An Effective Regression Test Case Selection Using Hybrid Whale Optimization Algorithm

Agrawal, Arun Prakash; Choudhary, Ankur; Kaur, Amandeep

doi:10.4018/ijdst.2020010105

Cited by 19 publications

(9 citation statements)

References 27 publications

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“…However, on the dark side of the proposed algorithm, it takes more execution time and loss in fault detection. In [Agrawal et al 2020], a safe regression test case selection method is based on the hybrid whale optimization algorithm. They have also compared the proposed approach with various other nature-based computing techniques like Bat Search, ACO-based approach, etc.…”

Section: Semantic-based Retrieval Using Metadatamentioning

confidence: 99%

“…As described in [Yoo and Harman 2012], mostly regression testing is used to keep account of each modification in software and retesting of test cases that focus only on modifications in all subsequent testing phases. In [Askarunisa, Shanmugapriya and Ramaraj 2010] [Kaur and Goyal 2011] [Suman and Seema 2012] [Yoo and Harman 2012] regression test prioritization is performed over test cases and analyzed for the average percentage of code coverage. This paper presents the effect of regression testing over a system.…”

Section: Introductionmentioning

confidence: 99%

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K-Step Crossover Method based on Genetic Algorithm for Test Suite Prioritization in Regression Testing

Gupta¹

2021

jucs

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Software is an integration of numerous programming modules  (e.g., functions, procedures, legacy system, reusable components, etc.) tested and combined to build the entire module. However, some undesired faults may occur due to a change in modules while performing validation and verification. Retesting of entire software is a costly affair in terms of money and time. Therefore, to avoid retesting of entire software, regression testing is performed. In regression testing, an earlier created test suite is used to retest the software system's modified module. Regression Testing works in three manners; minimizing test cases, selecting test cases, and prioritizing test cases. In this paper, a two-phase algorithm has been proposed that considers test case selection and test case prioritization technique for performing regression testing on several modules ranging from a smaller line of codes to huge line codes of procedural language. A textual based differencing algorithm has been implemented for test case selection. Program statements modified between two modules are used for textual differencing and utilized to identify test cases that affect modified program statements. In the next step, test case prioritization is implemented by applying the Genetic Algorithm for code/condition coverage. Genetic operators: Crossover and Mutation have been applied over the initial population (i.e. test cases), taking code/condition coverage as fitness criterion to provide a prioritized test suite. Prioritization algorithm can be applied over both original and reduced test suite depending upon the test suite's size or the need for accuracy. In the obtained results, the efficiency of the prioritization algorithms has been analyzed by the Average Percentage of Code Coverage (APCC) and Average Percentage of Code Coverage with cost (APCCc). A comparison of the proposed approach is also done with the previously proposed methods and it is observed that APCC & APCCc values achieve higher percentage values faster in the case of the prioritized test suite in contrast to the non-prioritized test suite.

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Section: Semantic-based Retrieval Using Metadatamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

K-Step Crossover Method based on Genetic Algorithm for Test Suite Prioritization in Regression Testing

Gupta¹

2021

jucs

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“…In the past few years, various approaches have been proposed to solve MS-RCPSP, thereby finding out intelligent algorithms to allocate and manage network resources in the Internet of things systems [14][15][16][17]. Some popular metaheuristic algorithms are the genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization [18][19][20][21][22].…”

Section: Approximation Algorithms For Ms-rcpspmentioning

confidence: 99%

“…)// parameter of the mutation operator (12) for (j � 0; j < n; j++) (14) end for (15) if (f (u i (t)) ≤ f(x i (t))) (16) x i (t+1) � u i (t) (17) else (18) x i (t+1) � x i (t) (19) end if (20) end for (21) Calculate the fitness and bestnest (22) If (makespan > min (fitness)) (23) makespan � min (fitness) (24) End if (25) bestnest ⟵ Reallocate (bestnest) (26) t ⟵ t + 1 (27) Input: currentBest //the best schedule among the current population Output: //the improved schedule (1) Begin (2) makespan � f(best) (3) newbest � currentBest; (4) R b ⟵ maxResource (newbest) //the last resource to finish its job (5) Tb ⟵ set of tasks is performed by resource Rb (6) For i � 1 to size(Tb) // Consider each task in T b , the set of tasks performed by resource R b (7) T i � T b [i]; (8) R i ⟵ set of resource that are skilled enough to execute the task i except R b (9) For j � 1 to size (R i ) // Consider each resource in turn (10)…”

Section: Imopse Datasetmentioning

confidence: 99%

“…R b � R b -{i} // Remove task i from the task list of R b (12) newmakespan � f(newbest) // e makespan of the new schedule (13) If newmakespan < makespan (14) makespan � newmakespan (15) Return newbest; // Successful Reallocate, return the new and better schedule (16) End if (17) newbest � currentBest; // Unsuccessful Reallocate, reuse the original schedule for the next iteration (18) End for (19) End for (20) Return best (21) End Function ALGORITHM 2: Function reallocate. 6 Scientific Programming (i) e number of tasks and resources (ii) e number of precedence relations between tasks (iii) e number of skills All simulations were run on a PC with Intel Core i5-CPU 2.2 GHz, 6 GB RAM, and operating system Windows 10.…”

Section: Imopse Datasetmentioning

confidence: 99%

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Intelligent Differential Evolution Scheme for Network Resources in IoT

Quoc

Doan²,

Thanh

et al. 2020

Scientific Programming

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Scheduling is a fundamental factor in managing the network resource of the Internet of things. For IoT systems such as production lines, to operate effectively, it is necessary to find an intelligent management scheme, i.e., schedule, for network resources. In this study, we focus on multiskill resource-constrained project scheduling problem (MS-RCPSP), a combinational optimization problem that has received extensive attention from the research community due to its advantages in network resource management. In recent years, many approaches have been utilized for solving this problem such as genetic algorithm and ant colony optimization. Although these approaches introduce various optimization techniques, premature convergence issue also occurs. Moreover, previous studies have only been verified on simulation data but not on real data of factories. This paper formulated the MS-RCPSP and then proposed a novel algorithm called DEM to solve it. The proposed algorithm was developed based on the differential evolution metaheuristic. Besides, we build the reallocate function to improve the solution quality so that the proposed algorithm converges rapidly to global extremum while also avoiding getting trapped in a local extremum. To evaluate the performance of the proposed algorithm, we conduct the experiment on iMOPSE, the simulated dataset used by previous authors in their research studies. In addition, DEM was verified on real dataset supported by a famous textile industry factory. Experimental results on both simulated data and real data show that the proposed algorithm not only finds a better schedule compared with related works but also can reduce the processing time of the production line currently used at the textile industry factory.

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Regression Test Case Selection: A Comparative Analysis of Metaheuristic Algorithms

Verma

Choudhary

Tiwari

2021

Proceedings of Second Doctoral Symposium on Computational Intelligence

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An Effective Regression Test Case Selection Using Hybrid Whale Optimization Algorithm

Cited by 19 publications

References 27 publications

K-Step Crossover Method based on Genetic Algorithm for Test Suite Prioritization in Regression Testing

K-Step Crossover Method based on Genetic Algorithm for Test Suite Prioritization in Regression Testing

Intelligent Differential Evolution Scheme for Network Resources in IoT

Regression Test Case Selection: A Comparative Analysis of Metaheuristic Algorithms

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