@misc{10481/72894,
year = {2022},
month = {1},
url = {http://hdl.handle.net/10481/72894},
abstract = {Software engineering is one of the most significant areas, which extensively used in
educational and industrial fields. Software engineering education plays an essential
role in keeping students up to date with software technologies, products, and processes
that are commonly applied in the software industry. The software development project
is one of the most important parts of the software engineering course, because it covers
the practical side of the course. This type of project helps strengthening students' skills
to collaborate in a team spirit to work on software projects. Software project involves the
composition of software product and process parts. Software product part represents
software deliverables at each phase of Software Development Life Cycle (SDLC)
while software process part captures team activities and behaviors during SDLC. The
low-expectation teams face challenges during different stages of software project.
Consequently, predicting performance of such teams is one of the most important
tasks for learning process in software engineering education. The early prediction of
performance for low-expectation teams would help instructors to address difficulties
and challenges related to such teams at earliest possible phases of software project
to avoid project failure. Several studies attempted to early predict the performance
for low-expectation teams at different phases of SDLC. This study introduces swarm
intelligence -based model which essentially aims to improve the prediction performance
for low-expectation teams at earliest possible phases of SDLC by implementing Particle
Swarm Optimization-K Nearest Neighbours (PSO-KNN), and it attempts to reduce the
number of selected software product and process features to reach higher accuracy with
identifying less than 40 relevant features. Experiments were conducted on the Software
Engineering Team Assessment and Prediction (SETAP) project dataset. The proposed
model was compared with the related studies and the state-of-the-art Machine Learning
(ML) classifiers: Sequential Minimal Optimization (SMO), Simple Linear Regression
(SLR), Naïve Bayes (NB), Multilayer Perceptron (MLP), standard KNN, and J48. The
proposed model provides superior results compared to the traditional ML classifiers
and state-of-the-art studies in the investigated phases of software product and process
development.},
publisher = {PeerJ},
keywords = {Optimization},
keywords = {PSO},
keywords = {Software engineering},
keywords = {Data mining},
keywords = {Machine learning},
keywords = {Artificial intelligence},
title = {Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects},
doi = {10.7717/peerj-cs.857},
author = {Al-Ahmad, Bilal I. and Al-Zoubi, Ala´ M.},
}