You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

An Evaluation of Monte Carlo-Based Hyper-Heuristic for Interaction Testing of Industrial Embedded Software Applications

Authors:

Bestoun Ahmed , Eduard Paul Enoiu, Wasif Afzal, Kamal Zamli

Publication Type:

Journal article

Venue:

Soft Computing Journal: A Fusion of Foundations, Methodologies and Applications


Abstract

Hyper-heuristic is a new methodology for the adaptive hybridization of meta-heuristic algorithms to derive a general algorithm for solving optimization problems. This work focuses on the selection type of hyper-heuristic, called the Exponential Monte Carlo with Counter (EMCQ). Current implementations rely on the memory-less selection that can be counterproductive as the selected search operator may not (historically) be the best performing operator for the current search instance. Addressing this issue, we propose to integrate the memory into EMCQ for combinatorial t-wise test suite generation using reinforcement learning based on the Q-learning mechanism, called Q-EMCQ. The limited application of combinatorial test generation on industrial programs can impact the use of such techniques as Q-EMCQ. Thus, there is a need to evaluate this kind of approach against relevant industrial software, with a purpose to show the degree of interaction required to cover the code as well as finding faults. We applied Q-EMCQ on 37 real-world industrial programs written in Function Block Diagram (FBD) language, which are used for developing a train control management system at Bombardier Transportation Sweden AB. The results of this study show that Q-EMCQ is an efficient technique for test case generation. Additionally, unlike the t-wise test suite generation, which deals with the minimization problem, we have also subjected Q-EMCQ to a maximization problem involving the general module clustering to demonstrate the effectiveness of our approach. The results show the Q-EMCQ is also capable of outperforming the original EMCQ as well as several recent meta/hyper-heuristic including Modified Choice Function (MCF), Tabu High Level Hyper-Heuristic(HHH), Teaching Learning based Optimization (TLBO), Sine Cosine Algorithm (SCA) and Symbiotic Optimization Search (SOS) in clustering quality within comparable execution time.

Bibtex

@article{Ahmed5759,
author = {Bestoun Ahmed and Eduard Paul Enoiu and Wasif Afzal and Kamal Zamli},
title = {An Evaluation of Monte Carlo-Based Hyper-Heuristic for Interaction Testing of Industrial Embedded Software Applications},
volume = {1},
pages = {1--20},
month = {March},
year = {2020},
journal = {Soft Computing Journal: A Fusion of Foundations, Methodologies and Applications},
url = {http://www.es.mdh.se/publications/5759-}
}