Delving into Human Factors through LSTM by Navigating Environmental Complexity Factors within Use Case Points for Digital Enterprises

Meeting customer requirements in software project management, even for large digital enterprises, proves challenging due to unpredictable human factors. It involves meticulous planning and environmental factor analysis, ultimately benefiting both companies and customers. This paper came as a natural...

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Bibliographic Details
Published in:Journal of theoretical and applied electronic commerce research 2024-02, Vol.19 (1), p.381-395
Main Authors: Rankovic, Nevena, Rankovic, Dragica
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Case studies
Complexity
Customers
Data augmentation
Datasets
Deep learning
digital enterprises
Electronic commerce
environmental complexity factors
Factor analysis
Fuzzy logic
Human factors
Machine learning
Neural networks
Optimization
post-agnostic models
Project management
recurrent neural networks (RNNs)
Research methodology
Software
Software development
use case points (UCPs)
Variables
Citations: Items that this one cites
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Summary:Meeting customer requirements in software project management, even for large digital enterprises, proves challenging due to unpredictable human factors. It involves meticulous planning and environmental factor analysis, ultimately benefiting both companies and customers. This paper came as a natural extension of our previous work where we left ourselves curious about what impact environmental complexity factors (ECFs) have in a use case point (UCP) approach. Additionally, we wanted to possibly decrease the mean magnitude relative error (MMRE) with deep learning models such as long-short-term-memory (LSTM) and gradient recurrent unit (GRU). The data augmentation technique was used to artificially increase the number of projects, since in the industry world, digital enterprises are not keen to share their data. The LSTM model outperformed the GRU and XGBoost models, while the average MMRE in all phases of the experiment for all models achieved 4.8%. Moreover, the post-agnostic models showed the overall and individual impact of eight ECFs, where the third ECF “team experience” on a new project has been shown as the most influential one. Finally, it is important to emphasize that effectively managing human factors within ECFs in UCPs can have a significant impact on the successful completion of a project.
ISSN:0718-1876
0718-1876
DOI:10.3390/jtaer19010020