Analysis of Data Preprocessing Techniques with Software Bug Model Datasets - Salahuddin Shaikha, Liu Changanb , Maaz Rasheed Malikc (International Journal of Advanced Science and Technology, 2020)
In reality, software metrics dataset is highly complex for us to situate lucid distinguish amongthe software defective and non-defective modules. Software metrics is imperative to preprocessthe metrics data from the perspective of data model revolution. We have highlighted the datapreprocessing methods and its experiential study on software metrics dataset. These datapreprocessing methods can be used to enhance the efficiency and effectiveness of the classifier insoftware bug datasets for overcome classification problems. Experiments analysis of the featureselection shows us that the enhancement of the TP rate as well as Positive Accuracy could notincrease, but declined as compared to the usage of preprocessing in majority scenarios ofsoftware bug predictions when contrasted against other techniques. During using of Classifiers,here we have analyzed that Random Forests, IBK, Logistic and Naïve Baes are the benefits forimproving the efficiency and better than all other classifiers for software bug model datasets.Here Propositionalization efficiency is better than other methods and especiallypropositionalization by function logistic classifier