Test Case Selection Using Feature Extraction and Clustering

Angelin Gladston, H. Khanna Nehemiah, P. Narayanasamy and A. Kannan
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Angelin Gladston: College of Engineering, Anna University, Chennai, India
H. Khanna Nehemiah: College of Engineering, Anna University, Chennai, India
P. Narayanasamy: College of Engineering, Anna University, Chennai, India
A. Kannan: College of Engineering, Anna University, Chennai, India

International Journal of Knowledge-Based Organizations (IJKBO), 2018, vol. 8, issue 2, 18-31

Abstract: This article explains the selection of important parameters from an execution pattern which brings out the details of the application of test cases. Hence, execution profiles are captured and a new execution profile-based clustering approach is chosen for test case selection, which uses three new features. These are Function frequency, Branches taken and Block percentage. The test cases are clustered using the extracted features. The experiments show that the proposed FBB selects smaller size of more relevant test cases which are more fault revealing compared to the existing Function Call Profile approach.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jkbo00:v:8:y:2018:i:2:p:18-31

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