XCSF for Automatic Test Case Prioritization

Lukas Rosenbauer, Anthony Stein, David Pätzel, Jörg Hähner

2020

Abstract

Testing is a crucial part in the development of a new product. Due to the change from manual testing to automated testing, companies can rely on a higher number of tests. There are certain cases such as smoke tests where the execution of all tests is not feasible and a smaller test suite of critical test cases is necessary. This prioritization problem has just gotten into the focus of reinforcement learning. A neural network and an XCS classifier system have been applied to this task. Another evolutionary machine learning approach is the XCSF which produces, unlike XCS, continuous outputs. In this work we show that XCSF is superior to both the neural network and XCS for this problem.

Paper Citation

in Harvard Style

Rosenbauer L., Stein A., Pätzel D. and Hähner J. (2020). XCSF for Automatic Test Case Prioritization. In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA; ISBN 978-989-758-475-6, SciTePress, pages 49-58. DOI: 10.5220/0010105700490058

in Bibtex Style

@conference{ecta20,
author={Lukas Rosenbauer and Anthony Stein and David Pätzel and Jörg Hähner},
title={XCSF for Automatic Test Case Prioritization},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA},
year={2020},
pages={49-58},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010105700490058},
isbn={978-989-758-475-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA
TI - XCSF for Automatic Test Case Prioritization
SN - 978-989-758-475-6
AU - Rosenbauer L.
AU - Stein A.
AU - Pätzel D.
AU - Hähner J.
PY - 2020
SP - 49
EP - 58
DO - 10.5220/0010105700490058
PB - SciTePress