Loreto Gonzalez-Hernandez

Construction of mixed covering arrays of variable strength using a tabu search approach

The development of a new system involves extensive tests on the software functionality in order to identify possible failures. Also, a system already built requires a fine tuning of its configurable options to give the best performance in the environment it is going to work. Both cases require a finite set of tests that avoids testing all the possible combinations (which is time consuming); to this situation Mixed Covering Arrays (MCAs) are a feasible alternative. MCAs are combinatorial structures represented as matrices having a test case per row. MCAs are small, in comparison with brute force, and guarantees a level of interaction among the parameters involved (a difference with random testing). We present a Tabu Search (TS) algorithm to construct MCAs; the novelty in the algorithm is a mixture of three neighborhood functions. We also present a new benchmark for the MCAs problem. The experimental evidence showed that the TS algorithm improves the results obtained by other approaches reported in the literature, finding the optimal solution in some the solved cases.

Simulated Annealing for Constructing Mixed Covering Arrays

Combinatorial testing is a method that can reduce costs and increase the effectiveness of software testing for many applications. It is based on constructing test-suites of economical size, which provide coverage of the most prevalent configurations of parameters. Mixed Covering Arrays (MCAs) are combinatorial structures which can be used to represent these test-suites. This paper presents a new Simulated Annealing (SA) algorithm for Constructing MCAs. This algorithm incorporates several distinguishing features including an efficient heuristic to generate good quality initial solutions, a compound neighborhood function which carefully combines two designed neighborhoods and a fine-tuned cooling schedule. The experimental evidence showed that our SA algorithm improves the obtained results by other approaches reported in the literature, finding the optimal solution in some of the solved cases.

CONSTRUCTION OF MIXED COVERING ARRAYS OF STRENGTHS 2 THROUGH 6 USING A TABU SEARCH APPROACH

The development of a new software system involves extensive tests of the software functionality in order to identify possible failures. Also, a software system already built requires a fine tuning of its configurable options to give the best performance in the environment where it is going to work. Both cases require a finite set of tests that avoids testing all the possible combinations (which is time consuming); to this situation mixed covering arrays (MCAs) are a feasible alternative. MCAs are combinatorial structures having a case per row. MCAs are small, in comparison with exhaustive search, and guarantee a level of interaction among the involved parameters (a difference with random testing). We present a tabu search algorithm (TSA) for the construction of MCAs. Also, we report the fine tuning process used to identify the best parameter values for TSA. The analyzed TSA parameters were three different initialization functions, five different tabu list sizes and the mixture of four neighborhood functio...

Metaheuristic approach for constructing functional test-suites

Today, software systems are complex and have many possible configurations. A deficient software testing process often leads to unfortunate consequences, including data losses, large economic losses, security breaches, and even bodily harm. Thus, the problem of performing effective and economical testing is a key issue. Combinatorial testing is a method that can reduce cost and increase the effectiveness of software testing for many applications. It is based on constructing economical sized test-suites that provide coverage of the most prevalent configurations. Mixed covering arrays (MCAs) are combinatorial structures that can be used to represent these test-suites. MCAs are combinatorial objects represented as matrices having a test case per row. MCAs are small, in comparison to an exhaustive approach, and guarantee a level of interaction coverage among the parameters involved. This study presents a metaheuristic approach based on a simulated annealing (SA) algorithm for constructing MCAs. This algorithm incorporates several distinguishing features, including an efficient heuristic to generate good quality initial solutions, and a compound neighbourhood function that combines two carefully designed neighbourhood functions. The experimental design involved a benchmark reported in the literature and two real cases of software components. The experimental evidence showed that the SA algorithm equals or improves the obtained results by other approaches reported in the literature, and also finds the optimal solution in some of the solved cases.

MiTS: a new approach of tabu search for constructing mixed covering arrays

Software systems have been increasingly used by our society, so a failure in them can lead to large losses. To reduce the failures of a software it is necessary to carry out the testing process appropriately. The combinatorial testing helps in the testing process by providing structures with a test set of small size, like Mixed Covering Arrays (MCAs). However, the problem of constructing an optimal test set is an NP-complete problem leading to the development of non exhaustive approaches to solve it. This paper proposes a new approach of Tabu Search (TS) called MiTS (that stands for Mixed Tabu Search) which focuses on constructing MCAs. The approach is based on the use of a mixture of neighborhood functions and a fine tuning process to improve the performance of the TS. Experimental evidence shows a poor performance when a single neighborhood function is used. In the other side, the TS (using a mixture of neighborhood functions) is competitive in the construction of MCAs over a known benchmark reported in the literature.

