Papers by Yazan A AlSariera
IEEE Access, 2020
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Owing to exponential growth of software lines of codes (LOC)s, testing becomes painstakingly diff... more Owing to exponential growth of software lines of codes (LOC)s, testing becomes painstakingly difficult activities. Test engineers are often under pressure to test more and more LOCs yet within the same targeted deadline. For this reason, efficient testing strategy is required. Pairwise testing is amongst the most common strategies for minimizing and sampling of tests for testing consideration. Recently, there are growing interests for adapting optimization algorithms as the basis of the newly developed strategies. Complementing the existing work, we propose a novel design and implementation of Bat-inspired algorithm (BA) for pairwise strategy, called Bat-inspired pairwise testing strategy (BPTS). Based on the benchmarking results, BPTS outperforms most existing strategies in terms of the generated test suite size. BPTS serves as our research vehicle to investigate the effectiveness of Bat-inspired algorithm for pairwise test generation, which is going to be helpful to reduce the time and cost of software testing by reducing the number of test cases.
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Combinatorial interaction testing is a practical approach aims to detect defects due to unwanted ... more Combinatorial interaction testing is a practical approach aims to detect defects due to unwanted and faulty interactions. Here, a set of sampled test cases is generated based on t-way covering problem (where t indicates the interaction strength). Often, the generation process is based on a particular t-way strategy ensuring that each t-way interaction is covered at least once. Much useful progress has been achieved as plethora of t-way strategies have been developed in the literature in the last 30 years. Recently, in line with the upcoming field called Search based Software Engineering (SBSE), many newly strategies have been developed adopting specific optimization algorithm (e.g. Genetic Algorithm (GA), Ant Colony (AC), Simulated Annealling (SA), Particle Swarm Optimization, and Harmony Search Algorithm (HS) as their basis in an effort to generate the most optimal solution. Although useful, strategies based on the aforementioned optimization algorithms are not without limitation. Specifically, these algorithms require extensive tuning before optimal solution can be obtained. In many cases, improper tuning of specific parameters undesirably yields sub-optimal solution. Addressing this issue, this paper proposes the adoption of parameter free optimization algorithms as the basis of future t-way strategies. In doing so, this paper reviews two existing parameter free optimization algorithms involving Teaching Learning Based Optimization (TLBO) and Fruitfly Optimization Algorithm (FOA) in an effort to promote their adoption for CIT.
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Combinatorial Interaction testing (or termed t-way testing) is a useful g strategy aimed at sampl... more Combinatorial Interaction testing (or termed t-way testing) is a useful g strategy aimed at sampling a set of test cases from a large search space. As part of the strategy implementation, researchers have started to turn into meta-heuristic algorithms in line with the emergence of the new field called Search based Software Engineering. Complementing in the aforementioned respect, this paper discusses the adoption of Bat Algorithm as the basis of t-way strategy. Our experience has been promising as our strategy has managed to outperform many existing work, where the results of the experiment shows that BTS is superior in term of the solution quality.
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This paper describes the generation oft-way test suite using the Late Acceptance Hill Climbing ba... more This paper describes the generation oft-way test suite using the Late Acceptance Hill Climbing based Strategy (LAHC) in the presence of constraints. Our benchmarking results have been promising as LAHC gives competitive results in all constraints configurations considered.
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IEEE Xplore, Dec 18, 2014
Optimization problem relates to finding the best solution from all feasible solutions. Over the l... more Optimization problem relates to finding the best solution from all feasible solutions. Over the last 30 years, many meta-heuristic algorithms have been developed in the literature including that of Simulated Annealing (SA), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Harmony Search Algorithm (HS) to name a few. In order to help engineers make a sound decision on the selection amongst the best meta-heuristic algorithms for the problem at hand, there is a need to assess the performance of each algorithm against common case studies. Owing to the fact that they are new and much of their relative performance are still unknown (as compared to other established meta-heuristic algorithms), Bacterial Foraging Optimization Algorithm (BFO) and Bat Algorithm (BA) have been adopted for comparison using the 12 selected benchmark functions. In order to ensure fair comparison, both BFO and BA are implemented using the same data structure and the same language and running in the same platform (i.e. Microsoft Visual C# with .Net Framework 4.5). We found that BFO gives more accurate solution as compared to BA (with the same number of iterations). However, BA exhibits faster convergence rate.
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Papers by Yazan A AlSariera