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 (K.L.N College of Engineering, Madurai, India) and D. Jeya Mala (Department of Computer Applications, Thiagarajar College of Engineering, Madurai, India)
Copyright: © 2017 |Pages: 31
DOI: 10.4018/IJOSSP.2017100101
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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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2. Materials And Methods

2.1. Background

Software testing is an action to confirm the actual outcomes with the expected outcomes and assure that the product framework is without defect. Test Case is a cluster of activities executed to check a specific aspect or effectiveness of programming application. The objective of any product venture is to formulate test cases which meet client prerequisite. In this article, huge records of the test case are automatically generated by using the combinatorial testing method, it may be redundant and it is required to eliminate repeated test cases. Test suites are minimized and the faults are forecasted by using the classification technique. Reduction in test suite will minimize the time of execution, effort and it will provide better coverage analysis.

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