A variable strength interaction test suites generation strategy using Particle Swarm Optimization

Ahmed, Bestoun S.; Zamli, Kamal Z.

doi:10.1016/j.jss.2011.06.004

Cited by 83 publications

(48 citation statements)

References 29 publications

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“…Although these results seem to negatively affect the usability of our approach, it should be noted that methods with more than 6 parameters are quite rare. Moreover, it is possible not to use all existing combinations of parameters, but use the common t-way approach (discussed for example in [20]) instead.…”

Section: B Dependency On Number Of Methods and Parametersmentioning

confidence: 99%

Interface-based Semi-automated Testing of Software Components

Potužák

Lipka

Brada

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-The component-based software development enables to construct applications from reusable components providing particular functionalities and simplifies application evolution. To ensure the correct functioning of a given component-based application and its preservation across evolution steps, it is necessary to test not only the functional properties of the individual components but also the correctness of their mutual interactions and cooperation. This is complicated by the fact that third-party components often come without source code and/or documentation of functional and interaction properties. In this paper, we describe an approach for performing rigorous semi-automated testing of software components with unavailable source code. Utilizing an automated analysis of the component interfaces, scenarios invoking methods with generated parameter values are created. When they are performed on a stable application version and their runtime effects (component interactions) are recorded, the resulting scenarios with recorded effects can be used for accurate regression testing of newly installed versions of selected components. Our experiences with a prototype implementation show that the approach has acceptable demands on manual work and computational resources.

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Section: B Dependency On Number Of Methods and Parametersmentioning

confidence: 99%

Interface-based Semi-automated Testing of Software Components

Potužák

Lipka

Brada

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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“…Ahmed et al [11] [2011] explained Variable Strength (VS)-PSTG strategy for test suite generation. The strategy was combined with PSO algorithm for t-way to construct VS test suites.…”

Section: A Review On Pairwise Test Case Generationmentioning

confidence: 99%

A Critical Review on Automated Test Case Generation for Conducting Combinatorial Testing Using Particle Swarm Optimization

Prakash¹,

Tatale²,

Kondhalkar³

et al. 2018

IJET

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In software development life cycle, testing plays the significant role to verify requirement specification, analysis, design, coding and to estimate the reliability of software system. A test manager can write a set of test cases manually for the smaller software systems. However, for the extensive software system, normally the size of test suite is large, and the test suite is prone to an error committed like omissions of important test cases, duplication of some test cases and contradicting test cases etc. When test cases are generated automatically by a tool in an intelligent way, test errors can be eliminated. In addition, it is even possible to reduce the size of test suite and thereby to decrease the cost & time of software testing. It is a challenging job to reduce test suite size. When there are interacting inputs of Software under Test (SUT), combinatorial testing is highly essential to ensure higher reliability from 72 % to 91 % or even more than that. A meta-heuristic algorithm like Particle Swarm Optimization (PSO) solves optimization problem of automated combinatorial test case generation. Many authors have contributed in the field of combinatorial test case generation using PSO algorithms. We have reviewed some important research papers on automated test case generation for combinatorial testing using PSO. This paper provides a critical review of use of PSO and its variants for solving the classical optimization problem of automatic test case generation for conducting combinatorial testing.

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“…However, an algorithm-based comparison is needed instead of utilizing a program-based comparison. PSO was investigated in generating test suite of combinatorial testing [38], and PSO outperformed SA and ACO in terms of the size of test suite and detected faults. Some variants of PSO such as PSO + GA were applied on test case prioritization, but the evaluation of test execution time and APFD through one algorithm is not sufficient to make a reliable decision [39].…”

Section: Related Workmentioning

confidence: 99%

A bat-inspired algorithm for prioritizing test cases

Öztürk

2017

Vietnam J Comput Sci

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By ordering test cases, early fault detection is focused on test case prioritization. In this field, it is widely known that algorithm and coverage criteria focused works are common. Previous works, which are related to test case prioritization, showed that practitioners need a novel method that optimizes test cases according to the cost of each test case instead of regarding the total cost of a test suite. In this work, by utilizing local and global search properties of a bat algorithm, a new bat-inspired test cases prioritization algorithm (BITCP) is proposed. In order to develop BITCP, test case execution time and the number of faults were adapted to the distance from the prey and loudness, respectively. The proposed method is then compared with four methods which are commonly used in this field. According to the results of the experiment, BITCP is superior to the conventional methods. In addition, as the complexity of the code of test cases increases, the decline in average percentage of fault detection is less in BITCP than the other four comparison algorithms produced.

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A variable strength interaction test suites generation strategy using Particle Swarm Optimization

Cited by 83 publications

References 29 publications

Interface-based Semi-automated Testing of Software Components

Interface-based Semi-automated Testing of Software Components

A Critical Review on Automated Test Case Generation for Conducting Combinatorial Testing Using Particle Swarm Optimization

A bat-inspired algorithm for prioritizing test cases

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