Open AccessMEASURING CODE QUALITY USING SUPPORT VECTOR MACHINE CLASSIFIER FOR IMPROVED CODING TECHNIQUES
Author(s) -
A.K. Iavarasi,
S. L. Aarthy
Publication year - 2016
Publication title -
international journal of computer and communication technology
Language(s) - English
Resource type - Journals
eISSN - 2231-0371
pISSN - 0975-7449
DOI - 10.47893/ijcct.2016.1374
Subject(s) - classifier (uml) , support vector machine , margin classifier , computer science , artificial intelligence , structured support vector machine , quadratic classifier , binary classification , pattern recognition (psychology) , machine learning , coding (social sciences) , binary number , source code , data mining , statistics , mathematics , arithmetic , operating system
classification is the problem of identifying a set of categories to a new comments.To improve the efficiency of the code,quality metrics are applied for evaluation.The binary classifier,predicts the false positive rates with lesser accuracy,and limited number of classes only to predict the accuracy for classifier.To address this problem,support vector machine classifier is used,which helps in detecting the false positive rates,improving code quality and the accuracy will also increased.