Please also check the GitHub Releases Page for the source-code artifacts of each version.
-
Provide a (normalised) area under curve for timeline output variables.
Variables for statistics that produce a timeline, e.g., coverage development over time imply in interesting property: while the final coverage of two distinct runs/configurations might be equal, one might have a faster coverage increase than the other, thus could be considered better. The area under curve, i.e., the integral over the development function allows to access this property easily.
-
Add the ability to write the logging to a log file and not only STDOUT.
-
Add an auto-deploy action to GitHub to push releases to PyPI.
-
Fix a typo in the documentation (cf. GitHub issue #75).
-
Switch to
ruff-format
.
- Fix a bug in the handling of infinite loops in global scope (thanks to @BergLucas in #65)
- Remove the dependency to our custom MutPy fork by integrating the relevant code into Pynguin directly (thanks to @BergLucas in #64)
- Add possibility to control number of mutations in MOSA.
- Compute the area under curve if Pynguin traces the overage over time; area under curve can be interpreted as the integral over the coverage function, which is computed via trapezoidal approximation.
- Fix the assertion generation for
float
s (thanks to @BergLucas in #70) - Fix assertion and invalid function call (thanks to @BergLucas in #72)
- Remove unused code
- Fix
ruff
warnings - Add sequence variable for type-evolution tracking
- Add CLI options to ignore methods and modules from analysis (see #62)
- Fix
TypeError
bug in instrumentation of bytecode (see GitHub PR #51) - Add a dump method for type-information statistics
- Fix handling of aliased modules (see GitHub issue #57)
- Fix method-signature handling for C extensions (see GitHub issue #59)
- Activate a larger selection of checkers for ruff
- Implement various stopping conditions
- Fix an issue with type-information retrieval (see GitHub issue #35)
-
Breaking: Restructure the code
Most of the code related to the generation algorithms has been moved. The code now resides in the
ga
package, where it fits much better. All accompanying modules have also been moved to different places. This might cause breakage if you have developed extensions for Pynguin that rely on the structure of the modules. -
Update the
bytecode
library to its latest GitHub version.Necessary to include a fix for a regression in that library, which was pointed out to us in GitHub issue #42 (hopefully fixed by this library upgrade).
-
Adjust the header of generated test files; we now refer to Pynguin's website.
-
Extend the usage of the
ruff
tool for code analysis.
- Update literature references in documentation
- Add issue templates for GitHub
- Re-license Pynguin to MIT License
- Move code to
src
-based layout - Make configuration of
mypy
more strict - Remove an accidentally committed file
- Fix distance computations for
<=
and always convert values tofloat
. - Improve method blacklist for module analysis.
- Improve type handling.
- Allow Pynguin to run on itself. This can be achieved by the
pynguin_self.py
script from theutils
package. - Improve warning in documentation that Pynguin actually executes the code under test.
- Various refactorings and improvements.
- Update copyright notices in all files.
-
Write Pynguin version number to Cobertura XML report
-
Fix the computation of coverage values
Pynguin 0.26.0 added the possibility to optimise for, e.g., branch coverage while yielding the line coverage of the resulting test suite in the end. This had some unintentional implications, such as the value of the
Coverage
output variable having weird values. We fix this by providing additional output variablesFinalBranchCoverage
andFinalLineCoverage
that contain the final coverage values after all generation and postprocessing. See the runtimevariable.py module for details. -
Rewrite the internal type handling in Pynguin
This is a huge internal change that improves the internal type system and adds the possibility to trace types during test execution. Some highlights:
- we got rid of our internal abstraction of
type | None
to mark whether type information exists - we allow to retrieve additional or new type information from the test execution
- enhance our internal type representation to make it more flexible
- add the missing primitive type
complex
- a more suitable subtyping check for
Union
s
- we got rid of our internal abstraction of
-
Allow the calculation of coverage values regardless of optimisation.
