RT Journal Article
A1 Mohammad Azzeh
A1 Ali Bou Nassif
A1 Shadi Banitaan

PB iet
T1 Comparative analysis of soft computing techniques for predicting software effort based use case points
JN IET Software
VO 12
IS 1
SP 19
OP 29
AB The size of a software project is a key measure of predicting software effort at the requirements and analysis phase. Use case points (UCP) is among software size metrics that achieved good reputation because of the increasing popularity of use case driven development methodologies in software industry. Nevertheless, there is no consistent method that can effectively translate the UCP into its corresponding effort. Previous estimation models were built using a very limited number of projects, and they were not well examined. The soft computing techniques were rarely applied for such problem and their performances have not been well investigated using a systematic procedure. This study looks into the accuracy and stability of some soft computing methods for the problem of effort estimation based on UCP. Four neural network methods, adaptive neuro fuzzy inference system and support vector regression have been used in this comparative study. The results suggest that most used soft computing techniques can work well with good accuracy for such problem. Among them, the general regression neural network is the superior one with stable ranking across different accuracy measures. Also, it has been found that using adjustment variables with basic UCP variables, solely or together, have positive impact on the accuracy and stability.
K1 software size metrics
K1 soft computing
K1 software industry
K1 software project
K1 analysis phase
K1 use case points
K1 software effort prediction
K1 support vector regression
K1 adaptive neuro fuzzy inference system
K1 use case driven development
K1 requirements phase
K1 regression neural network
K1 UCP
DO https://doi.org/10.1049/iet-sen.2016.0322
UL https://digital-library.theiet.org/;jsessionid=5rd928ij6j7bl.x-iet-live-01content/journals/10.1049/iet-sen.2016.0322
LA English
SN 1751-8806
YR 2018
OL EN