SETAP: Software engineering teamwork assessment and prediction using machine learning

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

“…The results were reported in the prior studies (Petkovic et al 2014;Petkovic et al 2012;Petkovic et al 2018) presented the important measures for only the time intervals T2 and T3 of the whole project time period and they lack to cover the key measures for all 11 time intervals of the software project period. For software process measures at T2 as in the study (Petkovic et al 2014), the best Recall is 66.7 % in their approach whereas it is 68% in our approach. Moreover, for software product measures at T3, the best Recall is 60% by using Random Forest classifier although it is 68.8 %.…”

“…We also note that none of the classifiers obtain good results in the prediction of the F class labeled for time intervals T4 and T5 where T4 represents the beta launch milestone while T5 represents the final delivery milestone. 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.…”

“…Several approaches used several techniques in the educational environment to assess individual student performance by exploring the teaching classes' personal and quantitative issues such as grades, dropping frequency, teaching effectiveness, and e-learning techniques proposed by (Kotsiantis 2012;Lykourentzou, et al 2009;Castro, et al 2007;Baker & Yacef 2009;Baker 2002;Macfadyen & Dawson 2010;Delen 2010;Jovanovic et al 2012;Hu et al, 2014;Guo et al 2015). The research studies were conducted by (Petkovic et al 2014;Petkovic et al 2012;Petkovic et al 2018) used Random Forest classifier as recommended by (Gomes, et al, 2017) to predict and assess software engineering teamwork rather than individual students by collecting the objective and quantitative data about team activity measures through a joint project among San Francisco State University (SFSU), Fulda University (Fulda) and Florida Atlantic University (FAU). They created a machine learning database that contains team activity measurements, instructor observation, and their grades.…”

“…Their approach used four primary means: time cards, software tools, class data, and instructor observations to collect data about the participating software engineering teams. The previous studies (Petkovic et al 2014;Petkovic et al 2012;Petkovic et al 2018) used only Random Forest classifiers and considered only a few time intervals such as T2 and T3 to report their results. Also, they lack to detect the essential measures across all the time intervals extensively.…”

Female Student Participation in Software Engineering Projects: Opportunities to Model Project Evaluation and to Improve Early Prediction of Teamwork Failure

“…However, the literature lacks a comprehensive investigation that connects the dots of online collaboration and gender diversity among academic software engineering projects. Petkovic et al (2014) proposed and constructed a dedicated online environment for project development, namely SETAP. Their system considers characteristics of each deliverable handed during the project life cycle, namely product characteristics.…”