ABCVS: An Artificial Bee Colony for Generating Variable T-Way Test Sets

Abstract

To achieve acceptable quality and performance of any software product, it is crucial to assess various software components in the application. There exist various softwaretesting techniques such as combinatorial testing and covering array. However, problems such as t-way combinatorial explosion is still challenging in any combinatorial testing strategy, as it takes into consideration the entire combinations of input variables. Therefore, to overcome this problem, several optimizations and metaheuristic strategies have been suggested. One of the most effective optimization algorithms based techniques is the Artificial Bee Colony (ABC) algorithm. This paper presents t-way generation strategy for both a uniform and variable strength test suite by applying the ABC strategy (ABCVS) to reduce the size of the test suite and to subsequently enhance the test suite generation interaction. To assess both the effectiveness and performance of the presented ABCVS, several experiments were conducted applying various sets of benchmarks. The results revealed that the proposed ABCVS outweigh the existing based strategies and demonstrated wider interaction between components as opposed to AI-search based and computational based strategies. The results also revealed higher prospect of ABCVS in the aspect of its effectiveness and performance as observed in the majority of case studies. 260 | P a g e www.ijacsa.thesai.org acceptable strategy, known as the Artificial Bee Colony Strategy (ABCVS) continuous to our previous research [40,41] for uniform and variable strength interaction t-way minimal test suite production. It is anticipated that ABCVS will address this problem. In fact, experimental results have revealed that the proposed ABCVS is able to support higher interaction strengths up to t = 6 compared to other Artificial Intelligence (AI)-based strategies. Furthermore, ABCVS it can compete against these other strategies in the majority of the case studies examined in the literature regarding efficiency (test suite generation) and performance (speed) and against other AI-based strategies having higher interaction strengths.The remainder of this paper is structured accordingly. Section II presents the background to this study, (i.e. CA and MCA concepts) which is followed by Section III which surveys "state of the art" testing strategies in this area. Section IV provides an overview of an ABC which is then followed by Section V describing the proposed strategy, consisting of two parts: (1) construction of the covering matrix, and (2) the proposed ABCVS. Section VI illustrates the tuning of the ABCVS parameters. Section VII evaluates the ABCVS by conducting several benchmark experiments in terms of effciency and performance alongside with statistical analysis evaluation by using Wilcoxon signed-rank test in Section VIII. Section IX discusses the advantages, limitations and threats to the validity of the approach, which followed by Section X providing overall conclusions to the study and presenting recommendations for future work. 12

Multiple Black Hole Inspired Meta-Heuristic Searching Optimization for Combinatorial Testing

“…However, all these approaches share a common factor: these approaches exploit the power of random combinatorial searching when they are integrated with heuristics for determining the t-strength covering array. The formulation of the optimization problem calls for an emphasis on two aspects: the devising of the objective function and the selection of the approach employing a pure-based approach [12] or hybrid-based approach [13]. In terms of the objective function formulation, some models use a minimization formulation, while others use a maximization formulation.…”

“…The computational comparison between the firefly meta-heuristic algorithm, genetic algorithm, and ant colony optimization revealed that the firefly algorithm has a shorter optimization time. Other models developed for solving combinatorial software testing using meta-heuristic searching include the memetic algorithm (MA) in [19], the particle swarm optimization (PSO) in [20], the artificial bee colony (ABC) and corresponding developed variants in [13], [21]- [26], the harmony search algorithm (HSA) in [27], the bat algorithm (BA) in [12], [28]- [30], and the flower pollination algorithm (FPA) in [31] and [32]. All these models are considered pure models because they utilize a common pure meta-heuristic approach.…”

“…Each of the metaheuristic searching-based approaches uses a representation related to the metaphor of the approach. For example, in the work of [21], ABC was used for t-way generation. The representation these researchers used presents the number of food sources by a test case and the fitness value by the coverage.…”

Most Valuable Player Algorithm for Solving Minimum Vertex Cover Problem

“…In general, many heuristic and metaheuristic algorithms were used to solve many optimization problems; such as Minimum Vertex Cover Problem (MVCP), Traveling Salesman Problem (TSP), 15 puzzle problem, task scheduling, software testing, and non-optimization problems [1][2][3][4]. Examples of heuristic algorithms are A* heuristic search algorithm and iterative deepening A* (IDA*) heuristic search algorithm [5].…”

“…Equation 2is used to calculate these probabilities [25]. (2) where g a and g b are any competed groups, k is a constant and is the normalized size of g a 's solutions sizes that is calculated as in (3) [25]. 3On the consequence of the winner group determination, the vertices values of g i solution are updated using either the equation in line 24 or in line 26 [25].…”