Next Article in Journal
Strategic Decisions for Sustainable Management at Significant Tourist Sites
Next Article in Special Issue
The Digitalization Sustainability Matrix: A Participatory Research Tool for Investigating Digitainability
Previous Article in Journal
Effect of Road Traffic on Air Pollution. Experimental Evidence from COVID-19 Lockdown
Previous Article in Special Issue
Big Data and the Ethical Implications of Data Privacy in Higher Education Research
 
 
Article

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

School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
*
Authors to whom correspondence should be addressed.
Sustainability 2020, 12(21), 8986; https://doi.org/10.3390/su12218986
Received: 19 September 2020 / Revised: 25 October 2020 / Accepted: 26 October 2020 / Published: 29 October 2020
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. View Full-Text
Keywords: sustainable education; educational data mining; software engineering; machine learning; predictive modeling; boosting; ensembles sustainable education; educational data mining; software engineering; machine learning; predictive modeling; boosting; ensembles
Show Figures

Figure 1

MDPI and ACS Style

Naseer, M.; Zhang, W.; Zhu, W. Prediction of Coding Intricacy in a Software Engineering Team through Machine Learning to Ensure Cooperative Learning and Sustainable Education. Sustainability 2020, 12, 8986. https://doi.org/10.3390/su12218986

AMA Style

Naseer M, Zhang W, Zhu W. Prediction of Coding Intricacy in a Software Engineering Team through Machine Learning to Ensure Cooperative Learning and Sustainable Education. Sustainability. 2020; 12(21):8986. https://doi.org/10.3390/su12218986

Chicago/Turabian Style

Naseer, Mehwish, Wu Zhang, and Wenhao Zhu. 2020. "Prediction of Coding Intricacy in a Software Engineering Team through Machine Learning to Ensure Cooperative Learning and Sustainable Education" Sustainability 12, no. 21: 8986. https://doi.org/10.3390/su12218986

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop