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

Muhammad Zohaib Iqbal, Andrea Arcuri, Lionel Briand

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationSearch Based Software Engineering - 4th International Symposium, SSBSE 2012, Proceedings
Pages136-151
Number of pages16
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event4th International Symposium on Search Based Software Engineering, SSBSE 2012 - Riva del Garda, Italy
Duration: 28 Sep 201230 Sep 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7515 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Symposium on Search Based Software Engineering, SSBSE 2012
Country/TerritoryItaly
CityRiva del Garda
Period28/09/1230/09/12

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