The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Le´vy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95 confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95 confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90 confidence level.
@cite_25 @cite_1 developed a simulated annealing-based strategy for supporting the construction of a uniform and variable-strength @math test suite. A large random search space is generated in the implementation. When the algorithm iterates, the strategy chooses better test cases to construct the final test suite using the binary search process and a transformation equation. The search space is transformed from one state to another according to a probability equation. The results of the study are mainly concerned with the interaction strengths of two and three @cite_1 .
@cite_1 @cite_25
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Interaction testing (also called combinatorial testing) is an cost-effective test generation technique in software testing. Most research work focuses on finding effective approaches to build optimal t-way interaction test suites. However, the strength of different factor sets may not be consistent due to the practical test requirements. To solve this problem, a variable strength combinatorial object and several approaches based on it have been proposed. These approaches include simulated annealing (SA) and greedy algorithms. SA starts with a large randomly generated test suite and then uses a binary search process to find the optimal solution. Although this approach often generates the minimal test suites, it is time consuming. Greedy algorithms avoid this shortcoming but the size of generated test suites is usually not as small as SA. In this paper, we propose a novel approach to generate variable strength interaction test suites (VSITs). In our approach, we adopt a one-test-at-a-time strategy to build final test suites. To generate a single test, we adopt ant colony system (ACS) strategy, an effective variant of ant colony optimization (ACO). In order to successfully adopt ACS, we formulize the solution space, the cost function and several heuristic settings in this framework. We also apply our approach to some typical inputs. Experimental results show the effectiveness of our approach especially compared to greedy algorithms and several existing tools. 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. Abstract : Testing is an expensive but essential part of any software project. Having the right methods to detect faults is a primary factor for success in the software industry. Component based systems are problematic because they are prone to unexpected interaction faults, yet these may be left undetected by traditional testing techniques. In all but the smallest of systems, it is not possible to test every component interaction. One can use a reduced test suite that guarantees to include a defined subset of interactions instead. A well studied combinatorial object, the covering array, can be used to achieve this goal. Constructing covering arrays for a specific software system is not always simple and the resulting object may not closely mirror the real test environment. Not only are new methods for building covering arrays needed, but new tools to support these are required as well. Our aim is to develop methods for building smaller test suites that provide stronger interaction coverage, while retaining the flexibility required in a practical test suite. We combine ideas from combinatorial design theory, computational search, statistical design of experiments and software engineering. We begin with a description of a framework for greedy algorithms that has formed the basis for several published methods and a widely used commercial tool. We compare this with a meta-heuristic search algorithm, simulated annealing. The results suggest that simulated annealing is more effective at finding smaller test suites, and in some cases improves on combinatorial methods as well. We then develop a mathematical model for variable strength interaction testing. This allows us to balance the cost and the time of testing by targeting individual subsets of components. We present construction techniques using meta-heuristic search and provide the first known bounds for objects of this type. We end by presenting some new cut-and-paste techniques that merge recursive combinatorial constructions Context: Although useful, AI-based variable strength t-way strategies are lacking in terms of the support for high interaction strength. Additionally, most AI-based strategies generally do not address the support for constraints. Addressing the aforementioned issues, this paper elaborates the design, implementation, and evaluation of a novel variable-strength-based on harmony search algorithm, called Harmony Search Strategy (HSS). Objective: The objective of this work is to investigate the adoption of harmony search algorithm for constructing variable-strength t-way strategy. Method: Implemented in Java, HSS integrates the harmony search algorithm as parts of its search engine. Result: Benchmarking results demonstrate that HSS gives competitive results against most existing AI-based (and pure computational) counterparts. However, unlike other AI-based counterparts, HSS addresses the support for high interaction strength and permits the support for constraints. Conclusion: AI-based t-way strategies tend to outperform the pure computational-based strategies in terms of test size.