Identifying Interactions for Combinatorial Testing using Data Flow Techniques

Author:

Sabharwal Sangeeta1,Aggarwal Manuj1

Affiliation:

1. Department of Computer Science and IT Netaji Subhas Institute of Technology Delhi, India

Abstract

Combinatorial (t-way) testing has been proved to be an effective technique that identifies faults caused by interactions among a small number of input parameters. However, the degree of interaction to be considered for testing is still an open issue. Although higher strength t-way testing improves fault detection, it leads to a considerable increase in number of interactions to be tested and hence the test set size. In this paper, an approach is proposed that attempts to reduce the number of interactions to be tested. The source code is transformed into a flow graph and data flow analysis is applied to it to identify the interactions that exist in the system. The initial results suggest that the approach is able to achieve a considerable reduction in the number of interactions to be tested.

Publisher

Association for Computing Machinery (ACM)

Reference16 articles.

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2. Mathur A.P. 2008. Foundations of Software Testing. Pearson Education. Mathur A.P. 2008. Foundations of Software Testing. Pearson Education.

3. Practical Combinatorial Testing;Kuhn D.R.;NIST Special Publication.,2010

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Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A prioritization method for combinatorial test cases based on small sample tests;2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD);2022-11-30

2. A novel approach for deriving interactions for combinatorial testing;Engineering Science and Technology, an International Journal;2017-02

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