Work place: JaganNathUniversity, Jaipur, India
E-mail: triptigautam@yahoo.co.in
Website:
Research Interests: Data Mining, Data Structures and Algorithms
Biography
Tripti Lamba is pursuing Ph.D from JIMS, Jaipur. She has done her M. Tech from Punjabi Univ, Patiala. She has 15 yrs of experience. Currently working with Institute of Information and Technology (IITM)as an Asst. Professor –IT. Her areas of interest are web technologies and data mining. She has published five papers in reputed International / National journals.
By Tripti Lamba Kavita A.K.Mishra
DOI: https://doi.org/10.5815/ijisa.2019.02.05, Pub. Date: 8 Feb. 2019
Machine Learning is a division of Artificial Intelligence which builds a system that learns from the data. Machine learning has the capability of taking the raw data from the repository which can do the computation and can predict the software bug. It is always desirable to detect the software bug at the earliest so that time and cost can be reduced. Feature selection technique wrapper and filter method is used to find the most optimal software metrics. The main aim of the paper is to find the best model for the software bug prediction. In this paper machine learning techniques linear Regression, Random Forest, Neural Network, Support Vector Machine, Decision Tree, Decision Stump are used and comparative analysis has been done using performance parameters such as correlation, R-squared, mean square error, accuracy for software modules named as ant, ivy, tomcat, berek, camel, lucene, poi, synapse and velocity. Support vector machine outperform as compare to other machine learning model.
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