Bestoun S. Ahmed

A variable strength interaction test suites generation strategy using Particle Swarm Optimization

This paper highlights a novel strategy for generating variable-strength (VS) interaction test suites, called VS Particle Swarm Test Generator (VS-PSTG). As the name suggests, VS-PSTG adopts Particle Swarm Optimization to ensure optimal test size reduction. To determine its efficiency in terms of the size of the generated test suite, VS-PSTG was subjected to well-known benchmark configurations. Comparative results indicate that VS-PSTG gives competitive results as compared to existing strategies. An empirical case study was conducted on a non-trivial software system to show the applicability of the strategy and to determine the effectiveness of the generated test suites to detect faults.

Application of Particle Swarm Optimization to uniform and variable strength covering array construction

Recently, researchers have started to explore the use of Artificial Intelligence (AI)-based algorithms as t-way (where t indicates the interaction strength) testing strategies. Many AI-based strategies have been developed, such as Ant Colony, Simulated Annealing, Genetic Algorithm, and Tabu Search. Although useful, most existing AI-based t-way testing strategies adopt complex search processes and require heavy computations. For this reason, existing AI-based t-way testing strategies have been confined to small interaction strengths (i.e., t@?3) and small test configurations. Recent studies demonstrate the need to go up to t=6 in order to capture most faults. In this paper, we demonstrate the effectiveness of our proposed Particle Swarm-based t-way Test Generator (PSTG) for generating uniform and variable strength covering arrays. Unlike other existing AI-based t-way testing strategies, the lightweight computation of the particle swarm search process enables PSTG to support high interaction strengths of up to t=6. The performance of our proposed PSTG is evaluated using several sets of benchmark experiments. Comparatively, PSTG consistently outperforms its AI counterparts and other existing testing strategies as far as the size of the array is concerned. Furthermore, our case study demonstrates the usefulness of PSTG for facilitating fault detection owing to interactions of the input components.

Achievement of minimized combinatorial test suite for configuration-aware software functional testing using the Cuckoo Search algorithm

A new approach is used to generate combinatorial test suites.Cuckoo Search application is investigated for a new type of application and case study.The strategy opens a new approach in Search Based Software Testing (SBST).The strategy is evaluated through different benchmarks and it is able to get comparative results.Application of combinatorial optimization is also investigated in the current paper. ContextSoftware has become an innovative solution nowadays for many applications and methods in science and engineering. Ensuring the quality and correctness of software is challenging because each program has different configurations and input domains. To ensure the quality of software, all possible configurations and input combinations need to be evaluated against their expected outputs. However, this exhaustive test is impractical because of time and resource constraints due to the large domain of input and configurations. Thus, different sampling techniques have been used to sample these input domains and configurations. ObjectiveCombinatorial testing can be used to effectively detect faults in software-under-test. This technique uses combinatorial optimization concepts to systematically minimize the number of test cases by considering the combinations of inputs. This paper proposes a new strategy to generate combinatorial test suite by using Cuckoo Search concepts. MethodCuckoo Search is used in the design and implementation of a strategy to construct optimized combinatorial sets. The strategy consists of different algorithms for construction. These algorithms are combined to serve the Cuckoo Search. ResultsThe efficiency and performance of the new technique were proven through different experiment sets. The effectiveness of the strategy is assessed by applying the generated test suites on a real-world case study for the purpose of functional testing. ConclusionResults show that the generated test suites can detect faults effectively. In addition, the strategy also opens a new direction for the application of Cuckoo Search in the context of software engineering.

PSTG: A T-Way Strategy Adopting Particle Swarm Optimization

As an activity to ensure quality and conformance, testing is one of the most important activities in any software or hardware product development cycle. Often, the challenge in testing is that the system may support a wide range of configurations. Ideally, it is desirable to test all of these configurations exhaustively. However, exhaustive testing is practically impossible due to time and resource limitations. To address this issue, there is a need for a sampling strategy that can select a subset of inputs as test data from an inherently large search space. Recent findings demonstrate that t-way interaction testing strategies based on artificial intelligence (i.e. where t indicates interaction strength) have been successful to obtain a near optimal solution resulting into smaller test set to be considered. Motivated by such findings, we have developed a new test generation strategy, called Particle Swarm Test Generator (PSTG). In this paper, we discuss the design of PSTG and demonstrate our preliminary test size reduction results against other competing t-way strategies including IPOG, WHITCH, Jenny, TConfig, and TVG.

A new multiobjective performance criterion used in PID tuning optimization algorithms.

