Software Effort Estimation Using Multilayer Perceptron and Long Short Term Memory

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 appl...

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Bibliographic Details
Published in:Informatica economica Vol. 23; no. 2/2019; pp. 76 - 87
Main Authors: PREDESCU, Eduard-Florin, STEFAN, Alexandru, ZAHARIA, Alexis-Valentin
Format: Journal Article
Language:English
Published: Bucharest INFOREC Association 30.06.2019
Inforec Association
Subjects:
Accuracy
Algorithms
Artificial intelligence
Automation
Datasets
Desharnais dataset
Efficiency
Estimates
Estimation
Long short-term memory
Machine learning
Multilayer perceptron
Multilayer perceptrons
Neural network algorithms
Neural networks
Project management
Short term
Software
Software development
Software effort estimation
ISSN:1453-1305, 1842-8088
Online Access:Get full text
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Summary: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.
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ISSN:1453-1305
1842-8088
DOI:10.12948/issn14531305/23.2.2019.07