Paper Info
Title
Support Vector Regression for Predicting the Enhancement Duration of Software Projects
Abstract
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.
Year
DOI
Venue
2017
10.1109/ICMLA.2017.0-101
2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)
Keywords
Field
DocType
Software engineering,software enhancement duration prediction,support vector egression,statistical regression,ISBSG
Kernel (linear algebra),Data modeling,Data set,Computer science,Regression analysis,Support vector machine,Software,Artificial intelligence,Software maintenance,Statistical hypothesis testing,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-5386-1419-8
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Cuauhtémoc López-Martín18611.13
Shadi Banitaan2479.14
Andres Garcia-Floriano300.34
Cornelio Yáñez-Márquez415326.34