Allows to yield, for example, the resulting line coverage of the test suite, while the optimisation during test generation was done for branch coverage. Add the required coverage values to your
--output-variables
list to activate this feature.Note: when doing this, the
Coverage
output variable will contain the average value of the incorporated coverage values! -
Provide a Cobertura-like coverage report
Pynguin already provides an HTML report that can be activated by setting
--create-coverage-report True
. This report is nice for human users but not very usable if one wants to automatically reason about the achieved coverage using tools. We thus also emit an XML report in the style of the Cobertura tool that can be used for further automated tools. -
Fix typo on
test_parameterizedstatements.py
(see #27, thanks to @stavares843) -
Fix typo on
testcase.py
(see #26, thanks to @stavares843) -
Improve mutation-based assertion generation
The assertion generation now does not more compare the recorded assertion traces but actually executes the assertions to make the whole process more reliable. Besides, we do not check for
is not None
as a fallback for object checks any more, but use anisinstance
check on the object's type to have a more precise assertion. -
Make statistics tests debuggable
Some accidental circular import made it impossible to run a debugger on tests in
pynguin.utils
. We resolved this by moving tests to another package. -
Partial rework of internal type system
We added an abstraction layer over the existing type hints from a module to make handling and reasoning with types easier. This abstraction is based on the one used by
mypy
, however, we only cover a small part of what PEP-484 actually defines. -
Make assertion generation more strict
- Fix bugs in mutation analysis and assertion generation
- Fix documentation build on read the docs
- Fix further issues with test-case isolation during execution.
- Fix some bugs regarding type information.
- Use astroid instead of Python's ast module for our module analysis due to its enhanced features.
- Update Pynguin's documentation to match fixes from Pynguin 0.24.0
-
Fix killing mutant reporting
-
Use black to format the generated test cases.
Pynguin now requires
black
as a run-time dependency to use its code formatting.
-
Provide a naive inheritance graph to improve input generation.
-
Improve killing of long-running test-case executions
-
Add computation of mutation scores for
MUTATION_ANALYSIS
assertion generation.The output variables
NumberOfCreatedMutants
,NumberOfKilledMutants
,NumberOfTimedOutMutants
, andMutationScore
allow to export those values. -
Do not enable
typing.TYPE_CHECKING
for SUT analysis as this may cause circular imports. -
Improve the black list of modules that shall not be incorporated into the test cluster.
-
Annotate failing tests with
@pytest.mark.xfail(strict=True)
. -
Improve log output of mutation-based assertion generation.
-
Add instrumentation to mutated modules to easier kill them.
This change is relevant only to the
MUTATION_ANALYSIS
assertion-generation strategy. -
Write errors in execution threads to the log instead of STDERR to avoid cluttering log output.
-
Add limits for amount and size of constants in the constant pool.
The configuration options
max_dynamic_length
andmax_dynamic_pool_size
allow to set sizes for the maximum length of strings/bytes that shall be stored in the dynamic constant pool and the maximum numbers of constants of a type, respectively. This prevents the constant pool from growing unreasonably large. -
Improve handling of type annotations.
-
Fix computation of cyclomatic complexity.
Computing cyclomatic complexity does not work for functions that are not present in the AST, e.g., default constructors. We now omit those from the computation of the cyclomatic-complexity output variables.
-
Fix selection of type-inference strategy.
-
Fix a bug in the type inference regarding cases where not type information is present.
-
Add a PyLint checker for calls to
print()
. -
Extend the blacklist of modules that shall not be analysed.
-
Raise
RuntimeError
from tracer when called from another thread. -
Provide better exception messages for critical failures.
-
Apply a further limit to the execution time of a single generated test case to at most 10 seconds.
-
Exclude empty enum classes from test cluster to fix test generation.
Parsing included modules raised an issue when the
enum
module is used: the test cluster then had a reference to theenum.Enum
class, which obviously does not contain any fields. In the following, generating tests failed, as soon as this class was selected to fulfil parameter values because there was no field to select from, e.g.,MyEnum.MY_FIELD
. We now exclude empty enums from the test cluster.
-
Fix a bug in the module analysis regarding nested functions
Nested functions/closures caused Pynguin's module analysis to crash with an failing assertion.
-
Improve the branch-distance computation for
bool
values -
Allow for more statistics variables regarding number of lines and cyclomatic complexity
- Fix a bug in terminating Pynguin due to threading
-
Remove splitting into passing and failing test suite.
