MutPy is a mutation testing tool for Python 3.x source code. MutPy supports standard unittest module, generates YAML/HTML reports and has colorful output. It's apply mutation on AST level. You could boost your mutation testing process with high order mutations (HOM) and code coverage analysis.
From article at Wikipedia:
Mutation testing (or Mutation analysis or Program mutation) evaluates the quality of software tests. Mutation testing involves modifying a program's source code or byte code in small ways. A test suite that does not detect and reject the mutated code is considered defective. These so-called mutations, are based on well-defined mutation operators that either mimic typical programming errors (such as using the wrong operator or variable name) or force the creation of valuable tests (such as driving each expression to zero). The purpose is to help the tester develop effective tests or locate weaknesses in the test data used for the program or in sections of the code that are seldom or never accessed during execution.
You can easily install MutPy from PyPi:
$ pip install mutpy
... or if you want to have latest changes you can clone this repository and install MutPy from sources:
$ hg clone https://bitbucket.org/khalas/mutpy $ cd mutpy/ $ python3 setup.py install
Main code (calculator.py) - we will mutate it:
def mul(x, y):
return x * y
Test (test_calculator.py) - we will check its quality:
from unittest import TestCase
from calculator import mul
class CalculatorTest(TestCase):
def test_mul(self):
self.assertEqual(mul(2, 2), 4)
Now we can run MutPy in the same directory where we have our sources files:
$ mut.py --target calculator --unit-test test_calculator -m
This command will produce the following output:
[*] Start mutation process: - targets: calculator - tests: test_calculator [*] All tests passed: - test_calculator [0.00031 s] [*] Start mutants generation and execution: - [# 1] AOR calculator.py:2 : -------------------------------------------------------------------------------- 1: def mul(x, y): ~2: return x / y -------------------------------------------------------------------------------- [0.02944 s] killed by test_mul (test_calculator.CalculatorTest) - [# 2] AOR calculator.py:2 : -------------------------------------------------------------------------------- 1: def mul(x, y): ~2: return x // y -------------------------------------------------------------------------------- [0.02073 s] killed by test_mul (test_calculator.CalculatorTest) - [# 3] AOR calculator.py:2 : -------------------------------------------------------------------------------- 1: def mul(x, y): ~2: return x ** y -------------------------------------------------------------------------------- [0.01152 s] survived - [# 4] SDL calculator.py:2 : -------------------------------------------------------------------------------- 1: def mul(x, y): ~2: pass -------------------------------------------------------------------------------- [0.01437 s] killed by test_mul (test_calculator.CalculatorTest) [*] Mutation score [0.21818 s]: 75.0% - all: 4 - killed: 3 (75.0%) - survived: 1 (25.0%) - incompetent: 0 (0.0%) - timeout: 0 (0.0%)
First of all we run MutPy with few parameters. The most important are:
--target- after this flag we should pass module which we want to mutate.--unit-test- this flag point to our unit tests module.
There are few phases in mutation process which we can see on printed by MutPy
output (marked by star [*]):
- main code and tests modules loading,
- run tests with original (not mutated) code base,
- code mutation (main mutation phase),
- results summary.
There are 4 mutants generated in main mutation phase - 3 of them are killed and
only 1 mutant survived. We can see all stats at the end of MutPy output. In
this case MutPy didn't generate any incompetent (raised TypeError) and
timeout (generated infinite loop) mutants. Our mutation score (killed to all
mutants ratio) is 75%.
To increase mutation score (100% is our target) we need to improve our tests. This is a mutant which survived:
def mul(x, y):
return x ** y
This mutant survived because our test check if 2 * 2 == 4. Also 2 ** 2 ==
4, so this data aren't good to specify multiplication operation. We should
change it, eg:
from unittest import TestCase
from calculator import mul
class CalculatorTest(TestCase):
def test_mul(self):
self.assertEqual(mul(2, 3), 6)
We can run MutPy again and now mutation score is equal 100%.
List of all arguments with which you can run MutPy:
-t TARGET [TARGET ...],--target TARGET [TARGET ...]- target module or package to mutate,-u UNIT_TEST [UNIT_TEST ...],--unit-test UNIT_TEST [UNIT_TEST ...]- test class, test method, module or package with unit tests,-m,--show-mutants- show mutants source code,-r REPORT_FILE,--report REPORT_FILE- generate YAML report,--report-html DIR_NAME- generate HTML report,-f TIMEOUT_FACTOR.--timeout-factor TIMEOUT_FACTOR- max timeout factor (default 5),-d,--disable-stdout- try disable stdout during mutation (this option can damage your tests if you interact withsys.stdout),-e.--experimental-operators- use experimental operators,-o OPERATOR [OPERATOR ...],--operator OPERATOR [OPERATOR ...]- use only selected operators,--disable-operator OPERATOR [OPERATOR ...]- disable selected operators,-l.--list-operators- list available operators,-p DIR.--path DIR- extend Python path,--percentage PERCENTAGE- percentage of the generated mutants (mutation sampling),--coverage- mutate only covered code,-h,--help- show this help message and exit,-v,--version- show program's version number and exit,-q,--quiet- quiet mode,--debug- debug mode,-c.--colored-output- try print colored output,--coverage- mutate only covered code,--order ORDER- mutation order,--hom-strategy HOM_STRATEGY- HOM strategy,--list-hom-strategies- list available HOM strategies,--mutation-number MUTATION_NUMBER- run only one mutation (debug purpose).
List of MutPy mutation operators sorted by alphabetical order:
- AOD - arithmetic operator deletion
- AOR - arithmetic operator replacement
- ASR - assignment operator replacement
- BCR - break continue replacement
- COD - conditional operator deletion
- COI - conditional operator insertion
- CRP - constant replacement
- DDL - decorator deletion
- EHD - exception handler deletion
- EXS - exception swallowing
- IHD - hiding variable deletion
- IOD - overriding method deletion
- IOP - overridden method calling position change
- LCR - logical connector replacement
- LOD - logical operator deletion
- LOR - logical operator replacement
- ROR - relational operator replacement
- SCD - super calling deletion
- SCI - super calling insert
- SIR - slice index remove
Experimental mutation operators:
- CDI - classmethod decorator insertion
- OIL - one iteration loop
- RIL - reverse iteration loop
- SDI - staticmethod decorator insertion
- SDL - statement deletion
- SVD - self variable deletion
- ZIL - zero iteration loop
Licensed under the Apache License, Version 2.0. See LICENSE file.
MutPy was developed as part of engineer's and master’s thesis at Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology.