Automated refinement of models for model-based testing using exploratory testing

Gebizli, Ceren Sahin; Sözer, Hasan

doi:10.1007/s11219-016-9338-2

Cited by 46 publications

(8 citation statements)

References 30 publications

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“…Also, Wang et al (2017) used model based testing to validate quorum-based systems implemented using the Gorums library through a new approach. On the other hand, some studies give the publications covering supporting techniques for modelling and test generation (Gebizli and Sozer, 2017), integration into industrial practice (Peleska, 2013), taxonomies (Utting et al, 2012), industrial evaluations (Blom et al, 2016), surveys and classification. For example Utting et al (2012) that have provided taxonomy of model based testing in which the principal aspects of MBT approaches are covered.…”

Section: Discussionmentioning

confidence: 99%

Combining Model-Based Testing and Failure Modes and Effects Analysis for Test Case Prioritization: A Software Testing Approach

Meriem¹,

Abdelaziz²

2019

Journal of Computer Science

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Due to The complexity of modern software projects and the increasing size of software systems, it becomes difficult to manually perform tests with limited resources. Also, manual testing cannot assure that the software is tested using all possible combinations of inputs. Therefore, automate software testing activities have become primordial in the Software Development Life Cycle (SDLC). Model-based testing is a prominent validation technique in software testing that uses models of the system under test to automatically generate test cases; this generation leads to a large number of test cases; which cannot exhaustively be executed, due to time and cost constraints. Moreover, the test-cases execution order has an influence on the rate at which faults can be detected. Therefore, it is essential to prioritize test cases in order to execute the most important with limited time and cost. On the other hand, The Failure Mode and Effects Analysis (FMEA) considered as one of the formal techniques to do risk based testing. It offers a structured methodology to identifying the system failure modes, analyzing their effects and setting up controls to risk reducing and improve the quality of systems or products. By applying such a technique to Model-based testing projects, we can benefit from FMEA analysis and the automating in same project. Through this paper, we introduced a new model based testing approach for prioritizing and ranking test cases according to the requirements and failure modes. In the suggested approach we used the Failure Mode and Effect Analysis methodology in model based testing way to automatically generate a set of pair {test case, priority number} to prioritize test cases. It differs from existing approaches in that it generates two types of test cases; requirement-based test cases and failure mode-based test cases. For the purpose of experimental evaluation and analysis, we will compare the suggested approach with some well-known prioritization methods and we will present a case study to illustrate the potential application of the proposed approach in a future work.

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Section: Discussionmentioning

confidence: 99%

Combining Model-Based Testing and Failure Modes and Effects Analysis for Test Case Prioritization: A Software Testing Approach

Meriem¹,

Abdelaziz²

2019

Journal of Computer Science

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“…Research has also been reported on the development of test environments to support exploratory testing. For example, ARME enables the automatic re nement of system models based on recorded testing activities of test engineers (Gebizli and Sözer, 2016). Tapir supports team collaboration in exploratory testing and reconstruction of system models (Bures et al, 2018).…”

Section: Exploratory Testingmentioning

confidence: 99%

Discovering Boundary Values of Feature-based Machine Learning Classifiers through Exploratory Datamorphic Testing

Zhu¹,

Bayley²

2021

Preprint

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Testing has been widely recognised as di cult for AI applications. is paper proposes a set of testing strategies for testing machine learning applications in the framework of the datamorphism testing methodology. In these strategies, testing aims at exploring the data space of a classi cation or clustering application to discover the boundaries between classes that the machine learning application de nes.is enables the tester to understand precisely the behaviour and function of the so ware under test. In the paper, three variants of exploratory strategies are presented with the algorithms implemented in the automated datamorphic testing tool Morphy. e correctness of these algorithms are formally proved. eir capability and cost of discovering borders between classes are evaluated via a set of controlled experiments with manually designed subjects and a set of case studies with real machine learning models.

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“…A Testdriven approach, which is a use case, has been chosen to validate the functional requirement [14]. Test engineer uses their knowledge and experience to create a test case [15]. The effectiveness of it has been proven in many cases.…”

Section: B Functional Requirement 1) Consistencymentioning

confidence: 99%

“…The effectiveness of it has been proven in many cases. The effectiveness of the generated test case depends on its correctness and completeness [15]. Test cases usually prepared during the requirement phase.…”

Section: B Functional Requirement 1) Consistencymentioning

confidence: 99%

Evaluation of Software Requirement Specification Based on IEEE 830 Quality Properties

Stephen

Mit

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Software requirement specification (SRS) documented an essential requirement of software and its external interface. Many studies found the quality of SRS, but lack of the informality organizing of document and representation of functional requirement. This paper aims to evaluate the quality properties of the software requirement specification (SRS). There are four quality properties to be assessed, which are completeness, correctness, preciseness, and consistency. Completeness quality is used to evaluate the structure of the SRS document; meanwhile, the other three qualities used to evaluate the functional requirement. The measurement for each quality properties has been proposed in the previous study. The evaluation process involves a few stages. In short, the prototype would extract text through the provided document, do a calculation, and came out with the result in the form of a similarity percentage. The prototype designs in such ways it minimizes the user interference. Those resulted in reducing human error. Corpus contains libraries of term and topic are expected to increase the reliability of detection. The corpus includes topics extracted from IEEE 830 standard, vague word, terms represent Create, Read, Update, and Delete (CRUD) operation, and terms denote possible datatype. The extracted functional requirement would be refined based on the Requirement Boilerplate (RB) template. RB adopted in the study to ensure the consistency of functional refinement requirements. The percentage of similarity is determined based on comparison with IEEE 830 standard. The rate of the result of each quality properties reflects the quality of the software requirement specification.

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Automated refinement of models for model-based testing using exploratory testing

Cited by 46 publications

References 30 publications

Combining Model-Based Testing and Failure Modes and Effects Analysis for Test Case Prioritization: A Software Testing Approach

Combining Model-Based Testing and Failure Modes and Effects Analysis for Test Case Prioritization: A Software Testing Approach

Discovering Boundary Values of Feature-based Machine Learning Classifiers through Exploratory Datamorphic Testing

Evaluation of Software Requirement Specification Based on IEEE 830 Quality Properties

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