RNN-CNN MODEL:A Bi-directional Long Short-Term Memory Deep Learning Network For Story Point Estimation

Abstract

In recent years, an increased interest in the adaption of agile software development by companies. Using iterative methodology enables them to do issue-based estimation and respond quickly to changes in the requirements. Agile methodology adopts Story Point Approach to estimate the effort that involves a user story or a resolving issue. Unlike traditional estimation, Agile Methodology focuses on individual programming task estimation instead of whole project estimation. In this work, we approach story point estimation using the RNN-CNN model. We consider the contextual information in a user story in both forward and backward directions to build the RNN-CNN model. The proposed model adopts a Bi-directional Long Short-Term Memory (BiLSTM), a tree-structured Recurrent Neural Network (RNN) with Convolutional Neural Network (CNN), tries to predict a story point for a user story description. Here, BiLSTM forward and backward feature learning will make network preserve the sequence data and CNN makes feature extraction accurate. The experimental results show the improvement in estimating the story points with a user story as an input using the proposed RNN-CNN. Furthermore, the analysis shows that the proposed RNN-CNN model outperforms the existing model and gives 74.2 % R2 Score on the Bamboo data set.