A SLR on Deep Learning Models Based on Textual Information For Effort Estimation in Scrum

Abstract

Deep Learning (DL) is a branch of Machine Learning where models are developed using neural networks made of several layers for prediction. DL models have been developed to predict effort estimation in software development. This paper presents a review of works which discuss the use of DL models for effort estimation for Scrum. The various textual information, the different DL techniques used. The methodology used for the review is snowballing. It was found that Deep-SE, a model which combines LSTM and RHN has been developed specifically for effort estimation. Also, a number of other DL techniques which have been experimented are discussed. A number of performance metrics were identified and also the perfomance of the various models were compared.