Improving Proceeding Test Case Prioritization with Learning Software Agents

Sebastian Abele, Peter Göhner

2014

Abstract

Test case prioritization is an important technique to improve the planning and management of a system test. The system test itself is an iterative process, which accompanies a software system during its whole life cycle. Usually, a software system is altered and extended continuously. Test case prioritization algorithms find and order the most important test cases to increase the test efficiency in the limited test time. Generally, the knowledge about a system’s characteristics grows throughout the development. With better experience and more empirical data, the test case prioritization can be optimized to rise the test efficiency. This article introduces a learning agent-based test case prioritization system, which improves the prioritization automatically by drawing conclusions from actual test results.

Paper Citation

in Harvard Style

Abele S. and Göhner P. (2014). Improving Proceeding Test Case Prioritization with Learning Software Agents . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-016-1, pages 293-298. DOI: 10.5220/0004920002930298

in Bibtex Style

@conference{icaart14,
author={Sebastian Abele and Peter Göhner},
title={Improving Proceeding Test Case Prioritization with Learning Software Agents},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2014},
pages={293-298},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004920002930298},
isbn={978-989-758-016-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Improving Proceeding Test Case Prioritization with Learning Software Agents
SN - 978-989-758-016-1
AU - Abele S.
AU - Göhner P.
PY - 2014
SP - 293
EP - 298
DO - 10.5220/0004920002930298