Abdullah B. Nasser

Sequence and sequence-less T-way test suite generation strategy based on flower pollination algorithm

In an attempt to ensure good-quality software, there is need to test all possible inputs. Owing to the fact that the exhaustive testing is hardly feasible, many software testing approaches has been proposed. Combinatorial Interaction Testing (CIT) is very promising technique to minimize the number of test cases. Although useful, most of exiting CIT strategies and tools focus on data inputs and assume “sequence-less” interactions between input parameters. However, reactive systems show sequence related behaviors and their faults may not expose if the sequence of inputs are not considered. In this paper, we propose a new t-way strategy (i.e. t refers to the degree of the combination) strategy, called Flower Strategy (FS), that addresses both sequence and sequence-less test generation. Experimental results show that FS produces test size.

Assessing optimization based strategies for t-way test suite generation: The case for flower-based strategy

Exhaustive testing is extremely difficult to perform owing to the large number of combinations. Thus, sampling and finding the optimal test suite from a set of feasible test cases becomes a central concern. Addressing this issue, the adoption of t-way testing (where t indicates the interaction strength) has come into the limelight. In order to summarize the achievements so far and facilitate future development, the main focus of this paper is, first, to present a critical comparison of adoption optimization algorithms (OA) as a basis of the t-way test suite generation strategy and, second, to propose a new t-way strategy based on Flower Pollination Algorithm, called Flower Strategy (FS). Analytical and experimental results demonstrate the applicability of FS for t-way test suite generation.

Hybrid flower pollination algorithm strategies for t-way test suite generation

The application of meta-heuristic algorithms for t-way testing has recently become prevalent. Consequently, many useful meta-heuristic algorithms have been developed on the basis of the implementation of t-way strategies (where t indicates the interaction strength). Mixed results have been reported in the literature to highlight the fact that no single strategy appears to be superior compared with other configurations. The hybridization of two or more algorithms can enhance the overall search capabilities, that is, by compensating the limitation of one algorithm with the strength of others. Thus, hybrid variants of the flower pollination algorithm (FPA) are proposed in the current work. Four hybrid variants of FPA are considered by combining FPA with other algorithmic components. The experimental results demonstrate that FPA hybrids overcome the problems of slow convergence in the original FPA and offers statistically superior performance compared with existing t-way strategies in terms of test suite size.

Parameter Free Flower Algorithm Based Strategy for Pairwise Testing

Adopted to solve optimization problems, meta-heuristic algorithms aim to judiciously explore the search space in search of the good optimal solution. As such, the effectiveness of any particular meta-heuristic algorithm is heavily dependent on their control parameters, that is, to ensure balance exploration and exploitation. In the field of t-way testing, much work has been done to adopt meta-heuristic algorithms for generating interaction test suite (where t indicates the interaction strength). In this paper, we propose an Adaptive Flower Pollination Algorithm (AFPA) for pairwise testing. Unlike the original Flower Pollination Algorithm (FPA), our AFPA removes the static probability dependency inherent in FPA (i.e. for selection of local and global search operator). Specifically, we allow a dynamic and adaptive probability instead. The experimental results show that AFPA can produce the optimum results in many cases. AFPA also demonstrates its capacity to dynamically control global and local search based on the system configuration.

Learning Cuckoo Search Strategy for t-way Test Generation

The performance of meta-heuristic algorithms highly depends on their exploitation and exploration techniques. In the past 30 years, many meta-heuristic algorithms have been developed which adopts different exploitation and exploration techniques. Several studies reported that the hybrid of meta-heuristics algorithms often perform better than its corresponding original algorithm. This paper presents a new hybrid algorithm; called Learning Cuckoo Search (LCS) strategy based on the integration student phase from Teaching Learning based Optimization (TLBO) Algorithm. To evaluate the developed algorithm, we use the problem of t-way test generation as our case study. The experiment results show that LCS has better performance as compared as to the original Cuckoo Search as well many other existing strategies.

Self-adaptive Population Size Strategy Based on Flower Pollination Algorithm for T-Way Test Suite Generation

The performance of meta-heuristic algorithms is highly dependents on the fine balance between intensification and diversification. Too much intensification may result in the quick loss of diversity and aggressive diversification may lead to inefficient search. Therefore, there is a need for proper parameter controls to balance out between intensification and diversification. The challenge here is to find the best values for the control parameters to achieve acceptable results. Many studies focus on tuning of the control-parameters and ignore the common parameter, that is, the population size. Addressing this issue, this paper proposes self-adaptive population size strategy based on Flower Pollination Algorithm, called saFPA for t-way test suite generation. In the proposed algorithm, the population size of FPA is dynamically varied based on the current need of the search process. Experimental results show that saFPA produces very competitive results as compared to existing strategies.

Test Cases Minimization Strategy Based on Flower Pollination Algorithm

Exhaustive testing in software testing is hard to implement due to a huge number of test cases and time-consuming in order to find bugs. Hence, a test cases minimization strategy is an essential to obtain an optimize test cases and reduce time. The major objective of this study is to propose a new test case minimization strategy called Test Generator Flower Pollination Strategy (TGFP) based the Flower Pollination Algorithm (FPA). The analytical and experimental findings evaluate the performance of the proposed strategy with existing combinatorial testing strategies. The research findings that have been obtained from the evaluation indicated that TGFP able to reduce a large number of test cases. On the basis of the findings of this research, it can be concluded that the TGFP has the potential to optimize the number of test cases compared to others t-way strategies no matter is optimization based or non-optimization based.

Adopting search-based algorithms for pairwise testing

Owing to an exponential increase in computational time associated with increasing number of components, exhaustive testing is impractical. Here, many researchers opt to adopt pairwise testing to minimize the overall number of tests. Recently, many existing work are focusing on the use of Search-Based algorithms as the basis of the implementation algorithm. This paper presents a critical comparison of Search-Based algorithm for generating the pairwise test suite. An analysis of existing SB pairwise strategies shows the positive and negative points for each strategy thereby highlighting promising future directions in this area.