SOFTWARE FAULT PREDICTION USING CASE-BASED REASONING: A COMPARATIVE ANALYSIS
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Abstract
Software fault estimation is important to increase the software reliability. Therefore, increasing the software reliability tends to increase the software quality. For testing the quality of software module, I have used four established similarity functions namely Euclidean method, Canberra method, Exponential method and a Manhattan method. The selection of a particular similarity measure may affect the performance precision of a CBR-Based fault prediction. It has been observed that, all the distance functions perform nearly the equal for the same data set which indicates efficiency of indigenous tool.
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