Introduction

Philosophy

Pynguin was developed with a few PEP 20 idioms in mind.

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Readability counts.

We furthermore like the thoughts and ideas from Robert C. Martin’s Clean Code. All contributions to Pynguin should keep these important rules in mind.

Publications on Pynguin

S. Lukasczyk, F. Kroiß, and G. Fraser. An Empirical Study of Automated Unit Test Generation for Python. Empirical Software Engineering 28, 36 (2023). DOI 10.1007/s10664-022-10248-w. arXiv:2111.05003

BibTeX entry:

@Article{journals/ese/LukasczykKF23,
  author    = {Stephan Lukasczyk and Florian Kroi{\ss} and Gordon Fraser},
  title     = {An empirical study of automated unit test generation for python},
  journal   = {Empirical Software Engineering},
  volume    = {28},
  number    = {2},
  year      = {2023},
  doi       = {10.1007/s10664-022-10248-w},
}

S. Lukasczyk and G. Fraser. Pynguin: Automated Unit Test Generation for Python. In Proceedings of the 44th International Conference on Software Engineering Companion. ACM, 2022. DOI: 10.1145/3510454.3516829. arXiv:2202.05218

BibTeX entry:

@inproceedings{DBLP:conf/icse/LukasczykF22,
  author    = {Stephan Lukasczyk and Gordon Fraser},
  title     = {Pynguin: Automated Unit Test Generation for Python},
  booktitle = {44th {IEEE/ACM} International Conference on Software Engineering:
               Companion Proceedings, {ICSE} Companion 2022, Pittsburgh, PA, USA,
               May 22-24, 2022},
  pages     = {168--172},
  publisher = {{ACM/IEEE}},
  year      = {2022},
  doi       = {10.1145/3510454.3516829},
}

S. Lukasczyk, F. Kroiß, and G. Fraser. Automated Unit Test Generation for Python. In Proceedings of the 12th Symposium on Search-based Software Engineering. Lecture Notes in Computer Science, vol. 12420, pp. 9–24. Springer, 2020. DOI: 10.1007/978-3-030-59762-7_2. arXiv:2007.14049

BibTeX entry:

@InProceedings{conf/ssbse/LukasczykKF20,
  author    = {Stephan Lukasczyk and Florian Kroi{\ss} and Gordon Fraser},
  title     = {Automated Unit Test Generation for Python},
  booktitle = {Proceedings of the 12th Symposium on Search-based Software Engineering (SSBSE 2020, Bari, Italy, October 7–8)},
  year      = {2020},
  publisher = {Springer},
  series    = {Lecture Notes in Computer Science},
  volume    = {12420},
  pages     = {9--24},
  doi       = {10.1007/978-3-030-59762-7\_2},
}

Theses on Pynguin

This is an (incomplete) list of theses done on Pynguin.

S. Labrenz: Using Checked Coverage as Fitness Function for Test Generation in Python. Master Thesis. University of Passau, 2022.

Provides checked coverage both as a fitness function for test generation as well as an optimisation criterion for assertion minimisation.
M. Königseder: DeepTyper für Python und der Einfluss von Typvorhersagen auf die automatische Testgenerierung. Bachelor Thesis. University of Passau, 2022.
M. Reichenberger: Measuring Oracle Quality in Python. Master Thesis. University of Passau, 2022.

Although this work did not directly contribute to Pynguin, its implementation of Checked Coverage was the basis for the thesis of S. Labrenz.
F. Straubinger: Mutation Analysis to Improve the Generation of Assertions for Automatically Generated Python Unit-tests. Bachelor Thesis. University of Passau, 2021.

Provided the mutation-based assertion generation for improved regression tests.
L. Steffens: Seeding Strategies in Search-Based Unit Test Generation for Python. Bachelor Thesis. University of Passau, 2021.

Provided the dynamic seeding as well as the seeding from existing test cases to Pynguin.
F. Kroiß: Automatic Generation of Whole Test Suites in Python. Bachelor Thesis. University of Passau, 2020.

Provided the whole-suite test generation algorithm as well as large parts of the core parts of Pynguin, e.g., instrumentation, test-case representation, and execution.
C. Frädrich: Combining Test Generation and Type Inference for Testing Dynamically Typed Programming Language. Master Thesis. University of Passau, 2019.

Implemented a proof-of-concept using a Randoop-like test-generation algorithm and incorporated several ideas for type inference. Although this work was done before Pynguin was actually startet, it is the foundation and proof-of-concept that test generation for Python was actually a feasible goal. Thus, we consider it as the seminal starting point of this endeavour.

MIT License

Pynguin is released under the terms of the MIT License.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.