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

An Analysis on the Applicability of Meta-Heuristic Searching Techniques for Automated Test Data Generation in Automatic Programming Assessment

حظي تقييم البرمجة التلقائي (APA) بالكثير من الاهتمام بين الباحثين بشكل أساسي لدعم الدرجات الآلية ووضع علامات على المهامالمكلف بادائها الطلاب أو التدريبات بشكل منهجي. يتم تعريف APA بشكل شائع كطريقة يمكن أن تعزز الدقة والكفاءة والاتساق وكذلك تقديمملاحظات فورية لحلول للطلاب. في تحقيق APA ، تعد عملية إنشاء بيانات الاختبار مهمة للغاية وذلك لإجراء اختبار ديناميكي لمهمةالطلاب. في مجال اختبار البرمجيات ، أوضحت العديد من الأبحاث التي تركز على توليد بيانات الاختبار نجاح اعتماد تقنيات البحث الفوقية(MHST) من أجل تعزيز إجراءات استنباط بيانات الاختبار المناسبة للاختبار الفعال. ومع ذلك، فإن الأبحاث التي أجريت على APA حتىالآن لم تستغل بعد التقنيات المفيدة لتشمل تغطية اختبار جودة برنامج أفضل. لذلك ، أجرت هذه الدراسة تقييماً مقارنا لتحديد أي تقنية بحثفوقي قابلة للتطبيق لدعم كفاءة توليد بيانات الاختبار الآلي (ATDG) في تنفيذ اختبار وظيفي ديناميكي. في تقييم البرمجة التلقائي يتم تضمينالعديد من تقنيات البحث الفوقية الحديثة في التقييم المقارن الذي يجمع بين كل من خوارزميات البحث المحلية والعالمية من عام 2000 حتىعام 2018 .تشير نتيجة هذه الدراسة إلى أن تهجين Cuckoo Search مع Tabu Search و lévy flight كواحدة من طرق البحث الفوقية الواعدةليتم تطبيقها ، حيث أنه يتفوق على الطرق الفوقية الأخرى فيما يتعلق بعدد التكرارات ونطاق المدخلات.Automatic Programming Assessment (APA) has been gaining lots of attention among researchers mainly to support automated grading and marking of students’ programming assignments or exercises systematically. APA is commonly identified as a method that can enhance accuracy, efficiency and consistency as well as providing instant feedback on students’ programming solutions. In achieving APA, test data generation process is very important so as to perform a dynamic testing on students’ assignment. In software testing field, many researches that focus on test data generation have demonstrated the successful of adoption of Meta-Heuristic Search Techniques (MHST) so as to enhance the procedure of deriving adequate test data for efficient testing. Nonetheless, thus far the researches on APA have not yet usefully exploited the techniques accordingly to include a better quality program testing coverage. Therefore, this study has conducted a comparative evaluation to identify any applicable MHST to support efficient Automated Test Data Generation (ATDG) in executing a dynamic-functional testing in APA. Several recent MHST are included in the comparative evaluation combining both the local and global search algorithms ranging from the year of 2000 until 2018. Result of this study suggests that the hybridization of Cuckoo Search with Tabu Search and lévy flight as one of promising MHST to be applied, as it’s outperforms other MHST with regards to number of iterations and range of inputs

An Enhanced Pairwise Search Approach for Generating Optimum Number of Test Data and Reduce Execution Time

In recent days testing considers the most important task for building software that is free from error. Since the resources and time is limited to produce software, hence, it is not possible of performing exhaustive tests (i.e. to test all possible combinations of input data.) An alternative to get ride from this type exhaustive numbers and as well to reduce cost, an approach called Pairwise (2 way) test data generation approach will be effective. Most of the software faults in pairwise approach caused by unusual combination of input data.  Hence, the demand for the optimization of number of generated test-cases and reducing the execution time is growing in software industries. This paper proposes an enhancement in pairwise search approach which generates optimum number of input values for testing purposes.  In this approach it searches the most coverable pairs by pairing parameters and adopts one-test-at-a-time strategy for constructing a final test-suite.  Compared to other existing strategies, Our proposed approach is effective in terms of number of generated test cases and of execution time. Keywords:, Software testing, Pairwise testing, Combinatorial interaction testing, Test case generation

A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation

Otsingupõhine tarkvara testimine kasutab metaheuristilisi algoritme, et automatiseerida testide genereerimist. Selle töö eesmärgiks on osaliselt taasluua 2010. aastal kirjutatud Ali et al. artikkel, et uurida, kuidas on aastatel 2008-2015 kasutatud metaheuristilisi algoritme testide loomiseks. See töö analüüsib, kuidas on antud artiklid koostatud ning kuidas neis on algoritmide maksumust ja efektiivsust hinnatud. Kogutud tulemusi võrreldakse Ali et al. tulemustega.Search based software testing uses metaheuristic algorithms to automate the generation of test cases. This thesis partially replicates a literature study published in 2010 by Ali et al. to determine how studies published in 2008-2015 use metaheuristic algorithms to automate the generation of test cases. The thesis analyses how these studies were conducted and how the cost-effectiveness is assessed in these papers. The trends detected in the new publications are compared to those presented in Ali et al

