Open AccessUsing CNN to Predict the Resolution Status of Bug Reports
Author(s) -
Muhammad Arshad,
Zhiqiu Huang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1828/1/012106
Subject(s) - computer science , security bug , software bug , convolutional neural network , software , resource (disambiguation) , artificial intelligence , data science , computer security , programming language , information security , security service , software security assurance , computer network
Bug tracking systems (BTS) are a resource for receiving bug reports that help to improve software applications. They usually contain reports reported by the end-users or developers. Bug Reports contain some suggestions, complaints, etc. The problem is that every submitted bug report is not accepted to implement. Mostly bug reports are rejected because they are incomplete, duplicate, expired, etc. while only a few are accepted to implement. But the developers have to check every bug report manually that needs many resources (i.e. labour, money, time). In this study, we proposed a convolutional neural network (CNN) based approach to automatically classify bug reports as accepted and rejected. Results show that the proposed approach achieves the highest performance as compared to closely related works.