Combining search-based and adaptive random testing strategies for environment model-based testing of real-time embedded systems

Authors:
Muhammad Zohaib Iqbal;Andrea Arcuri;Lionel Briand
Affiliations:
Simula Research Laboratory, Certus Center for V & V, Lysaker, Norway,Department of Informatics, University of Oslo, Norway;Simula Research Laboratory, Certus Center for V & V, Lysaker, Norway;Simula Research Laboratory, Certus Center for V & V, Lysaker, Norway,SnT Center, University of Luxembourg, Luxembourg
Venue:
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
Year:
2012

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Abstract

Effective system testing of real-time embedded systems (RTES) requires a fully automated approach. One such black-box system testing approach is to use environment models to automatically generate test cases and test oracles along with an environment simulator to enable early testing of RTES. In this paper, we propose a hybrid strategy, which combines (1+1) Evolutionary Algorithm (EA) and Adaptive Random Testing (ART), to improve the overall performance of system testing that is obtained when using each single strategy in isolation. An empirical study is carried out on a number of artificial problems and one industrial case study. The novel strategy shows significant overall improvement in terms of fault detection compared to individual performances of both (1+1) EA and ART.