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Adaptive Knowledge-Supported Testing: An Approach for Improving Testing Efficiency

Authors:
Philipp Helle
Wladimir Schamai

Keywords: Testing, Adaptive Testing, Machine Learning

Abstract:
This paper introduces a new method for automatic test parameter generation that has been named adaptive knowledge-supported testing. The approach uses a combination of random testing for test parameter generation and machine learning and data mining techniques to optimize these test parameters based on the results from previous tests. The goal is to enable efficient testing of complex systems which cannot be tested exhaustively anymore due to the huge number of possible input combinations. The paper provides a description of the method and also results from the evaluation of a first proof-of-concept demonstrator that has been implemented to validate the method.

Pages: 1 to 6

Copyright: Copyright (c) IARIA, 2014

Publication date: October 12, 2014

Published in: conference

ISSN: 2308-4316

ISBN: 978-1-61208-370-4

Location: Nice, France

Dates: from October 12, 2014 to October 16, 2014