Programming Fault Prediction Using Quad Tree-Based Fluffy C-Means Clustering Algorithm
Keywords:Quad Tree, C-Means Algorithm, Fuzzy logic
Software measurements and blame information having a place with a past programming form are utilized to construct the product blame expectation show for the following arrival of the product. Unsupervised procedures like bunching might be utilized for blame expectation as a part of programming modules, all the more so in those situations where blame marks are not accessible. In this paper a Quad Tree-based Fuzzy C-Means calculation has been connected for anticipating deficiencies in program modules. The points of this paper are twofold. In the first place, Quad Trees are connected for observing the underlying group focuses to be contribution to the Fuzzy C-Means Algorithm. An information edge parameter oversees the quantity of introductory bunch focuses and by shifting the limit the client can create wanted beginning group focuses. The idea of grouping increase has been utilized to decide the nature of bunches for assessment of the Quad Tree-based introduction calculation when contrasted with other instatement procedures. These bunches got by Quad Tree-based calculation were found to have most extreme pick up qualities. Second, the Quad Tree based calculation is connected for anticipating shortcomings in program modules. The general blunder rates of this forecast approach are contrasted with other existing calculations and are observed to be better in the vast majority of the cases.