AN EFFICIENT BLACK-BOX REGRESSION MAXIMIZATION (BBM) FOR COMBINATORIAL TESTING USING GREEDY SEARCH ALGORITHM
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Abstract
Comparing behaviours of program models has become an important task in software maintenance and regression testing. Combinatorial testing focuses on recognizing faults that happen due to interaction of values of a small number of input parameters.In this paper presents the Black-Box Regression Maximization (BBM) Algorithm with Density-based Spatial Clustering Algorithm (DSC) using Greedy Search optimization algorithm focuses on combinatorial testing and proactively exposes behavioural deviations by checking inside block transitions. In this method presents new approach of BBM with Internal block transitions to measure the dissimilarity statements in large program data. To identify specific faults, an adaptive testing rule repeatedly constructs and tests configurations in order to determine, for each interaction of interest, whether it is faulty or not.
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