SBFT Tool Competition 2024 -- Python Test Case Generation Track
Abstract
Test case generation (TCG) for Python poses distinctive challenges due to the language's dynamic nature and the absence of strict type information. Previous research has successfully explored automated unit TCG for Python, with solutions outperforming random test generation methods. Nevertheless, fundamental issues persist, hindering the practical adoption of existing test case generators. To address these challenges, we report on the organization, challenges, and results of the first edition of the Python Testing Competition. Four tools, namely UTBotPython, Klara, Hypothesis Ghostwriter, and Pynguin were executed on a benchmark set consisting of 35 Python source files sampled from 7 open-source Python projects for a time budget of 400 seconds. We considered one configuration of each tool for each test subject and evaluated the tools' effectiveness in terms of code and mutation coverage. This paper describes our methodology, the analysis of the results together with the competing tools, and the challenges faced while running the competition experiments.
Keywords
Cite
@article{arxiv.2401.15189,
title = {SBFT Tool Competition 2024 -- Python Test Case Generation Track},
author = {Nicolas Erni and Al-Ameen Mohammed Ali Mohammed and Christian Birchler and Pouria Derakhshanfar and Stephan Lukasczyk and Sebastiano Panichella},
journal= {arXiv preprint arXiv:2401.15189},
year = {2024}
}
Comments
4 pages, to appear in the Proceedings of the 17th International Workshop on Search-Based and Fuzz Testing (SBFT@ICSE 2024)