SFLA‐based heuristic method to generate software structural test data

Ghaemi, Amir; Arasteh, Bahman

doi:10.1002/smr.2228

Cited by 28 publications

(8 citation statements)

References 17 publications

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“…For example, accessing the same data by different modules can be considered as an effective similarity metric. Different heuristic and machine‐learning algorithms (Arasteh, 2022; Arasteh, Abdi, et al, 2022; Arasteh, Fatolahzadeh, et al, 2022; Bouyer et al, 2007; Ghaemi & Arasteh, 2020; Hatami & Arasteh, 2020; Keshtgar & Arasteh, 2017; Zadahmad et al, 2011) have been used to salve the optimization problems in software engineering field; hence the other heuristic algorithms can be used for SMC problem.…”

Section: Resultsmentioning

confidence: 99%

Generating the structural graph‐based model from a program source‐code using chaotic forrest optimization algorithm

et al. 2023

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One of the most important and costly stages in software development is maintenance. Understanding the structure of software will make it easier to maintain it more efficiently. Clustering software modules is thought to be an effective reverse engineering technique for deriving structural models of software from source code. In software module clustering, the most essential objectives are to minimize connections between produced clusters, maximize internal connections within created clusters, and maximize clustering quality. Finding the appropriate software system clustering model is considered an NP‐complete task. The previously proposed approaches' key limitations are their low success rate, low stability, and poor modularization quality. In this paper, for optimal clustering of software modules, Chaotic based heuristic method using a forest optimization algorithm is proposed. The impact of chaos theory on the performance of the other SFLA‐GA and PSO‐GA has also been investigated. The results show that using the logistic chaos approach improves the performance of these methods in the software‐module clustering problem. The performance of chaotic based FOA, SFLA‐GA and PSO‐GA is superior to the other heuristic methods in terms of modularization quality and stability of the results.

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Section: Resultsmentioning

confidence: 99%

Generating the structural graph‐based model from a program source‐code using chaotic forrest optimization algorithm

et al. 2023

Self Cite

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“…In Ghaemi and Arasteh [26], SFLA was proposed to generate test data. In this work, branch coverage was used as the basis of the fitness function to generate effective test data.…”

Section: Search-based Methods For Test Data Generation In Sequential mentioning

confidence: 99%

“…To evaluate the capability of the proposed method for identifying program failures, some faults have been introduced in three benchmark programs, Gcd1, Index and Matrix, and the ability of the proposed method to detect resulting failures has been measured. In this regard, our method is compared with SFLA_Seq [26] and NICS_GA [27] as two search-based methods which have already been used for test data generation in sequential and concurrent programs, respectively. These methods were briefly described in Section 2.2.…”

Section: Evaluation Of the Failure Detection Capabilitymentioning

confidence: 99%

A Search-Based Test Data Generation Method for Concurrent Programs

Mirhosseini¹,

Haghighi²

2020

IJCIS

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Concurrent programs are being widely adopted in development of multi-core and many-core processors. However, these types of programs present some features such as concurrency, communication and synchronization which make their testing more challenging than sequential programs. Search-based techniques, which use meta-heuristic search algorithms, have frequently been used for testing sequential programs, especially in the test data generation activity. However, application of search-based techniques in test data generation for concurrent programs has seldom been covered in the literature. The first contribution of this paper is to present a search-based test data generation framework for concurrent programs. Additionally, a hybrid meta-heuristic algorithm, called SFLA-VND, is proposed, which could be used in the mentioned framework as well as other meta-heuristic algorithms. SFLA-VND is a combination of the shuffled frog leaping algorithm (SFLA) and the variable neighborhood descent (VND). The proposed framework has been experimented on five concurrent benchmark programs by applying genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), SFLA and SFLA-VND. Experimental results demonstrate the effectiveness and efficiency of this framework. Also, the results confirm the superiority of SFLA-VND in comparison with some popular meta-heuristic algorithms, when they are used for test data generation.

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“…The shu ed frog leaping algorithm (SFLA) is presented for generating structural test data [51]. This method has a good convergence rate and is very easy to implement.…”

Section: F Sflamentioning

confidence: 99%

Automatic Test Data Generation based on the Prime Path Coverage Criterion: A Grouping-based GA Approach

Damia

Esnaashari

Parvizimosaed

2023

Preprint

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Software testing is the process of running an application with the goal of finding bugs and subsequently improving its quality. Software testing, as a key process, plays a role in ensuring the quality of software systems. Testing is currently considered an industry in the field of software. Given that about 40% of the cost of producing any software is spent on testing, creating tools for automatically generating test data will significantly reduce the current costs of software development. This process can be considered an optimization problem, and thus, search algorithms can be used for tackling it. The Genetic Algorithm (GA) is one of the widest algorithms in this field. In this paper, we have proposed a novel GA approach, called Group-based GA (G-GA), which differs from the standard GA algorithm in the following ways. First of all, a new fitness function has been utilized that uses search space information to guide the population. The population is divided into four groups, each of which is updated according to its fitness level. Finally, in the proposed algorithm, the selection operator has been omitted and thus, the algorithm has less complexity and calculations than the standard GA. Also, the proposed algorithm considers a good level of exploration and exploitation at each step. Experiments have shown that the proposed G-GA method, in terms of the convergence speed and the search time, significantly outperforms the basic GA, its variations, PSO, Tabu Search, and Simulated Annealing.

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SFLA‐based heuristic method to generate software structural test data

Cited by 28 publications

References 17 publications

Generating the structural graph‐based model from a program source‐code using chaotic forrest optimization algorithm

Generating the structural graph‐based model from a program source‐code using chaotic forrest optimization algorithm

A Search-Based Test Data Generation Method for Concurrent Programs

Automatic Test Data Generation based on the Prime Path Coverage Criterion: A Grouping-based GA Approach

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