Classifying the bugs using multi-class semi supervised support vector machine

Abstract

It is always important in the Software Industry to know about what types of bugs are getting reported into the applications developed or maintained by them. Categorizing bugs based on their characteristics helps Software Development team to take appropriate actions in order to reduce similar defects that might get reported in future releases. Defects or Bugs can be classified into many classes, for which a training set is required, known as the Class Label Data Set. If Classification is performed manually then it will consume more time and efforts. Also, human resource having expert testing skills & domain knowledge will be required for labelling the data. Therefore Semi Supervised Techniques are been used to reduce the work of labelling dataset, which takes some labeled with unlabeled dataset to train the classifier. In this paper Self Training Algorithm is used for Semi Supervised Learning and Winner-Takes-All strategy is applied to perform Multi Class Classification. This model provides Classification accuracy up to 93%.