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
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7515)


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.


Evolutionary Algorithm Fault Detection Unify Modeling Language Hybrid Strategy System Under Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Muhammad Zohaib Iqbal
    • 1
    • 2
  • Andrea Arcuri
    • 1
  • Lionel Briand
    • 1
    • 3
  1. 1.Simula Research LaboratoryCertus Center for V & VLysakerNorway
  2. 2.Department of InformaticsUniversity of OsloNorway
  3. 3.SnT CenterUniversity of LuxembourgLuxembourg

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