Abstract:
Software applications obtain enhancement requests on a large scale to fulfil user requirements
through a bug tracking system, which is an important application for keeping
record of the bugs in the software development process. Conventionally, software developers
used to manually check these requests. However, manual inspection of these
requests turns out to be a boring, hectic and time-consuming activity. Therefore, an
automatic prediction system is required which can reject or approve an enhancement
report without manual check. Existing approaches target this problem through machine
learning techniques such as Multinomial Naive Bayes and Support Vector Machine. In
this work we utilize a deep learning based approach for approval prediction of enhancement
reports that include the following steps: First, we preprocess each enhancement
report using natural language processing techniques. Then we perform emotion analysis
of each preprocessed enhancement reports using Senti4SD to compute its sentiment.
Next we combine the summary and description of enhancement report as a sequence of
words to learn the features with some attention mechanism using deep recurrent neural
network (RNN). Finally, on the basis of features selection model and sentiment we train
deep neural network based classifier, the convolutional neural network (CNN) to predict
the approval prediction of enhancement report. We evaluate the proposed approach on
an open-source bug tracking system. Results of numerical evaluation suggest that the
proposed approach significantly outperforms the existing approaches and achieve the
accuracy of 82.15%, precision 90.56%, recall 80.10% and f-measure as 85.01%.
