AbdulRahman A. Alsewari

Design and implementation of a harmony-search-based variable-strength t-way testing strategy with constraints support

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.

Interaction test data generation using Harmony Search Algorithm

This paper describes the adoption of Harmony Search Algorithm based strategy, called HSS, for generating interaction test data. In a nutshell, HSS generates a set of test data (as a complete test suite) that covers the t-way interaction at least once in a greedy manner (i.e. here, t indicates the interaction strength). The main feature of HSS is the fact that it is the first t-way strategy that is based on the Harmony Search Algorithm. Preliminary results demonstrate that HSS gives comparable results with other existing t-way strategies.

A Parameter Free Choice Function Based Hyper-Heuristic Strategy for Pairwise Test Generation

Hyper-heuristics are advanced high-level search methodologies that solve hard computational problems indirectly via low-level heuristics. Choice function based hyper-heuristics are selection and acceptance hyper-heuristics that use statistical information to rank low-level heuristics for selection. In this paper, we describe a choice function based hyper-heuristic called Pairwise Choice Function based Hyper-heuristic (PCFHH) for the pairwise test generation problem. PCFHH uses a combination of three measures to select and apply an effective low-level heuristic from a set of four low-level heuristics at any stage of the search. Our experimental results have been encouraging as PCFHH outperforms most of pairwise test generation strategies on many of the problem instances.

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.

An Orchestrated Survey on T-Way Test Case Generation Strategies Based on Optimization Algorithms

The test case construction is amongst the most labor-intensive tasks and has significant influence on the effectiveness and efficiency in software testing. Due to the market needed for diverse types of tests, recently, several number of t-way testing strategies (where t indicates the interaction strengths) have been developed adopting different approaches Algebraic, Pure computational, and Optimization Algorithms (OpA). This paper presents an orchestrated survey of the existing OpA t-way strategies as Simulated Annealing (SA), Genetic Algorithm (GA), Ant Colony Algorithm (ACA), Particle Swarm Optimization based strategy (PSTG), and Harmony Search Strategy (HSS). The results demonstrate the strength and the limitations of each strategy, thereby highlighting possible research for future work in this area.

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.

A variable combinatorial test suite strategy based on modified greedy algorithm

A software should be tested before released to the market to be sure that a software has been achieved the quality assurance measurement objectives. Therefore, one of the testing sorts is the combinatorial interaction testing (CIT) which is intended to discover the faults that are happened by interacting between the software features. Test case generation is the most active area of CIT research. As the problem of generating the most minimum test suite of CIT is NP-hard (i.e. NP where NP terms Non-deterministic Polynomial). Several researchers have been addressed the combinatorial interaction testing issues by developing the various strategies based on a search-based approach or a pure-computational approach, although, these are useful, but most of them have a lack to support the variable strength interaction which is one of CIT techniques. A variable strength interaction is the interaction between some of software features which have higher priority than the interaction between the others software features. This proposed will suggest a new CIT strategy based on a modified greedy algorithm (MGA) with addressing the supporting of variable strength interaction to generate a satisfactory test suite size.

Constraints dependent t-way test suite generation using harmony search strategy

Recently, many new researchers have considered the adoption of Artificial Intelligence-based Algorithm for the construction of t-way test suite generation strategies (where t indicates the interaction strengths). Although useful, most existing AI-based strategies have not sufficiently dealt or even experimented with the problem of constraints. Here, it is desirable for a particular AI-based strategy of interest to be able to automatically exclude the set of impossible or forbidden combinations from the final t-way generated suite. This paper describes our experience dealing with constraints from within a Harmony Search Algorithm based strategy, called HSS. Our experience with HSS is encouraging as we have obtained competitive test size as overall.

Multi-Start Jaya Algorithm for Software Module Clustering Problem

1 IBM Centre of Excellence, Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Pahang, Malaysia, kamalz@ump.edu.my 2 Faculty of Computer Systems and Software Engineering,Universiti Malaysia Pahang, Pahang, Malaysia, alsewari@ump.edu.my 3 Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University, Prague, Czech Republic, albeybes@fel.cvut.cz *Correspondence: Kamal Z. Zamli, IBM Centre of Excellence, Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Pahang, Malaysia, kamalz@ump. edu.my Abstract Jaya algorithm has gained considerable attention lately due to its simplicity and requiring no control parameters (i.e. parameter free). Despite its potential, Jaya algorithm is inherently designed for single objective problems. Additionally, Jaya is limited by the intense conflict between exploration (i.e. roams the random search space at the global scale) and exploitation (i.e. neighborhood search by exploiting the current good solution). Thus, Jaya requires better control for exploitation and exploration in order to prevent premature convergence and avoid being trapped in local optima. Addressing these issues, this paper proposes a new multi-objective Jaya variant with a multi-start adaptive capability and Cuckoo search like elitism scheme, called MS-Jaya, to enhance its exploitation and exploration allowing good convergence while permitting more diverse solutions. To assess its performances, we adopt MSJaya for the software module clustering problem. Experimental results reveal that MS-Jaya exhibits competitive performances against the original Jaya and state-of-the-art parameter free meta-heuristic counterparts consisting of Teaching Learning based Optimization (TLBO), Global Neighborhood Algorithm (GNA), Symbiotic Optimization Search (SOS), and Sine Cosine Algorithm (SCA).