An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t -way test suite generation

Zamli, Kamal Z.; Din, Fakhrud; Kendall, Graham; Ahmed, Bestoun S.

doi:10.1016/j.ins.2017.03.007

Cited by 74 publications

(39 citation statements)

References 29 publications

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“…More recently, computational intelligence techniques have been used as components of general purpose methods managing low level (meta)heuristics for overall performance improvement. For example, [13] introduced a fuzzy inference selection based hyper-heuristic which mixed and controlled four search operators, each derived from a different metaheuristic to solve a computationally hard problem of t-way test suite generation. However, the aforementioned studies all focus on single objective optimisation.…”

Section: Introductionmentioning

confidence: 99%

A Learning Automata-Based Multiobjective Hyper-Heuristic

Özcan

John

2019

IEEE Trans. Evol. Computat.

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Section: Introductionmentioning

confidence: 99%

A Learning Automata-Based Multiobjective Hyper-Heuristic

Özcan

John

2019

IEEE Trans. Evol. Computat.

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“…Get immediate reward/punishment rt using Eq. 4 11 Get the maximum Q value for the next state st+1 12 Update ↵t using Eq.3 13 Update Q- Get immediate reward/punishment rt using Eq. 4 21 Get the maximum Q value for the next state st+ 22 Update ↵t using Eq.…”

Section: Q-learning Monte Carlo Hyper-heuristic Strategymentioning

confidence: 99%

An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications

et al. 2020

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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 is 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

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“…The first direction is to adopt hybrid search algorithm as an ensemble of two or more search-based algorithms. The second direction relates to the adoption of hyper-heuristic algorithms [39,40] to choose a particular heuristic for execution adaptively during run-time.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

Pairwise Test Data Generation Based On Flower Pollination Algorithm

Nasser

Alsewari

Tairan

et al. 2017

MJCS

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Owing to an exponential increase in computational time associated with increasing number of system components, exhaustive testing is increasingly become impractical. Here, many researchers opt to adopt pairwise testing to minimize the overall number of tests. Recently, many existing works are focusing on the use of Search-Based algorithms as the basis of the implementation algorithm for pairwise test suite generation; however, there is no single strategy that can be the best for all cases. Currently, researches on Flower Pollination Algorithm (FPA) are very active and its applications have been proven successful to solve many optimization problems. This paper proposes a new search-based strategy for generating the pairwise test suite, called Pairwise Flower Strategy (PairFS). The main feature of PairFS is that it is the first pairwise strategy that adopts FPA as its core implementation. To evaluate and benchmark our proposed strategy, several existing comparative experiments are adopted from the literature. The results of the experiment show that PairFS in many cases are more efficient than the existing strategies in terms of the generated pairwise test suite size. Keywords

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An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t -way test suite generation

Cited by 74 publications

References 29 publications

A Learning Automata-Based Multiobjective Hyper-Heuristic

A Learning Automata-Based Multiobjective Hyper-Heuristic

An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications

Pairwise Test Data Generation Based On Flower Pollination Algorithm

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