Construction of Prioritized T-Way Test Suite Using Bi-Objective Dragonfly Algorithm

Software testing is important for ensuring the reliability of software systems. In software testing, effective test case generation is essential as an alternative to exhaustive testing. For improving the software testing technology, the t-way testing technique combined with metaheuristic algorithm has been great to analyze a large number of combinations for getting optimal solutions. However, most of the existing t-way strategies consider test case weights while generating test suites. Priority of test cases hasn’t been fully considered in previous works, but in practice, it’s frequently necessary to distinguish between high-priority and low-priority test cases. Therefore, the significance of test case prioritization is quite high. For this reason, this paper has proposed a t-way strategy that implements an adaptive Dragonfly Algorithm (DA) to construct prioritized t-way test suites. Both test case weight and test case priority have equal significance during test suite generation in this strategy. We have designed and implemented a Bi-objective Dragonfly Algorithm (BDA) for prioritized t-way test suite generation, and the two objectives are test case weight and test case priority. The test results demonstrate that BDA performs competitively against existing t-way strategies in terms of test suite size, and in addition, BDA generates prioritized test suites.©2022 Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Applying Architectural Analysis for Current Software Systems: A Case Study of KFC and Pizza Hut Online Food Ordering Systems in Malaysia

The main aim of this study is to discover the ability in analyzing, criticizing and providing suggestion in improving the selected important properties of a software application using architectural analysis dimensions. The researchers selected KFC and Pizza Hut online food ordering systems in Malaysia for the case study purpose. These two selected systems are critically analyzed using seven architectural dimensions such as goals of analysis, scope of analysis, primary architectural concern being analyzed, level of formality of architectural models, type of analysis, level of automation, system stakeholders who are interested in analysis. The finding suggests that there are some characteristics provided by Pizza Hut system which are better than KFC system. Furthermore, details of the findings and discussion are highlighted from seven different aspects of analysis which have been carefully studied and very well analyzed on two popular online food ordering systems

Construction of prioritized t-way test suite using Bi-objective Dragonfly Algorithm

Software testing is important for ensuring the reliability of software systems. In software testing, effective test case generation is essential as an alternative to exhaustive testing. For improving the software testing technology, the t-way testing technique combined with metaheuristic algorithm has been great to analyze a large number of combinations for getting optimal solutions. However, most of the existing t-way strategies consider test case weights while generating test suites. Priority of test cases hasn’t been fully considered in previous works, but in practice, it’s frequently necessary to distinguish between high-priority and low-priority test cases. Therefore, the significance of test case prioritization is quite high. For this reason, this paper has proposed a t-way strategy that implements an adaptive Dragonfly Algorithm (DA) to construct prioritized t-way test suites. Both test case weight and test case priority have equal significance during test suite generation in this strategy. We have designed and implemented a Bi-objective Dragonfly Algorithm (BDA) for prioritized t-way test suite generation, and the two objectives are test case weight and test case priority. The test results demonstrate that BDA performs competitively against existing t-way strategies in terms of test suite size, and in addition, BDA generates prioritized test suites

SRPTackle: A semi-automated requirements prioritisation technique for scalable requirements of software system projects

ContextRequirement prioritisation (RP) is often used to select the most important system requirements as perceived by system stakeholders. RP plays a vital role in ensuring the development of a quality system with defined constraints. However, a closer look at existing RP techniques reveals that these techniques suffer from some key challenges, such as scalability, lack of quantification, insufficient prioritisation of participating stakeholders, overreliance on the participation of professional expertise, lack of automation and excessive time consumption. These key challenges serve as the motivation for the present research.ObjectiveThis study aims to propose a new semiautomated scalable prioritisation technique called ‘SRPTackle’ to address the key challenges.MethodSRPTackle provides a semiautomated process based on a combination of a constructed requirement priority value formulation function using a multi-criteria decision-making method (i.e. weighted sum model), clustering algorithms (K-means and K-means++) and a binary search tree to minimise the need for expert involvement and increase efficiency. The effectiveness of SRPTackle is assessed by conducting seven experiments using a benchmark dataset from a large actual software project.ResultsExperiment results reveal that SRPTackle can obtain 93.0% and 94.65% as minimum and maximum accuracy percentages, respectively. These values are better than those of alternative techniques. The findings also demonstrate the capability of SRPTackle to prioritise large-scale requirements with reduced time consumption and its effectiveness in addressing the key challenges in comparison with other techniques.ConclusionWith the time effectiveness, ability to scale well with numerous requirements, automation and clear implementation guidelines of SRPTackle, project managers can perform RP for large-scale requirements in a proper manner, without necessitating an extensive amount of effort (e.g. tedious manual processes, need for the involvement of experts and time workload)

Comparison of performances of Jaya Algorithm and Cuckoo Search algorithm using benchmark functions

Nowadays, selecting the best possible solution among several solutions becomes an important skill for engineering and research. Therefore, engineers are turning to optimization methods as a complementary alternative strategy of exhaustive searching. Metaheuristic algorithms have been used successfully for solving different optimization problems. To help engineers select the best metaheuristic algorithms for their problems, there is a need to evaluate the performance of different metaheuristic algorithms against each other using common case studies. This paper aims to compare the performance of two metaheuristic algorithms which are Jaya Algorithm (JA) and Cuckoo Search (CS) using some common benchmark functions. CS and JA have implemented in the same platform (Intellij IDEA Community Edition 2020.2.3) using the same language (Java). The experimental results show that JA has better and consistent performance as compared to CS in most cases in terms of execution time and test suite size; however, the performance of JA is still within acceptable ranges

