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

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