Community Detection in Model-based Testing to Address Scalability: Study Design

Silistre, Alper; Kılınççeker, Onur; Belli, Fevzi; Challenger, Moharram; Kardaş, Geylani

doi:10.15439/2020f163

Cited by 4 publications

(7 citation statements)

References 19 publications

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“…In the current work, we model GUI under test as given in [48] and partially in [29]. Then, we use the FSM model for a mutant generation.…”

Section: Gui Testingmentioning

confidence: 99%

“…Then, we use the FSM model for a mutant generation. However, we convert them to the RE model for test generation that is different from [29], [48]. Our modeling approach is more similar to [5] in which the authors do not offer a test generation approach that we propose in the current work.…”

Section: Gui Testingmentioning

confidence: 99%

“…On the other hand, there is another solution [69], [70] to cope with huge models utilizing layered modeling methods that are well-suited for the hierarchical structure of GUI systems. These layers can be manually created by the tester or automatically extracted from a non-layered model by utilizing a community detection algorithm introduced in [48] to mitigate the complexity thread.…”

Section: Internal Validitymentioning

confidence: 99%

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Model-Based Ideal Testing of GUI Programs–Approach and Case Studies

et al. 2021

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Traditionally, software testing is aimed at showing the presence of faults. This paper proposes a novel approach to testing graphical user interfaces (GUI) for showing both the presence and absence of faults in the sense of ideal testing. The approach uses a positive testing concept to show that the GUI under consideration (GUC) does what the user expects; to the contrary, the negative testing concept shows that the GUC does not do anything that the user does not expect, building a holistic view. The first step of the approach models the GUC by a finite state machine (FSM) that enables the model-based generation of test cases. This is always possible as the GUIs are considered as strictly sequential processes. The next step converts the FSM to an equivalent regular expression (RE) that will be analyzed first to construct test selection criteria for excluding redundant test cases and construct test coverage criteria for terminating the positive test process. Both criteria enable us to assess the adequacy and efficiency of the positive tests performed. The negative tests will be realized by systematically mutating the FSM to model faults, the absence of which are to be shown. Those mutant FSMs will be handled and assessed in the same way as in positive testing. Two case studies illustrate and validate the approach; the experiments' results will be analyzed to discuss the pros and cons of the techniques introduced.

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“…In the current work, we model GUI under test as given in [48] and partially in [29]. Then, we use the FSM model for a mutant generation.…”

Section: Gui Testingmentioning

confidence: 99%

Section: Gui Testingmentioning

confidence: 99%

Section: Internal Validitymentioning

confidence: 99%

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Model-Based Ideal Testing of GUI Programs–Approach and Case Studies

et al. 2021

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“…However, for the bigger problem sizes, the response time can be unreasonable. To tackle this problem, scalability can be improved using the available techniques in the literature, such as [30,34,79].…”

Section: External Validitymentioning

confidence: 99%

Model-based ideal testing of hardware description language (HDL) programs

et al. 2021

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An ideal test is supposed to show not only the presence of bugs but also their absence. Based on the Fundamental Test Theory of Goodenough and Gerhart (IEEE Trans Softw Eng SE-1(2): [156][157][158][159][160][161][162][163][164][165][166][167][168][169][170][171][172][173] 1975), this paper proposes an approach to model-based ideal testing of hardware description language (HDL) programs based on their behavioral model. Test sequences are generated from both original (fault-free) and mutant (faulty) models in the sense of positive and negative testing, forming a holistic test view. These test sequences are then executed on original (fault-free) and mutant (faulty) HDL programs, in the sense of mutation testing. Using the techniques known from automata theory, test selection criteria are developed and formally show that they fulfill the major requirements of Fundamental Test Theory, that is, reliability and validity. The current paper comprises a preparation step (consisting of the sub-steps model construction, model mutation, model conversion, and test generation) and a composition step (consisting of the sub-steps pre-selection and construction of Ideal test suites). All the steps are supported by a toolchain that is already implemented and is available online. To critically validate the proposed approach, three case studies (a sequence detector, a traffic light controller, and a RISC-V processor) are used and the strengths and weaknesses of the approach are discussed. The proposed approach achieves the highest mutation score in positive and negative testing for all case studies in comparison with two existing methods (regular expression-based test generation and context-based random test generation), using four different techniques. KeywordsModel-based testing • Ideal testing • Mutation testing • Behavioral model • Hardware description language Communicated by Dr Benoit Baudry.

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“…Based on the above work, Silistre et al [23] proposed two selection seed selection strategies: GRACLUS CENTERS and SPREAD HUBS. Both methods select a single node as a seed and use the community expansion method proposed by Andersen et al [22].…”

Section: Related Workmentioning

confidence: 99%

A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

Liu

Guo

2021

Complexity

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Community structure is one of the most important characteristics of complex networks, which has important applications in sociology, biology, and computer science. The community detection method based on local expansion is one of the most adaptable overlapping community detection algorithms. However, due to the lack of effective seed selection and community optimization methods, the algorithm often gets community results with lower accuracy. In order to solve these problems, we propose a seed selection algorithm of fusion degree and clustering coefficient. The method calculates the weight value corresponding to degree and clustering coefficient by entropy weight method and then calculates the weight factor of nodes as the seed node selection order. Based on the seed selection algorithm, we design a local expansion strategy, which uses the strategy of optimizing adaptive function to expand the community. Finally, community merging and isolated node adjustment strategies are adopted to obtain the final community. Experimental results show that the proposed algorithm can achieve better community partitioning results than other state-of-the-art algorithms.

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Community Detection in Model-based Testing to Address Scalability: Study Design

Cited by 4 publications

References 19 publications

Model-Based Ideal Testing of GUI Programs–Approach and Case Studies

Model-Based Ideal Testing of GUI Programs–Approach and Case Studies

Model-based ideal testing of hardware description language (HDL) programs

A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

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