Year: 2018
Type: conference-paper
Abstract: Performance of the bug prediction model is directly depends on the misclassification of bug reports. Misclassification issue surely scarifies the accuracy of the system. To resolve this issue the manual examination of bug reports are required, but it is very time consuming and tedious job for a developer and tester. In this paper the hybrid approach of merging text mining, natural language processing and machine learning techniques is used to identify bug report as bug or non-bug. The four incorporates fields with textual fields are added to bug reports to improve the performance of classifier. TF-IDF and Bigram feature extraction methods are used with feature selection and K-nearest neighbor (K-NN) classifier. The performance of the proposed system is evaluated by using Precision, Recall and F-measure by using five datasets. It is observed that the performance of K-NN classifier is changed according to the dataset and addition of bigram method improve the performance of classifier.
Source: Procedia Computer Science
Authors: Ashima Kukkar, Rajni Mohana
Institution: Jaypee University of Information Technology
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2807010072