Model-Based Test Prioritizing – A Comparative Soft-Computing Approach and Case Studies

Belli, Fevzi; Eminov, Mübariz; Gökçe, Nida

doi:10.1007/978-3-642-04617-9_54

Cited by 2 publications

(6 citation statements)

References 12 publications

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“…The importance of this method comes to light especially when the source code of the system is unavailable [21][22][23].…”

Section: Model-based Test Prioritization Using Cluster Analysismentioning

confidence: 99%

“…Here, the optimal number of clusters, say c , is determined in advance by using the cluster validity algorithm, as described in [57]. Those c clusters are then obtained by means of both ACL and FCM clustering algorithms [21][22][23].…”

Section: Model-based Test Prioritization Using Cluster Analysismentioning

confidence: 99%

“…all [13,[21][22][23][24]. The importance index, imp(x i ), of the i th event belonging to the k th cluster is in this respect defined as follows:…”

Section: Scopementioning

confidence: 99%

“…Model-based test case prioritization can in this respect be considered as an inexpensive alternative to the existing code-based test prioritization methods, while on the other hand it may be sensitive to the correct/incorrect information provided by the testers/developers. The importance of this method comes to light especially when the source code of the system is unavailable [21][22][23].…”

Section: Model-based Test Prioritization Using Cluster Analysismentioning

confidence: 99%

“…One of the benefits of the proposed approach is that it can be deployed in any stage of software development to reflect the preferences of the tester wherever he/she needs to select a subset among all possible/available test cases in favor of the total testing cost. Although the authors' previous works [13,[18][19][20][21][22][23] also defined similar attributes for events and assigned values to them in order to determine the degree of preference by means of cluster analysis [24], the approach presented in this article is different in that it classifies the events using two techniques adapted from soft computing, namely neural networks and fuzzy logic [22,23]. Details and the theoretical basis of the aforementioned clustering-based prioritization approaches are given in the authors' previous works [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Model-based test case prioritization using cluster analysis: a soft-computing approach

Gökçe¹,

Belli²,

Eminli³

et al. 2015

Turk J Elec Eng & Comp Sci

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Abstract:Model-based testing is related to the particular relevant features of the software under test (SUT) and its environment. Real-life systems often require a large number of tests, which cannot exhaustively be run due to time and cost constraints. Thus, it is necessary to prioritize the test cases in accordance with their importance as the tester perceives it, usually given by several attributes of relevant events entailed. Based on event-oriented graph models, this paper proposes an approach to ranking test cases in accordance with their preference degrees. For forming preference groups, events are clustered using an unsupervised neural network and fuzzy c-means clustering algorithm. The suggested approach is model-based, so it does not necessitate the availability of the source code of the SUT. It differs from existing approaches also in that it needs no prior information about the tests carried out before. Thus, it can be used to reflect the tester's preferences not only for regression testing as is common in the literature but also for ranking test cases in any stage of software development. For the purpose of experimental evaluation, we compare the suggested prioritization approach with six well-known prioritization methods.

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“…The importance of this method comes to light especially when the source code of the system is unavailable [21][22][23].…”

Section: Model-based Test Prioritization Using Cluster Analysismentioning

confidence: 99%

Section: Model-based Test Prioritization Using Cluster Analysismentioning

confidence: 99%

“…all [13,[21][22][23][24]. The importance index, imp(x i ), of the i th event belonging to the k th cluster is in this respect defined as follows:…”

Section: Scopementioning

confidence: 99%

Section: Model-based Test Prioritization Using Cluster Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Model-based test case prioritization using cluster analysis: a soft-computing approach

Gökçe¹,

Belli²,

Eminli³

et al. 2015

Turk J Elec Eng & Comp Sci

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Protocol-Based Testing of Firewalls

Tuğlular

Belli

2009

2009 Fourth South-East European Workshop on Formal Methods

Self Cite

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Abstract-A firewall is the most important tool of network security defense. Its proper functioning is critical to the network it protects. Therefore a firewall should be tested rigorously with respect to its implemented network protocols and security policy specification. We propose a combined approach for test case generation to uncover errors both in firewall software and in its configuration. In the proposed approach, abstract test cases are generated by mutating event sequence graph model of chosen network protocol and filled with values from policy specification by using equivalence partitioning and boundary value analysis. A case study is presented to validate the presented approach.

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Model-Based Test Prioritizing – A Comparative Soft-Computing Approach and Case Studies

Cited by 2 publications

References 12 publications

Model-based test case prioritization using cluster analysis: a soft-computing approach

Model-based test case prioritization using cluster analysis: a soft-computing approach

Protocol-Based Testing of Firewalls

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