A Search-Based Test Data Generation Method for Concurrent Programs

Mirhosseini, Seyed Mohsen; Haghighi, Hassan

doi:10.2991/ijcis.d.200805.003

Cited by 5 publications

(3 citation statements)

References 37 publications

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“…According to the data, the suggested SFLA has an average branch coverage of 99.99 percent, an average success rate of 99.97 percent, and an average number of generations covering all branches of 2.03. [52] Propose a searchbased 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.…”

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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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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“…16 The investigation of automatic generation in concurrent software is recent and studies have aimed at mapping the consolidated techniques of sequential applications to the domain of concurrent applications. [17][18][19][20][21] However, several gaps must be explored for enhancing the automatic generation of test data in this domain.…”

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confidence: 99%

Bio‐inspired optimization to support the test data generation of concurrent software

Vilela

Neto

Pinto³

et al. 2022

Concurrency and Computation

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Concurrent programming is increasingly present in modern applications. Although it provides higher performance and better use of available resources, the mechanisms of interaction between processes/threads result in a greater challenge for software testing activity. The nondeterminism present in those applications is one of the main issues during the test activity since the same test input can produce different possible execution paths, which may or not contain defects. The test data automatic generation can alleviate this problem, ensuring higher speed and reliability in software testing activity. This paper explores the automatic test data generation for concurrent programs through Genetic Algorithm, a bioinspired optimization technique, and proposes a test data generation approach for concurrent programs, called BioConcST, and a new operator for the selection of test subjects, called FuzzyST, which uses fuzzy logic. The approaches were evaluated in an experimental study towards their validation. The results showed that BioConcST is more promising than the other approaches at all analyzed levels. FuzzyST, together with Elitism and Tournament operators, provided the best results; however, it proved more suitable for concurrent programs of higher complexity. K E Y W O R D Sconcurrent software testing, search-based software testing, test data generation INTRODUCTIONAlthough most people may not know it, all current computer users and smartphones use concurrent software daily. Operating systems (OSs), included in such devices, employ concurrent programming mechanisms to manage the scheduling and execution of processes, whether in mono-processed environments, with pseudo-parallelism, or in multi-processed ones, where there is parallel execution between processes.The first contribution of concurrent programming to operating systems was proposed by Dijkstra, 1 who aimed to identify and resolve mutual exclusion for preventing a simultaneous access to a shared resource, also called a critical region. This and other mechanisms of concurrent programming have enabled Operational Systems to optimize the use of computational resources and, consequently, improve their performance.Presently, concurrent programming is no longer restricted to Operating Systems, since modern applications also demand a better use of resources and higher performance in the execution of routines and tasks. As an example, parallel programming and distributed programming, branches of concurrent programming, have led to the emergence of new technologies widely used, such as Web services and Cloud computing, which employ concepts of concurrent programming to provide communication and optimization mechanisms of services. 2,3

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“…A investigação da geração automática no contexto de software concorrente ainda é recente, os trabalhos existentes (NISTOR et al, 2012;GONG, 2013;GONG, 2014;GONG, 2016;MIRHOSSEINI;HAGHIGHI, 2020) empenham-se em mapear as técnicas consolidadas de aplicações sequenciais para o domínio de aplicações concorrentes, contudo, ainda existem lacunas que precisam ser exploradas para potencializar a geração automática de dados de teste nesse domínio.…”

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Otimização bioinspirada para apoio à geração de dados de teste para software concorrente

Vilela¹

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A Search-Based Test Data Generation Method for Concurrent Programs

Cited by 5 publications

References 37 publications

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

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

Bio‐inspired optimization to support the test data generation of concurrent software

Otimização bioinspirada para apoio à geração de dados de teste para software concorrente

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