with fewer than four steps can cause any failures. Starting a smallcheck from existing quick tests using the --fromTests option is one way to add extra confidence in your testing.

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. One additional difference for the typical user is that TSTL has considerable built-in support for performing differential/reference testing, where your SUT is compared to a reference implementation, possibly with some code to handle expected differences (see the pyfakefs example for a good look at how powerful this can be). Finally, TSTL is built as a practical testing tool, but the design is strongly influenced by the decision to make it useful as a platform for experimenting with novel software testing algorithms.

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.

If you test real software with a good harness, you may well find many issues. There are a few ways to deal with this. First, using --normalize when doing --multiple runs with tstl_rt can help. In some cases (file systems) normalization (or even reduction) goes too far. In testing at NASA, we found that "last operation" was a good heuristic for different bugs. Using --keepLast in testing (or when using tstl_reduce) forces reduction and normalization to leave the last step alone. Normalization can still move it around, or change the pool it uses, but is much more careful about changing the actual action performed. There is also a tool tstl_triage that takes a glob expression for a set of tests, runs them all, and reports ones with different (heuristic) failure signatures. In particular, it gives you the shortest test for each signature. Remember that triage requires a glob expression (in quotes) not a list of files. This is so it can handle even sets of tests that go beyond the shell expansion limit. We assume that you won't need to handle that many tests in regression, but for triage, who knows? Another tool, tstl_fpf takes similar arguments to tstl_triage but instead of clustering tests into groups that are likely the same bug, it ranks all tests, such that very different tests are high in the ranking, using the "furthest-point-first" (FPF) method proposed by Chen et. al in PLDI 2013.

For more details on TSTL, the best starting point is a comprehensive journal paper in STTT: http://agroce.github.io/sttt17.pdf. There are also NASA Formal Methods (NFM) and International Symposium on Software Testing and Analysis (ISSTA) 2015 papers at http://agroce.github.io/nfm15.pdf and http://agroce.github.io/issta15.pdf, with some implementation details or concepts that are not present in the more up-to-date and complete paper. In particular, the NFM paper, "A Little* Language for Testing" has a deprecated syntax and other issues, but is the most concise explanation of the core TSTL idea: a DSL embedding a full programming language, designed to make testing (and building testing tools) easy.

There is a more recent paper describing test normalization, a feature unique to TSTL, in more detail, http://agroce.github.io/issta17.pdf, as well as a tool paper describing how to use TSTL's test manipulation commands (http://agroce.github.io/issta17tool.pdf).

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 above is preliminary. The best way to get started, once you understand the basic tools (tstl, tstl_rt, tstl_replay, and tstl_reduce) is to examine the examples directory and try out real TSTL test harnesses. For the brave, reading tstl/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.

Note that TSTL was originally written for Python 2.7, has mostly been developed/tested that way, and is not extremely well-tested yet with Python 3.0+. However, it should work ok, thanks to mrbean-bremen, and the Travis tests check that TSTL works fine on Python 3.6. Earlier 3.0+ versions may have some "gotchas."

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.

Who is responsible for TSTL?

• Alex Groce (agroce) wrote this file, and most of the current code base, and is running the show. If there is a problem with TSTL, it is my fault, and don't blame anyone below.

• Josie Holmes (josieholmes) contributed to core language design changes, and is responsible for the ideas (and some of the code) for the various slippage reduction strategies, plus the LOC bias work and Markov things. Before Josie's work, TSTL was extremely hard to read, and considerably less efficient.

• Jervis Pinto was the other original TSTL-er, and has his fingerprints on various parts of the early design and code that form the foundations of TSTL.

• Pranjal Mittal contributed a number of critical elements, including the initial effort to prepare TSTL for a pip release as a useful tool, and has helped publicize TSTL.

• Pooria Azimi added the <int,1> notation, which turns out to be one of the most important changes, and eliminated the need for the exceedingly awkward way of handling binding via Python functions and commit point based guards. Without this, you really don't have a useful TSTL.

• Kevin Kellar developed a (beta) Java version of TSTL: https://github.com/flipturnapps/TSTL-Java.

• My (Alex's) other graduate students (Amin Alipour, Rahul Gopinath, Arpit Christi, Chaoqiang Zhang, Shalini Shamasunder) and almost-mine graduate student (Iftekhar Ahmed) contributed to the general intellectual climate in which TSTL was born.

• Students in CS 499 at Northern Arizona University and CS 362, 562, and 569 at Oregon State University contributed a lot of ideas, and a few concrete language/tool changes or bug reports. These are too numerous to mention, and in some cases I don't recall who asked "why do you do it that stupid way?" in class, and got me thinking that it was in fact a stupid way to do things.

• Ned Batchelder, David R. MacIver, and John Regehr have no actual code in TSTL, but all contributed in significant ways to various implementation aspects, in ways that go beyond the general disclaimer that TSTL freely steals from the entire software testing (research) community.

• The pyfakefs team (mrbean-bremen and jmcgeheeiv on github) really worked with me to test pyfakefs, which resulted in a number of nice improvements to TSTL and to differential testing in particular. More recently, mrbean-bremen has taken the lead in making TSTL compatible with Python 3, which seems to mostly be done now!

• Jakub Wilk helped with modifications to python-afl that made TSTL/AFL integration work much better.

• Corey Kosak helped turn this README into something that you might actually enjoy reading, and gets to the point much faster than previous versions.

* Do you actually remember that asterisk way up there? The footnote is that TSTL is a little language. However, in another sense, it embeds all of Python which makes it pretty big. It depends on how you think about it.