In PID controller design, an optimization algorithm is commonly employed to search for the optimal controller parameters. The optimization algorithm is based on a specific performance criterion which is defined by an objective or cost function. To this end, different objective functions have been proposed in the literature to optimize the response of the controlled system. These functions include numerous weighted time and frequency domain variables. However, for an optimum desired response it is difficult to select the appropriate objective function or identify the best weight values required to optimize the PID controller design. This paper presents a new time domain performance criterion based on the multiobjective Pareto front solutions. The proposed objective function is tested in the PID controller design for an automatic voltage regulator system (AVR) application using particle swarm optimization algorithm. Simulation results show that the proposed performance criterion can highly improve the PID tuning optimization in comparison with traditional objective functions.

An efficient strategy for covering array construction with fuzzy logic-based adaptive swarm optimization for software testing use

New approach is used to generate combinatorial test suitesFuzzy-based strategy developed to generate the test cases using adaptive techniqueIt is a new approach in developing Artificial Intelligent and Expert systemsThe strategy proves its efficiency, performance compared to its counterpartsThe strategy proves its effectiveness also through the case study Recent research activities have demonstrated the effective application of combinatorial optimization in different areas, especially in software testing. Covering array (CA) has been introduced as a representation of the combinations in one complete set. CAλ(N; t, k, v) is an N?×?k array in which each t-tuple for an N × t sub array occurs at least λ times, where t is the combination strength, k is the number of components (factors), and v is the number of symbols for each component (levels). Generating an optimized covering array for a specific number of k and v is difficult because the problem is a non-deterministic polynomial-time hard computational one. To address this issue, many relevant strategies have been developed, including stochastic population-based algorithms. This paper presents a new and effective approach for constructing efficient covering arrays through fuzzy-based, adaptive particle swarm optimization (PSO). With this approach, efficient covering arrays have been constructed and the performance of PSO has been improved for this type of application. To measure the effectiveness of the technique, an empirical study is conducted on a software system. The technique proves its effectiveness through the conducted case study.

An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation

Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t-way test generation strategy (where t indicates the interaction strength) including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Harmony Search (HS). Although useful, meta-heuristic algorithms that make up these strategies often require specific domain knowledge in order to allow effective tuning before good quality solutions can be obtained. Hyper-heuristics provide an alternative methodology to meta-heuristics which permit adaptive selection and/or generation of meta-heuristics automatically during the search process. This paper describes our experience with four hyper-heuristic selection and acceptance mechanisms namely Exponential Monte Carlo with counter (EMCQ), Choice Function (CF), Improvement Selection Rules (ISR), and newly developed Fuzzy Inference Selection (FIS), using the t-way test generation problem as a case study. Based on the experimental results, we offer insights on why each strategy differs in terms of its performance.

T-Way Test Data Generation Strategy Based on Particle Swarm Optimization

Due to market demands, software has grown tremendously in size and functionalities over the years. As side effects of such growth, there tend to be more and more unwanted interaction between software and system parameters. These unwanted interactions can sometimes lead to nasty and difficult bugs to detect. In order to address these issues, t-way strategies (i.e. where t indicates interaction strength) are helpful to generate a set of test cases (i.e. to form a complete suite) that cover the required interaction strength as least once from a typically large space of possible test values. In this paper, we highlight a new t-way strategy based on Particle Swarm Optimization, called PSTG. Preliminary results demonstrated that PSTG compares well against other existing t-way strategies.

Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites

The teaching learning-based optimization (TLBO) algorithm has shown competitive performance in solving numerous real-world optimization problems. Nevertheless, this algorithm requires better control for exploitation and exploration to prevent premature convergence (i.e., trapped in local optima), as well as enhance solution diversity. Thus, this paper proposes a new TLBO variant based on Mamdani fuzzy inference system, called ATLBO, to permit adaptive selection of its global and local search operations. In order to assess its performances, we adopt ATLBO for the mixed strength t-way test generation problem. Experimental results reveal that ATLBO exhibits competitive performances against the original TLBO and other meta-heuristic counterparts.

Application of combinatorial interaction design for DC servomotor PID controller tuning

Combinatorial optimization has been used in different research areas. It has been employed successfully in software testing fields to construct minimum set of combinations (i.e., in terms of size) which in turn represents the minimum number of test cases. It was also found to be a successful approach that can be applied to solve other similar problems in different fields of research. In line with this approach, this paper presents a new application of the combinational optimization in the design of PID controller for DC servomotor. The design of PID controller involves the determination of three parameters. To find optimal initial PID parameters, different tuning methods have been proposed and designed in the literature. The combinatorial design is concerned with the arrangement of finite set of elements into combinatorial set that satisfies some given constraints. Consequently, the proposed method takes the interaction of the input parameters as a constraint for constructing this combinatorial set. The generated sets are then used in the proposed tuning method. The method proved its effectiveness within a set of experiments in a simulated environment.