Previously, we consider a test case passing if it did not raise any exception during its execution; it was considered failing otherwise. Pynguin did a split of the test cases into two test suites before exporting them. This was mainly an artefact from implementing the random algorithm in the very beginning of the project. Due to the improved assertion export for exception assertions we can now get rid of the split and export only one test module containing all generated test cases.
-
Remove the option to use a log file (
--log_file
or--log-file
).Pynguin writes its output to STDOUT/STDERR now, if requested by the
-v
/-vv
switch. This output is formatted by @willmcgugan's amazing rich library. A user can disable the output formatting by setting the--no-rich
flag. Of course, because we believe thatrich
is such an awesome library, we also provide an alias for this flag, called--poor
😉
-
Distinguish between expected and unexpected exceptions.
We consider an exception to be expected if it is explicitly raised in the code under test or is documented in the code's docstring. For those exceptions we build an
with pytest.raises
block around the exception-raising statement. All other exceptions are considered to be unexpected. We decorate the test method with the@pytest.mark.xfail
decorator and let the exception happen. It is up to the user to decide whether such an exception is expected. An exception here is theAssertionError
: it is considered to be expected as soon as there is anassert
statement in the code under test. -
Improve installation description to explicitly point to using a virtual environment (see GitHub issue #23, thanks to @tuckcodes).
-
Improve variable names and exception assertions
The assertion generation got an improved handling for asserting on exceptions, which creates more meaningful and (hopefully) better understandable assertions for exceptions.
-
Enhance the module analysis
This is basically a rewrite of our previously existing test cluster, which keeps track of all the callables from the subject under test as well as the subject's dependencies. It also incorporates an analysis of the subject's AST (if present) and allows for more and more precise information about the subject which can then improve the quality of the generated tests.
-
To distinguish bytecode instructions during instrumentation we add an
ArtificialInstr
for our own added instructions. -
Fix a bug in the tracing of runtime types.
During assertion generation Pynguin tracks the variable types to decide for which values it actually is able to generate assertions. Creating an assertion on a generator function does not work, as the type is not exposed by Python but only present during runtime—thus generating an object of this type always fail. We mitigate this by ignoring objects of type
builtins.generator
from the assertion generation. -
Improve documentation regarding coverage measurement and the coverage report
-
One can now use multiple stopping conditions at a time.
This breaks the CLI in two ways:
- The parameter
--stopping-condition
has been removed. - The parameter
--budget
was renamed to--maximum-search-time
.
Users have to change their run configurations accordingly!
To specify stopping conditions, add one or many from
--maximum-search-time
,--maximum-test-executions
,--maximum-statement-executions
, and--maximum-iterations
to your command line. - The parameter
- Clarify log output for search phases
- Pynguin now uses the
ast.unparse
function from Python's AST library instead of the third-partyastor
library to generate the source code of the test cases.
-
We drop the support for Python 3.8 and Python 3.9 with this version!
You need Python 3.10 to run Pynguin! We recommend using our Docker container, which is already based on Python 3.10, to run Pynguin.
- Add line coverage visualisation to the coverage report.
- Add a citation reference to our freshly accepted ICSE'22 tool demo paper “Pynguin: Automated Unit Test Generation for Python.
- Unify the modules for the analysis of the module under test.
- Add line coverage as another coverage type (thanks to @labrenz).
The user can now choose between using either line or branch coverage or both by
setting the parameter
--coverage-metrics
toLINE
,BRANCH
, orLINE,BRANCH
. Default value isBRANCH
.
- Update
CITATION.cff
information
- Refactor the assertion generation. This unifies the
SIMPLE
and theMUTATION_ANALYSIS
approaches. Furthermore, Pynguin now uses theMUTATION_ANALYSIS
approach again as the default. - Update the type annotations in Pynguin's code to the simplified, future versions
(e.g. instead of
Dict[str, Set[int]]
we can now writedict[str, set[int]]
) and do not need any imports from Python'styping
module. - Fix a crash of the seeding when native modules are present. Fixes #20.
