Towards Uniform Modeling and Holistic Testing of Hardware and Software

Kılınççeker, Onur; Belli, Fevzi

doi:10.1109/ubmyk48245.2019.8965650

Cited by 4 publications

(11 citation statements)

References 19 publications

(19 reference statements)

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“…Test inputs acquired from this complement graph contain illegal (unexpected) input variables representing these undesired functions. The HT is already used to model and test graphical user interfaces [14], web service composition [15], web application [16], interactive systems [17], hardware designs [18], and android applications [19].…”

Section: A Holistic Testing (Ht)mentioning

confidence: 99%

“…We adapt the code-based mutation testing approaches presented in [8], [20]- [22] to obtain code-based mutants from the original program using mutation operators. The authors of [22]- [24] offer model-based mutation testing that we utilize in the current work to construct model-based mutants from the original (fault-free) model by using model mutation operators given in [18] for the FSM model. We use the similar idea proposed in [13] as being a hybrid approach by applying [8] Fortran-like programs Logical Expression replaces Usage of simple faults to detect complex faults via coupling effect [8] VHDL programs The ten mutation operators N/S [20] Fortran Programs The twenty-two mutation operators Providing The Mothra software testing environment [21] Java programs twelve method level and twenty-eight class-level operators N/S Model-Based Mutation [22] Statecharts Eight for FSM and eleven for Extended FSM Providing Proteum/ST software testing environment [23] Event Sequence Graph (ESG) Insertion, Omission, Replace operators Comprehensive comparison of event-based and state-based models [24] Event Sequence Graph (ESG) Insertion, Omission, Replace operators Proposing a MBMT approach with three real life case studies.…”

Section: B Code and Model-based Mutation Testingmentioning

confidence: 99%

“…We utilize the MT to acquire mutant models for faultspecific models by mutation operators [18]. Then, we construct the CTs from transformed the RE.…”

Section: A the Mbit And It's Proofmentioning

confidence: 99%

“…This module is called GUI Under Test. An omission, insertion, and replace mutation operators [18] are performed on these FSM(s) for a mutant generation. Then, further steps are carried on as provided in Section III-B and III-C.…”

Section: A Test Preparationmentioning

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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Section: A Holistic Testing (Ht)mentioning

confidence: 99%

Section: B Code and Model-based Mutation Testingmentioning

confidence: 99%

“…We utilize the MT to acquire mutant models for faultspecific models by mutation operators [18]. Then, we construct the CTs from transformed the RE.…”

Section: A the Mbit And It's Proofmentioning

confidence: 99%

Section: A Test Preparationmentioning

confidence: 99%

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

et al. 2021

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“…RE-based coverage criteria are used to assess the adequacy of the testing method. Kilincceker and Belli [16] propose novel coverage criteria based on the analysis of a RE model. These coverage criteria are used to generate test sequences for testing GUI systems in [7] by means of random test generation.…”

Section: A Model Based Gui Testingmentioning

confidence: 99%

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

Silistre

Kılınççeker

Belli

et al. 2020

Annals of Computer Science and Information Systems

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Model-based GUI testing has achieved widespread recognition in academy thanks to its advantages compared to code-based testing due to its potentials to automate testing and the ability to cover bigger parts more efficiently. In this study design paper, we address the scalability part of the model-based GUI testing by using community detection algorithms. A case study is presented as an example of possible improvements to make a model-based testing approach more efficient. We demonstrate layered ESG models as an example of our approach to consider the scalability problem. We present rough calculations with expected results, which show 9 times smaller time and space units for 100 events in the ESG model when a community detection algorithm is applied.

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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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Towards Uniform Modeling and Holistic Testing of Hardware and Software

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

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

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

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