HABCSm: A Hamming Based t-way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation

Search-based software engineering that involves the deployment of meta-heuristics in applicable software processes has been gaining wide attention. Recently, researchers have been advocating the adoption of meta-heuristic algorithms for t-way testing strategies (where t points the interaction strength among parameters). Although helpful, no single meta-heuristic based t-way strategy can claim dominance over its counterparts. For this reason, the hybridization of meta-heuristic algorithms can help to ascertain the search capabilities of each by compensating for the limitations of one algorithm with the strength of others. Consequently, a new meta-heuristic based t-way strategy called Hybrid Artificial Bee Colony (HABCSm) strategy, based on merging the advantages of the Artificial Bee Colony (ABC) algorithm with the advantages of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. HABCSm is the first t-way strategy to adopt Hybrid Artificial Bee Colony (HABC) algorithm with Hamming distance as its core method for generating a final test set and the first to adopt the Hamming distance as the final selection criterion for enhancing the exploration of new solutions. The experimental results demonstrate that HABCSm provides superior competitive performance over its counterparts. Therefore, this finding contributes to the field of software testing by minimizing the number of test cases required for test execution

ABC Algorithm for Combinatorial Testing Problem

Computer software is in high demand everywhere in the world. The high dependence on software makes software requirements more complicated. As a result, software testing tasks get costlier and challenging due to a large number of test cases, coupled with the vast number of the system requirements. This challenge presents the need for reduction of the system redundant test cases. A combinatorial testing approach gives an intended result from the optimization of the system test cases. Hence, this study implements a combinatorial testing strategy called Artificial Bee Colony Test Generation (ABC-TG) that helps to get rid of some of the current combinatorial testing strategies. Results obtained from the ABC-TG were benchmarked with the results obtained from existing strategies in order to determine the efficiency of the ABC-TG. Finally, ABC-TG shows the efficiency and effectiveness in terms of generating optimum test cases size of some of the case studies and a comparable result with the existing combinatorial testing strategies

TBat: A Novel Strategy for Minimization of T-Way Interaction Test Suite Based on the Particle Swarm Optimization and the Bat Algorithm

Our continuing dependencies on software raise issues of reliability. Lack of testing can lead to disastrous consequences including loss of data, loss of fortunes as well as loss of lives. For these reasons, many combinations of possible input parameters, hardware/software environments, and system conditions need to be checked against for conformance based on the system’s specification. Often, this results into combinatorial explosion of test cases. This project develops a novel strategy to minimize the test consideration using the Particle Swarm Optimization and the Bat Algorithm

A Bat-inspired Strategy for Pairwise Testing

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

Opposition-based Whale Optimization Algorithm

The Whale Optimization Algorithm (WOA) is a newly proposed metaheuristic optimization algorithm, which simulate humpback whales hunting behavior. Like other population-based algorithms, WOA generate its population randomly during the exploration and exploitation phases, which could generate values far from the optimum solution or stuck the exploration around local optima. In order to improve solution accuracy and reliability, this paper proposes a new algorithm based on WOA. The new algorithm called Opposition-based Whale Optimization (OWOA). The OWOA use the Opposition-based method to enhance Whale Optimization Algorithm (WOA) performance. The OWOA looks for the solution in the opposite direction of suggested values to test if the opposite select has better solution. The OWOA is tested and compared with the original algorithm WOA and other metaheuristic methods. The benchmark results prove the efficiency of the OWOA being more efficient than WOA

An Interaction Strategy for Testing Software Product Lines using the Bat-inspired Algorithm

Software product lines (SPLs) represent an engineering method for creating a portfolio of similar software systems for a shared set of software product assets. Owing to the significant growth of SPLs, there is a need for systematic approach for ensuring the quality of the resulting product derivatives. Combinatorial t-way testing (where t indicates the interaction strength) has been known to be effective especially when the number of product's features and constraints in the SPLs of interest are huge. In line with the recent emergence of Search based Software Engineering (SBSE), this article presents a novel strategy for SPLs tests reduction using Bat-inspired algorithm (BA), called SPLBA. Our experience with SPLBA has been promising as the strategy performed well against existing strategies in the literature

A Bat-inspired Strategy for T-Way Interaction Testing

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

Benchmarking of Bat-inspired Interaction Testing Strategy

Although desirable, exhaustive testing of the software system is impractical because of the significant growth of the search space of systems features (large search space).Several sampling strategies have been introduced to systematically reduce the test data for consideration. Complementing existing sampling strategies (i.e. in terms of dealing with interaction faults). To be specific, the interaction testing technique (refers to as t-way strategy) is based on interaction strength and is capable of dramatically reducing the number of test suite while ensuring practical coverage. T-way interaction testing has been extensively exploited resulting into many prototypes strategy implementations. Recently, there are growing interests for adopting optimization algorithms as the basis of the newly developed strategies contributing to the new and upcoming search based software testing (SBST) area of research. In this article, we benchmarked the bat-inspired testing strategy (BTS) for uniform strength interaction against some of the available t-way testing tools and strategies. Based on the benchmarking results, BTS achieved some new minimum test suite sizes over the existing strategies

Adapting the Elitism on the Greedy Algorithm for Variable Strength Combinatorial Test Cases Generation

A combinatorial testing (CT) is an important technique usually employed in the generation of test cases. The generation of an optimal sized test case is a Non-Deterministic Polynomial hard problem (NP). In recent times, many researchers had developed the various strategies based on the search-based approach to address the combinatorial testing issues. This study presented the most recent variable interaction strength (VS) CT strategy using an enhanced variant in the greedy algorithm. Hence, they are referred to as variable strength modified greedy strategy (VS-MGS). Moreover, the modified strategy supports a VS together with interaction strength up to six. The proposed variant-greedy algorithm employed the elitism mechanism alongside the iteration in order to improve its’ efficiency. This algorithm is invariably called the modified greedy algorithm (MGA). Furthermore, the efficiency and performance of the VS-MGS using MGA were assessed firstly by comparing its results with the original greedy algorithm results and thereafter benchmarked with the results of the existing VS CT strategies. The VS-MGS’s results ultimately revealed that the adaptation of elitism mechanism with iteration in greedy algorithm resulted in an improved efficiency in the process of generating a near-optimal test case set size

A review of challenges and security risks of cloud computing

Cloud computing has been an attention in the new era of the IT technologies as there is an increase demand in the services or utility computing all over the wide world web. Security risk resulting from resource sharing throughout the cloud computing becomes one of the most challenging concerns in providing powerful processing and storage as on-demand services. Taking the advantage of low cost derived from the increase in efficiency and performance facilitated by cloud computing, governments and organizations around the globe are motivated to build or migrate to the cloud. However, there are still many technical issues relating to the features of cloud computing and the provision of quality service, leading to a delay in adopting cloud computing. This review paper highlights the security risks and challenges of cloud computing and study the security requirements for cloud computing. The primary aim of this review is to classify the security risks and challenges related to the different forms of cloud computing (SaaS, PaaS and IaaS)