Authors:
Zaineb Sakhrawi
1
;
Asma Sellami
2
and
Nadia Bouassida
2
Affiliations:
1
University of Sfax, Faculty of Economics and Management of Sfax, Sfax, Tunisia
;
2
University of Sfax, Higher Institute of Computer Science and Multimedia, Sfax, Tunisia
Keyword(s):
Software Enhancement Effort Estimation, Functional Change, Functional Size, COSMIC FSM Method, Scrum, Stacking Ensemble Model, Web Application.
Abstract:
The frequent changes in software projects may have an impact on the accuracy of the Software Enhancement Effort Estimation (SEEE) and hinder management of the software project. According to a survey on
agile software estimation, the most common cost driver among effort estimation models is software size. Indeed, previous research works proved the effectiveness of the COSMIC Functional Size Measurement (FSM)
method for efficiently measuring software functional size. It has been also observed that COSMIC sizing is
an efficient standardized method for measuring not only software size but also the functional size of an enhancement that may occur during the scrum enhancement project. Intending to increase the SEEE accuracy
the purpose of this paper is twofold. Firstly, it attempts to construct a stacking ensemble model. Secondly,
it intends to develop a localhost web application to automate the SEEE process. The constructed stacking
ensemble model takes the functional Size of an enh
ancement or a functional change, denoted as FS(FC), as
a primary independent variable. The stacking ensemble model combines three Machine Learning (ML) techniques: Decision Tree Regression, Linear Support Vector Regression, and Random Forest Regression. Results
show that the use of the FS(FC) as an input to SEEE using the stacking ensemble model provides significantly
better results in terms of MAE (Mean Absolute Error) = 0.206, Mean Square Error (MSE) = 0.406, and Root
Mean Square Error (RMSE) = 0.595.
(More)