Inter project defect classification based on word embedding

Defect classification is a process to classify defects based on predefined categories. It is time consuming and manual process. Many automatic defect classification methods have been proposed to speed up the process of defect classification. However, these methods have not utilized the inter relations among the defect reports. In the literature for defect classification, Term Frequency-Inverse Document Frequency and Bag of words based approaches have been proposed. In this paper, we have proposed word embedding based model for the defect classification which is proven to be better in comparison with the existing methods. We have also proposed models for inter project defect classification by considering combination of different datasets of the same domain. We tested the proposed approach on 4096 defect reports using K nearest neighbor, Random forest, Decision tree, Support vector machine, Stochastic gradient descent and Ada boost classifiers in terms of accuracy, precision, recall and F1-score. Experimental results show that Decision tree achieves highest accuracy 98.21% while trained and tested on GloVe word embedding. We have also generated new word embedding using the bug reports corpus. Further, we compare the proposed model with Lopes et.al., 2020 and results show that our model outperforms.