Software Effort Estimation Using Multilayer Perceptron and Long Short Term Memory - PREDESCU, Eduard-Florin; STEFAN, Alexandru; ZAHARIA, Alexis-Valentin (Informatica economica, 2019)
Software effort estimation is a hot topic for study in the last decades. The biggest challenge for project managers is to meet their goals within the given time limit. Machine learning software can take project management software to a whole new level. The objective of this paper is to show the applicability of using neural network algorithms in software effort estimation for project management. To prove the concept we are using two machine learning algorithms: Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM). To train and test these machine learning algorithms we are using the Desharnais dataset. The dataset consists of 77 sample projects. From our results we have seen that Multilayer Perceptron algorithm has better performance than Long Short-Term Memory, by having a better determination coefficient for software effort estimation. Our success in implementing a machine learning that can estimate the software effort brings real benefits in the field ofproject management assisted by computer, further enhancing the ability of a manager to organize the tasks within the time limit of the project. Although, we need to take into consideration that we had a limited dataset that we could use so a real advancement would be to implement and test these algorithms using a real life company as a subject of testing.