Prediction of Coding Intricacy in a Software Engineering Team through Machine Learning to Ensure Cooperative Learning and Sustainable Education

Abstract

Coding deliverables are vital part of the software project. Teams are formed to develop a software project in a term. The performance of the team for each milestone results in the success or failure of the project. Coding intricacy is a major issue faced by students as coding is believed to be a complex field demanding skill and practice. Future education demands a smart environment for understanding students. Prediction of the coding intricacy level in teams can assist in cultivating a cooperative educational environment for sustainable education. This study proposed a boosting-based approach of a random forest (RF) algorithm of machine learning (ML) for predicting the coding intricacy level among software engineering teams. The performance of the proposed approach is compared with viable ML algorithms to evaluate its excellence. Results revealed promising results for the prediction of coding intricacy by boosting the RF algorithm as compared to bagging, J48, sequential minimal optimization (SMO), multilayer perceptron (MLP), and Naïve Bayes (NB). Logistic regression-based boosting (LogitBoost) and adaptive boosting (AdaBoost) are outperforming with 85.14% accuracy of prediction. The concerns leading towards high coding intricacy level can be resolved by discussing with peers and instructors. The proposed approach can ensure a responsible attitude among software engineering teams and drive towards fulfilling the goals of education for sustainable development by optimizing the learning environment.

Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects

“…Comparing to the approach by Naseer, Zhang & Zhu (2020b) , the mutual key product features across all the five investigated phases are: FemaleTeamMembersPercent , averageInPersonMeetingHoursTotalbyWeek , averageMeetingHoursTotalByWeek , uniqueCommitMessageCount , averageGlobalLeadAdminHoursResponseCountByWeek , globalLeadAdminHoursResponseCount , and teamDistribution .…”

“…The research studies ( Petkovic et al, 2014 ; Petkovic et al, 2016 ; Petkovic et al, 2018 ; Al-Taharwa, 2020 ; Naseer, Zhang & Zhu, 2020a ; Naseer, Zhang & Zhu, 2020b ) aimed to early predict performance of low-expectation teams by utilizing the SETAP project dataset, they applied various classification and features selection techniques on different time intervals of software project.…”

“…In addition, Naseer, Zhang & Zhu (2020b) used software product dataset to predict coding intricacy through highlighting code deliverable characteristics by employing LogitBoost, AdaBoost, Bagging, Random forest, J48, sequential minimal optimization (SMO), multilayer perceptron (MLP), and Naïve Bayes (NB) classifiers, they used the last time interval (T11) as training and testing set as it covered composite aspects of implementation through final delivery phases.…”

“…The early prediction of low-performing teams reduces the possibilities of project failure which is considered a major challenge against successful software engineering. Several studies ( Petkovic et al, 2014 ; Petkovic et al, 2016 ; Petkovic et al, 2018 ; Al-Taharwa, 2020 ; Naseer, Zhang & Zhu, 2020a ; Naseer, Zhang & Zhu, 2020b ) attempted to early predict the performance of students teams in software engineering education by implementing different classification models and features selection techniques by demonstrating different phases of software project.…”

Teaching Algorithms to Develop the Algorithmic Thinking of Informatics Students

“…Based on the above research, it is necessary to look for ways to teach algorithms that would lead to a change in the way students learn algorithms. It is necessary to teach algorithms, especially for students of informatics study programs, in such a way that they develop algorithmic thinking, the ability to solve problems and creativity, which is a sub-dimension of computational thinking [36]. As with learning generally, learning the algorithms of students in school mathematics can take place at different cognitive levels.…”