TY - GEN
T1 - Classifying Composition of Software Development Team Using Machine Learning Techniques
AU - Yuhana, Umi Laili
AU - Sa'Adah, Umi
AU - Indraswari, Chandra Kirana Jatu
AU - Rochimah, Siti
AU - Rasyid, Maulidan Bagus Afridian
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Software development projects still reportedly have high failure rates. The ineffective composition of the software team has been recognized as the main aspect of the failure of the software project. In this study, a classification model of the composition of an effective software development team was developed. The model developed consists of three predictor variables: personality, role, and gender. Outcome variables to determine team effectiveness are seen in the quality of the team. To measure the quality of the team, two metrics were used: team development level assessment and team dysfunction assessment. The techniques used for classification are logistic regression and decision trees. The experimental results show that the best method is produced by a decision tree with the highest accuracy value of 70%. Therefore, the results conclude that the use of the decision tree method can determine an effective team as software development team.
AB - Software development projects still reportedly have high failure rates. The ineffective composition of the software team has been recognized as the main aspect of the failure of the software project. In this study, a classification model of the composition of an effective software development team was developed. The model developed consists of three predictor variables: personality, role, and gender. Outcome variables to determine team effectiveness are seen in the quality of the team. To measure the quality of the team, two metrics were used: team development level assessment and team dysfunction assessment. The techniques used for classification are logistic regression and decision trees. The experimental results show that the best method is produced by a decision tree with the highest accuracy value of 70%. Therefore, the results conclude that the use of the decision tree method can determine an effective team as software development team.
KW - Decision Tree Capacity Building
KW - Logistic Regression
KW - Team composition
UR - http://www.scopus.com/inward/record.url?scp=85149142333&partnerID=8YFLogxK
U2 - 10.1109/CENIM56801.2022.10037407
DO - 10.1109/CENIM56801.2022.10037407
M3 - Conference contribution
AN - SCOPUS:85149142333
T3 - Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2022
SP - 122
EP - 127
BT - Proceeding of the International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2022
Y2 - 22 November 2022 through 23 November 2022
ER -