Combinatorial Test Generation using Improved Harmony Search Algorithm

Bao, Xiaoan; Liu, Shuhan; Zhang, Na; Dong, Meng

doi:10.14257/ijhit.2015.8.9.13

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…In 2015, Bao et al [23] proposed an improved HS algorithm in which the harmony memory is initialised using a greedy algorithm. In addition, the parameters that control the search are adapted during the search.…”

Section: Related Search-based Methodsmentioning

confidence: 99%

Search‐based software engineering for constructing covering arrays

2018

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Search-based software engineering involves the application of optimisation methods to solve software engineering problems. One of the most significant difficulties in testing software systems is the effort needed to build the test suites required to validate a software system, which efficiently exposes faults. Given the importance of the software testing stage, a specific sub-area known as search-based software testing has become relevant in recent years. In this work, a search-based software testing algorithm for constructing covering arrays is proposed. A covering array is a combinatorial structure that can be used as a set of test cases. By utilising this algorithm, the authors reduce the size of 893 test suites.

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Section: Related Search-based Methodsmentioning

confidence: 99%

Search‐based software engineering for constructing covering arrays

2018

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“…CAs are simulated annealing [77]- [80], tabu search [44], [81]- [83], memetic algorithms [84], genetic algorithms [81], [85], [86], ant colony [87], particle swarm optimization [88]- [90], harmony search [91], and cuckoo search [92]. Some of these approaches have been implemented following a parallel programming paradigm, like the parallel simulated annealing approach to construct CAs implemented in [93].…”

Section: Some Metaheuristics That Have Been Used To Constructmentioning

confidence: 99%

Methods to Construct Uniform Covering Arrays

2019

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Uniform covering arrays are covering arrays in which every column has the same alphabet. In recent years, a number of methods to construct such arrays have been developed. Here, we review several of these methods organizing them into six classes: algebraic, recursive, exact, greedy, metaheuristic, and transformations. The objective of this paper is to highlight the strategy of some representative algorithms of each class. Most of the reviewed methods are accompanied by examples and/or pseudocodes. This paper ends with a discussion about the general strengths and weaknesses of each class of methods.INDEX TERMS Covering arrays, uniform covering arrays, methods to construct covering arrays.

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“…The main initialization functions used in state-of-the-art methods are as follows: a) random matrix initialization [ 21 , 23 , 26 , 27 ], b) initialization with a balanced number of symbols per column [ 27 ], c) initialization through row augmentation [ 39 ], d) initialization based on submatrices [ 40 ], and e) initialization based on greedy strategies [ 41 , 42 ]. The four first strategies do not consider the number of missing t -wise combinations in the construction of the initial matrix.…”

Section: Related Workmentioning

confidence: 99%

Optimal shortening of uniform covering arrays

et al. 2017

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Software test suites based on the concept of interaction testing are very useful for testing software components in an economical way. Test suites of this kind may be created using mathematical objects called covering arrays. A covering array, denoted by CA(N; t, k, v), is an N × k array over with the property that every N × t sub-array covers all t-tuples of at least once. Covering arrays can be used to test systems in which failures occur as a result of interactions among components or subsystems. They are often used in areas such as hardware Trojan detection, software testing, and network design. Because system testing is expensive, it is critical to reduce the amount of testing required. This paper addresses the Optimal Shortening of Covering ARrays (OSCAR) problem, an optimization problem whose objective is to construct, from an existing covering array matrix of uniform level, an array with dimensions of (N − δ) × (k − Δ) such that the number of missing t-tuples is minimized. Two applications of the OSCAR problem are (a) to produce smaller covering arrays from larger ones and (b) to obtain quasi-covering arrays (covering arrays in which the number of missing t-tuples is small) to be used as input to a meta-heuristic algorithm that produces covering arrays. In addition, it is proven that the OSCAR problem is NP-complete, and twelve different algorithms are proposed to solve it. An experiment was performed on 62 problem instances, and the results demonstrate the effectiveness of solving the OSCAR problem to facilitate the construction of new covering arrays.

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Combinatorial Test Generation using Improved Harmony Search Algorithm

Abstract: Abstract

Cited by 6 publications

References 14 publications

Search‐based software engineering for constructing covering arrays

Search‐based software engineering for constructing covering arrays

Methods to Construct Uniform Covering Arrays

Optimal shortening of uniform covering arrays

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