Open AccessEarly stage software effort estimation using random forest technique based on use case points
Author(s) -
Satapathy Shashank Mouli,
Acharya Barada Prasanna,
Rath Santanu Kumar
Publication year - 2016
Publication title -
iet software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.305
H-Index - 43
eISSN - 1751-8814
pISSN - 1751-8806
DOI - 10.1049/iet-sen.2014.0122
Subject(s) - random forest , use case points , computer science , function point , schedule , software , perceptron , software development , software metric , machine learning , gradient boosting , artificial neural network , boosting (machine learning) , software sizing , software development process , reliability engineering , data mining , software quality , software construction , engineering , programming language , operating system
Due to the increasing complexity of software development activities, the need for effective effort estimation techniques has arisen. Underestimation leads to disruption in the project's estimated cost and delivery. On the other hand, overestimation causes outbidding and financial losses in business. Effective software effort estimation techniques enable project managers to schedule the software life cycle activities appropriately. Correctly assessing the effort needed to develop a software product is a major concern in software industries. Random forest (RF) technique is a popularly used machine learning technique that helps in improving the prediction values. The main objective of this study is to precisely assess the software projects development effort by utilising the use case point approach. The effort parameters are optimised utilising the RF technique to acquire higher prediction accuracy. Moreover, the results acquired applying the RF technique is compared with the multi‐layer perceptron, radial basis function network, stochastic gradient boosting and log‐linear regression techniques to highlight the performance attained by each technique.