African buffalo optimization algorithm based t-way test suite generation strategy for electronic-payment transactions

The use of meta-heuristics in Combinatorial Interaction Testing (CIT) is becoming more and more popular due to their effectiveness and efficiency over the traditional methods especially in authenticating electronic payment (e�payment) transactions. Concomitantly, over the past two decades, there has been a rise both in the development of metaheuristics and their application to diverse theoretical and practical areas including CIT in e-payments. In the implementa�tion of t-way strategies (the t is used to represent the interaction strength), mixed results have been reported; some very exciting but, in other cases, the perfor�mance of metaheuristics has been, to say the least, below par. This mixed trend has led many researchers to explore alternate ways of improving the effectiveness and efficiency of metaheuristics in CIT, hence this study. It must be emphasized, however, that available literature indicates that no particular metaheuristic testing strategy has had consistent superior performance over the others in diverse testing environments and configurations. The need for effectiveness, therefore, necessi�tates the need for algorithm hybridization to deploy only the component parts of algorithms that have been proven to enhance overall search capabilities while at the same time eliminating the demerits of particular algorithms in the hybridiza�tion procedure. In this paper, therefore, a hybrid variant of the African Buffalo Optimization (ABO) algorithm is proposed for CIT. Four hybrid variants of the ABO are proposed through a deliberate improvement of the ABO with four algo�rithmic components. Experimental procedures indicate that the hybridization of the ABO with these algorithmic components led to faster convergence and greater effectiveness superior to the outcomes of existing techniques, thereby placing the algorithm among the best when compared with other methods/techniques

Deep Pipeline Architecture for Fast Fractal Color Image Compression Utilizing Inter-Color Correlation

Fractal compression technique is a well-known technique that encodes an image by mapping the image into itself and this requires performing a massive and repetitive search. Thus, the encoding time is too long, which is the main problem of the fractal algorithm. To reduce the encoding time, several hardware implementations have been developed. However, they are generally developed for grayscale images, and using them to encode colour images leads to doubling the encoding time 3× at least. Therefore, in this paper, new high-speed hardware architecture is proposed for encoding RGB images in a short time. Unlike the conventional approach of encoding the colour components similarly and individually as a grayscale image, the proposed method encodes two of the colour components by mapping them directly to the most correlated component with a searchless encoding scheme, while the third component is encoded with a search-based scheme. This results in reducing the encoding time and also in increasing the compression rate. The parallel and deep-pipelining approaches have been utilized to improve the processing time significantly. Furthermore, to reduce the memory access to the half, the image is partitioned in such a way that half of the matching operations utilize the same data fetched for processing the other half of the matching operations. Consequently, the proposed architecture can encode a 1024×1024 RGB image within a minimal time of 12.2 ms, and a compression ratio of 46.5. Accordingly, the proposed architecture is further superior to the state-of-the-art architectures.©2022 The Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

African Buffalo Optimization Algorithm Based T-Way Test Suite Generation Strategy for Electronic-Payment Transactions

The use of meta-heuristics in Combinatorial Interaction Testing (CIT) is becoming more and more popular due to their effectiveness and efficiency over the traditional methods espe-cially in authenticating electronic payment (e-payment) transactions. Concomitantly, over the past two decades, there has been a rise both in the development of metaheuristics and their application to diverse theoretical and practical areas including CIT in e-payments. In the implementation of t-way strategies (the t is used to represent the interaction strength), mixed results have been reported; some very exciting but, in other cases, the performance of metaheuristics has been, to say the least, below par. This mixed trend has led many re-searchers to explore alternate ways of improving the effectiveness and efficiency of me-taheuristics in CIT, hence this study. It must be emphasized, however, that available litera-ture indicates that no particular metaheuristic testing strategy has had consistent superior performance over the others in diverse testing environments and configurations. The need for effectiveness, therefore, necessitates the need for algorithm hybridization to deploy only the component parts of algorithms that have been proven to enhance overall search capa-bilities while at the same time eliminating the demerits of particular algorithms in the hybrid-ization procedure. In this paper, therefore, a hybrid variant of the African Buffalo Optimi-zation (ABO) algorithm is proposed for CIT. Four hybrid variants of the ABO are proposed through a deliberate improvement of the ABO with four algorithmic components. Experi-mental procedures indicate that the hybridization of the ABO with these algorithmic com-ponents led to faster convergence and greater effectiveness superior to the outcomes of existing techniques, thereby placing the algorithm among the best when compared with other methods/techniques

Latin hypercube sampling Jaya algorithm based strategy for T-way test suite generation

T-way testing is a sampling strategy that generates a subset of test cases from a pool of possible tests. Many t-way testing strategies appear in the literature to-date ranging from general computational ones to meta-heuristic based. Owing to its performance, man the meta-heuristic based t-way strategies have gained significant attention recently (e.g. Particle Swarm Optimization, Genetic Algorithm, Ant Colony Algorithm, Harmony Search, Jaya Algorithm and Cuckoo Search). Jaya Algorithm (JA) is a new metaheuristic algorithm, has been used for solving different problems. However, losing the search's diversity is a common issue in the metaheuristic algorithm. In order to enhance JA's diversity, enhanced Jaya Algorithm strategy called Latin Hypercube Sampling Jaya Algorithm (LHS-JA) for Test Suite Generation is proposed. Latin Hypercube Sampling (LHS) is a sampling approach that can be used efficiently to improve search diversity. To evaluate the efficiency of LHS-JA, LHS-JA is compared against existing metaheuristic-based t-way strategies. Experimental results have shown promising results as LHS-JA can compete with existing t-way strategies