- Provide a hint in the documentation that PyCharm 2021.3 now integrates
poetry
support, thus no plugin is required for this (and newer) versions (thanks to @labrenz).
- Fix a bug for mutating a statement that is not in the current test case (see #17).
- Set default assertion generation to
SIMPLE
due to issues with the experimental new generation strategy. - Add GitHub Actions that also runs our CI chain on GitHub.
- Breaking: Simplify the logging such that Pynguin uses different log levels also
for the log file. This removes the
-q
option to make all outputs quiet. - Pynguin now also supports field accesses during test generation. This is a preliminary feature.
- Fix a deadlock in the executor
- Pynguin now uses Python 3.10 as its default version for the provided Docker container as well as our CI. Still, Pynguin supports Python 3.8 and 3.9. Up to now, Python 3.11 is not yet supported.
- Refactor the state-trace implementation
- Remove the module-loader singleton
- Fix loading of mutated module for assertion generation. Caused that no assertion was generated because the mutants were not loaded properly. This is a regression from merging the assertion-generation strategy in Pynguin 0.13.0.
- Add the reference to the preprint of our EMSE journal submission to documentation.
- Clarify on the value of the
PYNGUIN_DANGER_AWARE
environment variable. One can set an arbitrary value for the variable, Pynguin only checks whether its defined.
- Fix documentation generation by adding our MutPy fork as a dependency
-
Add an assertion-generation strategy based on mutation (thanks to @f-str).
The new strategy is enabled by default (can be configured using the
--assertion-generation
parameter). It uses a custom fork of the mutation-testing framework MutPy, which mutates the subject under test and for each mutant traces the values of fields and returned by method calls. If these values differ between mutant and original code, an assertion is generated based on the result on the original subject under test. One can also control whether the strategy shall also generate assertions for unchanged values by the--generate-all-assertions
flag.The release also updates the documentation accordingly.
Note: This feature is an early prototype of such an assertion generation, which might cause unexpected behaviour. You can switch back to the previous strategy for assertion generation by setting
--assertion-generation SIMPLE
.
- Generate more reasonable variable names in tests.
Before this release, Pynguin only generated variables named
var0
,var1
, etc. A simple heuristics now attempts to generate more reasonable names depending on the type of the variable, such asint_0
,bool_1
, orstr_2
. We also adjusted the documentation to match this change. - We updated all provided PyCharm run configurations the use the more sophisticated queue example instead of the simple example module to see an improved output.
- Prevent a potential regression when updating the dependencies to version 0.0.17 of the simple-parsing library for CLI argument parsing, which changed its API.
- Fix a control-dependency bug in DynaMOSA. Loops in the control-dependence graph caused DynaMOSA to not consider certain targets because they were control dependent on goals that had not yet been covered due to the loop.
- Improve documentation
- Split and extend
FitnessValues
to avoid expensive re-computations. This also extends the API of theFitnessValues
and refactors large parts of the fitness handling. - Fix for bumpiness of flaky tests. Whenever Pynguin generates a test that behaves flaky result could be that coverage over time looks like ventricular fibrillation especially for the MIO algorithm. The fix prevents this by carefully revisiting the equality of chromosomes.
- Improve handling of entry/exit nodes in the CFG; this fixes issues with Python 3.10
- Provide support for Python 3.10
- Pynguin now set
typing.TYPE_CHECKING = True
explicitly before parsing the subject under test in order to be able to collect also information about types that are only imported due to type checking/providing type annotations. - Improved generation of collection statements
- Cleanup the implementation of the algorithms
- Add supports for enums in the test-generation process
- Cleanup the implementation of the dynamic value seeding
- Make Pynguin executions as deterministic as we possibly can
- Make DynaMOSA the default algorithm
- Allow the generation of an HTML coverage report similar to the one generated by Coverage.py. This allows to show the subject under test and the coverage achieved by the test cases generated by Pynguin in the web browser.
- Add a CITATION.cff file
- Improve the internal control-flow graph
- Improve the documentation
- Cleanup and remove unused code
- Fix a bug in post-processing
- Fix a bug in branch coverage instrumentation on
for
loops - Add a variant of the whole-suite algorithm that uses an archive
- Guard imports that are only necessary for type checking in Pynguin's modules by
if typing.TYPE_CHECKING
conditions
-
Add explicit code-execution prevention (thanks to @Wooza).
