An Analysis of Deep Neural Network for Recommending Developers to Fix Reported Bugs

Abstract

Occurrence of bugs during the production cycle of software projects is a serious concern of the present time. According to an estimate, a very large number of bugs are recorded while dealing with complex and popular software releases. To locate these bugs and to solve them in efficient manner software industries incorporate the process of bug triage in software testing. Bug triage is intended to recommend the bug reports to an appropriate developer effectively to fix them successfully. However, it becomes labor-intensive and expensive to manually allocate these bug reports to the developer. Deep learning methods have been extensively used and experimented to various domains such as medical diagnosis, earthquake prediction and many more. To handle the above said bugs concerns, many studies have been carried out in order to automate the bug triaging process. Several researchers have directed their efforts by applying deep learning methods in different settings for autonomous recommendation for developers to remove or fix their bugs. In this paper we have proposed a Convolutional Neural Network model for recommending Top 10 developers to fix the reported bugs. For better performance of the model Word2Vec and Glove embeddings are combined with the neural network. The performance of CNN+Word2vec and CNN+Glove models is calculated by averaging the accuracy for 10 developers at five distinct learning rates. The reported results demonstrate that the combination of Convolution with word2vec embedding gives better average accuracy in the testing phase.