Downloads: 129 | Views: 134
Research Paper | Computer Science & Engineering | India | Volume 6 Issue 7, July 2017
Application of Two-Stage Data Pre-processing Approach for Software Fault Prediction
Sajna P | Shahad P 
Abstract: Software fault prediction is a valuable exercise in software quality assurance to better allocate limited testing resources. Classification is one of the effective strategies for predicting software errors. The classification models are trained based on data sets obtained by historical repositories of mining software. In this project, a new Two-stage data preprocessing approach is applied with classification models such as Naive Bayes, Decision Tree, Knn Classifier and SVM to improve the prediction accuracy of each classification model. The data preprocessing approach in two stages incorporates both the selection of features and the reduction of instances. Specifically, in the feature selection stage, first relevance analysis is done, second, a threshold-based clustering method is proposed, termed novel threshold-based clustering algorithm, to drive redundancy control. In the instance reduction stage, random sampling is applied to maintain the balance between defective and non defective instances. To demonstrate this project chose real-world software project dataset, such as Eclipse.
Keywords: Data Mining, Fault Detection, Prediction Algorithms, Data preprocessing, Classifiers
Edition: Volume 6 Issue 7, July 2017,
Pages: 635 - 641
Similar Articles with Keyword 'Data Mining'
Survey Paper, Computer Science & Engineering, India, Volume 11 Issue 8, August 2022Pages: 947 - 949
COVID-19 Prediction using Machine Learning Algorithms
Saily Suresh Patil
Research Paper, Computer Science & Engineering, India, Volume 10 Issue 8, August 2021Pages: 1068 - 1070
Predicting the Course Knowledge Level of Students using Data Mining Techniques