Pynguin now requires you to set the
PYNGUIN_DANGER_AWARE
environment variable before it actually does test generation. This was added due to the fact that Pynguin executes the module under test, including its dependencies, which could potentially cause harm on the user's system. By requiring the variable to be set explicitly, a user confirms that they are aware of this issue. Inside the Docker container, the variable is set automatically; we highly recommend this way of executing Pynguin!
- Fix spelling errors in README
- Proper support for Python 3.9
- Improve branch-distance calculations for
byte
values - Cleanup algorithm implementations
- Regroup configuration options
- Improve branch-distance calculations for data containers
- Save import coverage to a separate output variable
- Delete some unused code
- Add warning notice to read-me file
-
Breaking: Renamed
RANDOM_SEARCH
toRANDOM_TEST_SUITE_SEARCH
to select the random-sampling algorithm based on test suites introduced in Pynguin 0.7.0. -
Improve input generation for collection types.
-
Add an implementation of tournament selection for the use with DynaMOSA, MOSA, and Whole Suite.
For Whole Suite, on can choose the selection algorithm (either rank or tournament selection) by setting the value of the
--selection
parameter. -
Add DynaMOSA test-generation algorithm.
It can be selected via
--algorithm DYNAMOSA
. -
Add MIO test-generation algorithm.
It can be selected via
--algorithm MIO
. -
Add a random sampling algorithm based on test cases.
The algorithm is available by setting
--algorithm RANDOM_TEST_CASE_SEARCH
. It randomly picks one test case, adds all available fitness functions to it and adds it to the MOSA archive. If the test case is covering one fitness target it is retrieved by the archive. If it covers an already covered target but is shorter than the currently covering test case for that target, it replaces the latter. -
Fix
OSError
from executors queue.The queue was kept open until the garbage collector delete the object. This caused an
OSError
because it reached the OS's limit of open resource handles. We now close the queue in the test-case executor manually to mitigate this. -
Fix
__eq__
and__hash__
of parameterised statements.Before this, functions such as
foo(a)
andbar(a)
had been considered equivalent from their equals and hash-code implementation, which only compared the parameters and returns but not the actual function's name. -
Fix logging to work properly again.
- Fixes to seeding strategies
- Fix readme file
- Breaking: Renamed algorithms in configuration options.
Use
RANDOM
instead ofRANDOOPY
for feedback-directed random test generation andWHOLE_SUITE
instead ofWSPY
for whole-suite test generation. - Add MOSA test-generation algorithm.
It can be selected via
--algorithm MOSA
. - Add simple random-search test-generation algorithm.
It can be selected via
--algorithm RANDOM_SEARCH
. - Pynguin now supports the usage of a configuration file (based on Python's
argparse) module.
Use
@<path/to/file>
in the command-line options of Pynguin to specify a configuration file. See theargparse
documentation for details on the file structure. - Add further seeding strategies to extract dynamic values from execution and to use existing test cases as a seeded initial population (thanks to @Luki42)
- Resolve some weird merging issue
- Refactor chromosome representation to make the subtypes more interchangeable
- Update logo art
- Fix for test fixture that caused changes with every new fixture file
- Add attention note to documentation on executing arbitrary code
- Fix URL of logo in read me
- Fix build issues
- Add support for simple assertion generation (thanks to @Wooza).
For now, assertions can only be generated for simple types (
int
,float
,str
,bool
). All other assertions can only check whether or not a result of a method call isNone
. The generated assertions are regression assertions, i.e., they record the return values of methods during execution and assume them to be correct. - Provide a version-independent DOI on Zenodo in the read me
- Several bug fixes
- Provide this changelog
- Extends the documentation with a more appropriate example
- Removes outdated code
- Make artifact available via Zenodo
- Extends public documentation
- Provides documentation on readthedocs
- First public release of Pynguin
Internal release that was used in the evaluation of our paper “Automated Unit Test Generation for Python” for the 12th Symposium on Search-Based Software Engineering