TY - GEN
T1 - Support vector regression for predicting the enhancement duration of software projects
AU - Lopez-Martin, Cuauhtemoc
AU - Banitaan, Shadi
AU - Garcia-Floriano, Andres
AU - Yanez-Marquez, Cornelio
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/1/16
Y1 - 2018/1/16
N2 - Software engineering (SE) has been defined as the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software. Enhancement is a type of software maintenance. SE involves software planning (SP), and SP includes prediction. In this study, we propose the application of two types of support vector regression (SVR) termed ϵ-SVR and ν-SVR to predict the duration of the software enhancement. A SVR is a type of support vector machine, which is a machine learning technique. Two data sets of software projects were used for training and testing the ϵ-SVR and ν-SVR. The prediction accuracy of the SVRs was compared to that of a statistical regression. Based on statistical tests, results showed that a ϵ-SVR with linear kernel was statistically better than that of a statistical regression model when software projects were enhanced on Mid Range platform and coded in programming languages of third generation.
AB - Software engineering (SE) has been defined as the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software. Enhancement is a type of software maintenance. SE involves software planning (SP), and SP includes prediction. In this study, we propose the application of two types of support vector regression (SVR) termed ϵ-SVR and ν-SVR to predict the duration of the software enhancement. A SVR is a type of support vector machine, which is a machine learning technique. Two data sets of software projects were used for training and testing the ϵ-SVR and ν-SVR. The prediction accuracy of the SVRs was compared to that of a statistical regression. Based on statistical tests, results showed that a ϵ-SVR with linear kernel was statistically better than that of a statistical regression model when software projects were enhanced on Mid Range platform and coded in programming languages of third generation.
KW - ISBSG
KW - Software engineering
KW - software enhancement duration prediction
KW - statistical regression
KW - support vector egression
UR - http://www.scopus.com/inward/record.url?scp=85048487627&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2017.0-101
DO - 10.1109/ICMLA.2017.0-101
M3 - Contribución a la conferencia
AN - SCOPUS:85048487627
T3 - Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
SP - 562
EP - 567
BT - Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
A2 - Palade, Vasile
A2 - Chen, Xuewen
A2 - Luo, Feng
A2 - Wani, M. Arif
A2 - Luo, Bo
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Y2 - 18 December 2017 through 21 December 2017
ER -