scholarly journals Correction to: Factorials Experiments, Covering Arrays, and Combinatorial Testing

2021 ◽  
Author(s):  
Raghu N. Kacker ◽  
D. Richard Kuhn ◽  
Yu Lei ◽  
Dimitris E. Simos
Keyword(s):  
Combinatorial Testing ◽  
Covering Arrays

Factorials Experiments, Covering Arrays, and Combinatorial Testing

2021 ◽  
Author(s):  
Raghu N. Kacker ◽  
D. Richard Kuhn ◽  
Yu Lei ◽  
Dimitris E. Simos
Keyword(s):  
Combinatorial Testing ◽  
Covering Arrays

Research Notes: Binary Test-Suites Using Covering Arrays

2018 ◽  
Vol 28 (09) ◽  
pp. 1321-1337 ◽  
Author(s):  
Jose Torres-Jimenez ◽  
Himer Avila-George ◽  
Ezra Federico Parra-González
Keyword(s):  
Software Testing ◽  
Test Generation ◽  
Software Systems ◽  
Combinatorial Testing ◽  
Covering Array ◽  
Covering Arrays ◽  
Almost All ◽  
Generation Problem ◽  
Test Suites

Software testing is an essential activity to ensure the quality of software systems. Combinatorial testing is a method that facilitates the software testing process; it is based on an empirical evidence where almost all faults in a software component are due to the interaction of very few parameters. The test generation problem for combinatorial testing can be represented as the construction of a matrix that has certain properties; typically this matrix is a covering array. Covering arrays have a small number of tests, in comparison with an exhaustive approach, and provide a level of interaction coverage among the parameters involved. This paper presents a repository that contains binary covering arrays involving many levels of interaction. Also, it discusses the importance of covering array repositories in the construction of better covering arrays. In most of the cases, the size of the covering arrays included in the repository reported here are the best upper bounds known, moreover, the files containing the matrices of these covering arrays are available to be downloaded. The final purpose of our Binary Covering Arrays Repository (BCAR) is to provide software testing practitioners the best-known binary test-suites.

Utilizing Combinatorial Testing on Discrete Event Simulation Models for Sustainable Manufacturing

2009 ◽  
Author(s):  
Bjo¨rn Johansson ◽  
Raghu Kacker ◽  
Ru¨ediger Kessel ◽  
Charles McLean ◽  
Ram Sriram
Keyword(s):  
Discrete Event Simulation ◽  
Manufacturing Systems ◽  
Discrete Event ◽  
Sustainable Manufacturing ◽  
Optimal Solution ◽  
Computation Time ◽  
Simulation Models ◽  
Combinatorial Testing ◽  
Covering Arrays ◽  
Event Simulation

This paper describes how combinatorial testing using covering arrays can be implemented to optimize discrete event simulation models of manufacturing systems for measures of sustainability. Discrete event simulation models often have hundreds of parameters and many test values for each parameter. Generally the interactions between the parameter-values are not well understood; this can lead to sub-optimization of the system. Most optimization engines and software for discrete event simulation packages use full factorial designs, which require many runs and hence a lot of computation time. In this paper we introduce combinatorial testing using a test-suite generation tool called NIST-ACTS (National Institute of Standards and Technology - Advanced Combinatorial Test Suites) to dramatically decrease the number of runs required to detect the interactions and determine an optimal solution.

New optimal covering arrays using an orderly algorithm

2018 ◽  
Vol 10 (01) ◽  
pp. 1850011 ◽  
Author(s):  
Idelfonso Izquierdo-Marquez ◽  
Jose Torres-Jimenez
Keyword(s):  
Small Subset ◽  
Computational Results ◽  
Covering Array ◽  
Covering Arrays ◽  
Computational Search ◽  
Minimum Number ◽  
Orderly Algorithm

A covering array [Formula: see text] is an [Formula: see text] array such that every [Formula: see text] subarray covers at least once each [Formula: see text]-tuple from [Formula: see text] symbols. For given [Formula: see text], [Formula: see text], and [Formula: see text], the minimum number of rows for which exists a CA is denoted by [Formula: see text] (CAN stands for Covering Array Number) and the corresponding CA is optimal. Optimal covering arrays have been determined algebraically for a small subset of cases; but another alternative to find CANs is the use of computational search. The present work introduces a new orderly algorithm to construct non-isomorphic covering arrays; this algorithm is an improvement of a previously reported algorithm for the same purpose. The construction of non-isomorphic covering arrays is used to prove the nonexistence of certain covering arrays whose nonexistence implies the optimality of other covering arrays. From the computational results obtained, the following CANs were established: [Formula: see text] for [Formula: see text], [Formula: see text], and [Formula: see text]. In addition, the new result [Formula: see text], and the already known existence of [Formula: see text], imply [Formula: see text].

scholarly journals Refining the In-Parameter-Order Strategy for Constructing Covering Arrays

2008 ◽  
Vol 113 (5) ◽  
pp. 287 ◽  
Author(s):  
Michael Forbes ◽  
Jim Lawrence ◽  
Yu Lei ◽  
Raghu N. Kacker ◽  
D. Richard Kuhn
Keyword(s):  
Covering Arrays
Sign in / Sign up

Export Citation Format

Share Document