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

Mehwish Naseer, Wu Zhang and Wenhao Zhu
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Mehwish Naseer: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Wu Zhang: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Wenhao Zhu: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

Sustainability, 2020, vol. 12, issue 21, 1-15

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.

Keywords: sustainable education; educational data mining; software engineering; machine learning; predictive modeling; boosting; ensembles (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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