An exact approach to maximize the number of wild cards in a covering array

Covering Arrays CA(N;t,k,v) are combinatorial structures that can be used to define adequate test suites for software testing. The smaller a CA is, the smaller the number of test cases that will be given to test the functionality of a software component in order to identify possible failures. Due to the fact that the construction of CAs of optimal size is a highly combinatorial problem, several approximated strategies have been developed. Some constructions of these strategies can be further improved through a post optimization process. For example, the wild card profile of a CA is the set of symbols that can be modified without changing the properties that define a CA. It has been shown that some CAs can be reduced by merging rows that contain wild cards. This paper presents a Branch and Bound (BB2,8,6) different profiles can be obtained; such profiles vary in the number of wild cards and their distribution in the CA.

Construction of Orthogonal Arrays of Index Unity Using Logarithm Tables for Galois Fields

Of particular interest in this chapter are the combinatorial objects called Orthogonal Arrays (OAs). These objects have been studied given of their wide range of applications in the industry, Gopalakrishnan & Stinson (2008) present their applications in computer science; among them are in the generation of error correcting codes presented by (Hedayat et al., 1999; Stinson, 2004), or in the design of experiments for software testing as shown by Taguchi (1994).

New bounds for mixed covering arrays in t-way testing with uniform strength

ContextCombinatorial testing (CT) can increase the effectiveness of software testing by ensuring that all t-way input combinations are covered in a test suite. When software components have different input cardinalities, CT uses a mixed covering array (MCA) to represent the test suite. This study proposes a new methodology for constructing MCAs of t ? { 2 - 6 } by using Mixed-Tabu Search (MiTS) as the construction strategy. ObjectiveThe objective of this study is to significantly improve the best bounds of MCAs of t ? { 2 - 6 } with uniform strength. MethodThe proposed solution incorporates a new procedure for efficient parameter tuning where statistical testing is used to identify the setting values that significantly affect the performance of MiTS. For validation purposes, we used a robust benchmark that comprised a set of 35 instances of real cases and a set of 95 academic instances, which represented the best bounds reported previously. ResultThe experimental results showed that our MiTS-based methodology improved 93 bounds and matched 36 of them. The Wilcoxon signed-rank test demonstrated that our MiTS-based methodology significantly enhanced the best bounds of MCAs compared with those reported previously with 95% confidence. ConclusionMCAs for t-way testing with a good solution quality (in terms of test size), which involves artificial intelligence-based strategies, may be obtained by following a well-established methodology during the construction process.

An energy-oriented optimization algorithm for solving the cell assignment problem in 4G-LTE communication networks

This paper presents a novel algorithm for solving the cell assignment problem with special emphasis on energy awareness. The algorithm aims at finding the minimum number of base stations (BSs) that have to be turned on to guarantee the required service to the maximum number of users at lowest cost. The main contribution of the algorithm is the design of an effective solution that ensures an optimal assignment in a subset of base stations N ⊆ N resulting in a drastic reduction of the search space within every subset N, eliminating the exponential growth over the number of users M, i.e. reducing the complexity from O(NM) to O(1). A branch-and-bound approach has been designed to determine the optimal base station assignments. Experiments demonstrate that our solution performs as expected in terms of profit, served clients, and energy savings due to active BSs, at the expense of very reasonable execution time overhead.

MiTS in Depth: An Analysis of Distinct Tabu Search Configurations for Constructing Mixed Covering Arrays

Alan turing work is related with the first use of heuristic algorithms. His work on broking the Nazi code of the Enigma cipher was oriented by a guided search whose expected result in most of the times would be the deciphering of the codes, even though sometimes it might not work. This idea reflects the modern meaning of an heuristic, and represents the main relationship with this chapter, as it involves the use of metaheuristics to try to guide the search to find a solution faster, or a better solution of a problem. The metaheuristic is Tabu Search (TS), and it is used to solve the Mixed Covering Array Problem (MCAP). This problem focuses on the construction of optimal test sets for software testing. The metaheuristic is designed through a fine tuning process that involves the parameters: initialization function, tabu list size, stop criterion, and neighborhood functions. The contributions are: a) a more robust fine tune process to design a new TS approach; b) the analys is of parameter values of the TS; and, c) new bounds over a benchmark reported in the literature.