Software effort estimation Based on long short term memory and stacked long short term memory

Abstract

Software effort estimation (SEE) is an important process for predicting the effort required to develop or maintain software projects. It is considered one of the difficult challenges for software engineering because the more accurate the estimation, the greater the chances of success in developing software projects to complete and deliver them within the specified scheduling and budget. The diversity in software projects led to the use of many techniques to estimate the effort. The main objective of the research is to find the best technique to get the estimated effort as close to the real effort, using Machine learning Algorithms, artificial neural network Long Short-Term Memory(LSTM) and the artificial neural network Stacked Long Short-Term Memory (Stacked_LSTM), with six data sets are China, Kitchenham, Kemerer, Cocomo 81, Albrecht, Decharnais, and comparing the performance of the two machine learning algorithms performance, Neuralnet and Lasso from previous research, used the same data sets and using the same evaluation metrics, which are: the root mean square error (RMSE), mean absolute error (MAE) and R_Squared. The results demonstrated the superiority of the Stacked _LSTM algorithm by all metrics with the dataset of China, Kemerer and Albrecht, while in the data sets, Descharnais and Kitchenham, the LSTM algorithm outperformed the rest of the methods. While in the Cocomo 81 dataset, Neuralnet algorithm outperformed the rest of the techniques used, but the best rating was obtained. It is for Stacked _LSTM algorithm with China data.