Early Prediction of a Team Performance in the Initial Assessment Phases of a Software Project for Sustainable Software Engineering Education

Abstract

Software engineering is a competitive field in education and practice. Software projects are key elements of software engineering courses. Software projects feature a fusion of process and product. The process reflects the methodology of performing the overall software engineering practice. The software product is the final product produced by applying the process. Like any other academic domain, an early evaluation of the software product being developed is vital to identify the at-risk teams for sustainable education in software engineering. Guidance and instructor attention can help overcome the confusion and difficulties of low performing teams. This study proposed a hybrid approach of information gain feature selection with a J48 decision tree to predict the earliest possible phase for final performance prediction. The proposed technique was compared with the state-of-the-art machine learning (ML) classifiers, naïve Bayes (NB), artificial neural network (ANN), logistic regression (LR), simple logistic regression (SLR), repeated incremental pruning to produce error reduction (RIPPER), and sequential minimal optimization (SMO). The goal of this process is to predict the teams expected to obtain a below-average grade in software product development. The proposed technique outperforms others in the prediction of low performing teams at an early assessment stage. The proposed J48-based technique outperforms others by making 89% correct predictions.

The Impact of Software Team Project Measurements on Students' Performance in Software Engineering Education

“…The previous studies (Petkovic et al 2014;Petkovic et al 2012;Petkovic et al 2018) lacked to present the classification on the remaining time intervals, focused only on T2 and T3, used only Random Forest classifier, and implemented stratified sampling in cross validation, since class labeled F is minority while the class labeled A is the majority as assigned in the dataset. Also, the study (Naseer et al 2020) investigated only the software product measures for the first five intervals T1, T2, T3, T4, and T5 by implementing several different classifiers. On the other side, our proposed assessment model acts as comprehensive approach that covers all the time intervals from T1 until T11 for both of software product and process, and it demonstrates classification and feature selection techniques.…”

“…Besides, the study (Naseer et al 2020) investigated only the software product measures for the first five intervals T1, T2, T3, T4, and T5 by implementing several different classifiers. The best Recall in their assessment model reported as 3.1 %, 15.6 %, 53.1 %, 78.1 %, and 75% for the time intervals T1, T2, T3, T4, and T5 respectively.…”

“…The process measures of T2 were: lateissue Count, issue Count, standard Deviation Help Hours Total By Week average Help Hourstotal By Week, standard Deviation Help Hours Average By Week, coding Deliverables Hours Average, help Hours Standard Deviation, and standard Deviation MeetingHoursAverageByWeek. Furthermore, the study (Naseer et al 2020) used J48 decision tree in sequential phases of assessments from T1 to T5 to select the software product features for the classification, they indicated few product measures as listed in their study. The proposed model detects the mutual measures after implementing evolutionary and PSO search as summarized in Table 2 and Table 3.…”

“…The common measures selected by our employed feature selection techniques and the feature selection technique employed by (Naseer et al 2020) Our feature selection technique detected many of the essential measures selected by previous work. Also, the proposed model detects many other important features that undetected by the previous studies (Petkovic, et al 2018;Naseer et al 2020). The literature proved that evolutionary and PSO search could select significant features.…”

“…Also, they lack to detect the essential measures across all the time intervals extensively. Also, a study (Naseer et al 2020) introduced a combined approach that employed feature selection and then classification on the same dataset. This study considers only five time intervals from T1 to T5 and captures only the product software measures.…”