Convolutional Neural Network Based Approval Prediction of Enhancement Reports

Abstract

For a given software enhancement report, identifying its possible approval status could help software developers by suggesting feature enhancements to compete in the software industry. An automatic solution for the approval prediction of enhancements could assist all the participants in resolving enhancements. The key challenges are the preprocessing of noisy textual information and the state-of-the-art feature models to combine the syntactical and semantic word information available in the given text. To this end, we propose a deep learning based approach for the approval prediction of enhancement reports that incorporates the users' sentiments involved in the text. First, we preprocess the textual information of all enhancement reports to avoid noise. Second, we compute the sentiment of each enhancement report using Senti4SD. Third, we combine the bag-of-words (BOW) representation and traditional word2vec based representation to learn the novel deep representation (a recurrent neural network (RNN) with attention based representation) of preprocessed text. Using an attention mechanism enables the model to remember the context over a long sequence of words in an enhancement report. Fourth, based on sentiment and deep representation, we train a deep learning based classifier for the approval prediction of enhancement reports. Finally, we reuse the 40, 000 enhancement reports from 10 real software applications to evaluate the proposed approach. The cross-application evaluation suggests that the proposed approach is accurate and outperforms the state-of-the-art. The results of the proposed approach improve the precision from 86.52% to 90.56%, recall from 66.45% to 80.10%, and f-measure from 78.12% to 85.01%.