Solving multiple sequence alignment problems by using a swarm intelligent optimization based approach

In this article, the alignment of multiple sequences is examined through swarm intelligence based an improved particle swarm optimization (PSO). A random heuristic technique for solving discrete optimization problems and realistic estimation was recently discovered in PSO. The PSO approach is a nature-inspired technique based on intelligence and swarm movement. Thus, each solution is encoded as “chromosomes” in the genetic algorithm (GA). Based on the optimization of the objective function, the fitness function is designed to maximize the suitable components of the sequence and reduce the unsuitable components of the sequence. The availability of a public benchmark data set such as the Bali base is seen as an assessment of the proposed system performance, with the potential for PSO to reveal problems in adapting to better performance. This proposed system is compared with few existing approaches such as deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) alignment (DIALIGN), PILEUP8, hidden Markov model training (HMMT), rubber band technique-genetic algorithm (RBT-GA) and ML-PIMA. In many cases, the experimental results are well implemented in the proposed system compared to other existing approaches

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

Artificial Bee Colony Algorithm for Pairwise Test Generation

Our dependence on software applications has become dramatic in many activities of our daily life as they help to increase the efficiency of our tasks. These software applications have many sets of input values, parameters, software/hardware environments and system conditions, which need to be tested to ensure software reliability and quality. However, the whole comprehensive software testing is virtually not possible due to marketing pressure and resource constraints. In an attempt to solve this problem, there has been a development of a number of sampling and pairwise strategies in the literature. In this paper, we evaluated and proposed a pairwise strategy named Pairwise Artificial Bee Colony algorithm (PABC). According to the benchmarking results, the PABC strategies outdo some existing strategies to generate a test case in many of the system configurations taken into consideration. In a case where PABC is not at its optimal stage or its best performance, the experiments of a test case are effectively competitive. PABC progresses as a means to achieve the effective use of the artificial bee colony algorithm for pairwise testing reduction

Soft Computing Techiniques for the Protein Folding Problem on High Performance Computing Architectures

The protein-folding problem has been extensively studied during the last fifty years. The understanding of the dynamics of global shape of a protein and the influence on its biological function can help us to discover new and more effective drugs to deal with diseases of pharmacological relevance. Different computational approaches have been developed by different researchers in order to foresee the threedimensional arrangement of atoms of proteins from their sequences. However, the computational complexity of this problem makes mandatory the search for new models, novel algorithmic strategies and hardware platforms that provide solutions in a reasonable time frame. We present in this revision work the past and last tendencies regarding protein folding simulations from both perspectives; hardware and software. Of particular interest to us are both the use of inexact solutions to this computationally hard problem as well as which hardware platforms have been used for running this kind of Soft Computing techniques.This work is jointly supported by the FundaciónSéneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under grants 15290/PI/2010 and 18946/JLI/13, by the Spanish MEC and European Commission FEDER under grant with reference TEC2012-37945-C02-02 and TIN2012-31345, by the Nils Coordinated Mobility under grant 012-ABEL-CM-2014A, in part financed by the European Regional Development Fund (ERDF). We also thank NVIDIA for hardware donation within UCAM GPU educational and research centers.Ingeniería, Industria y Construcció

Automated pairwise testing approach based on classification tree modeling and negative selection algorithm

Generating the test cases for analysis is an important activity in software testing to increase the trust level of users. The traditional way to generate test cases is called exhaustive testing. It is infeasible and time consuming because it generates too many numbers of test cases. A combinatorial testing was used to solve the exhaustive testing problem. The popular technique in combinatorial testing is called pairwise testing that involves the interaction of two parameters. Although pairwise testing can cover the exhaustive testing problems, there are several issues that should be considered. First issue is related to modeling of the system under test (SUT) as a preprocess for test case generation as it has yet to be implemented in automated proposed approaches. The second issue is different approaches generate different number of test cases for different covering arrays. These issues showed that there is no one efficient way to find the optimal solution in pairwise testing that would consider the invalid combination or constraint. Therefore, a combination of Classification Tree Method and Negative Selection Algorithm (CTM-NSA) was developed in this research. The CTM approach was revised and enhanced to be used as the automated modeling and NSA approach was developed to optimize the pairwise testing by generate the low number of test cases. The findings showed that the CTM-NSA outperformed the other modeling method in terms of easing the tester and generating a low number of test cases in the small SUT size. Furthermore, it is comparable to the efficient approaches as compared to many of the test case generation approaches in large SUT size as it has good characteristic in detecting the self and non-self-sample. This characteristic occurs during the detection stage of NSA by covering the best combination of values for all parameters and considers the invalid combinations or constraints in order to achieve a hundred percent pairwise testing coverage. In addition, validation of the approach was performed using Statistical Wilcoxon Signed-Rank Test. Based on these findings, CTM-NSA had been shown to be able perform modeling in an automated way and achieve the minimum or a low number of test cases in small SUT size