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An Effective Approach to Test Suite Reduction and Fault Detection Using Data Mining Techniques

An Effective Approach to Test Suite Reduction and Fault Detection Using Data Mining Techniques

B. Subashini, D. Jeya Mala
Copyright: © 2017 |Volume: 8 |Issue: 4 |Pages: 31
ISSN: 1942-3926|EISSN: 1942-3934|EISBN13: 9781522512707|DOI: 10.4018/IJOSSP.2017100101
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MLA

Subashini, B., and D. Jeya Mala. "An Effective Approach to Test Suite Reduction and Fault Detection Using Data Mining Techniques." IJOSSP vol.8, no.4 2017: pp.1-31. http://doi.org/10.4018/IJOSSP.2017100101

APA

Subashini, B. & Mala, D. J. (2017). An Effective Approach to Test Suite Reduction and Fault Detection Using Data Mining Techniques. International Journal of Open Source Software and Processes (IJOSSP), 8(4), 1-31. http://doi.org/10.4018/IJOSSP.2017100101

Chicago

Subashini, B., and D. Jeya Mala. "An Effective Approach to Test Suite Reduction and Fault Detection Using Data Mining Techniques," International Journal of Open Source Software and Processes (IJOSSP) 8, no.4: 1-31. http://doi.org/10.4018/IJOSSP.2017100101

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Abstract

Software testing is used to find bugs in the software to provide a quality product to the end users. Test suites are used to detect failures in software but it may be redundant and it takes a lot of time for the execution of software. In this article, an enormous number of test cases are created using combinatorial test design algorithms. Attribute reduction is an important preprocessing task in data mining. Attributes are selected by removing all weak and irrelevant attributes to reduce complexity in data mining. After preprocessing, it is not necessary to test the software with every combination of test cases, since the test cases are large and redundant, the healthier test cases are identified using a data mining techniques algorithm. This is healthier and the final test suite will identify the defects in the software, it will provide better coverage analysis and reduces execution time on the software.

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