Authors:
Mourad Badri
;
Linda Badri
and
William Flageol
Affiliation:
University of Quebec, Canada
Keyword(s):
Use Cases, Use Case Metrics, Class Diagrams, Objective Class Points, Source Code Size, Test Code Size, Prediction Models, Linear Regression.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Requirements Engineering
;
Service-Oriented Software Engineering and Management
;
Software and Systems Development Methodologies
;
Software Engineering
;
Software Process Improvement
;
Software Quality Management
;
Symbolic Systems
Abstract:
Source code size, in terms of SLOC (Source Lines of Code), is an important parameter of many parametric software development effort estimation methods. Moreover, test code size, in terms of TLOC (Test Lines of Code), has been used in many studies to indicate the effort involved in testing. This paper aims at comparing empirically the Use Case Metrics (UCM) method, a use case model based method that we proposed in previous work, and the Objective Class Points (OCP) method in terms of early prediction of SLOC and TLOC for object-oriented software. We used both simple and multiple linear regression methods to build the prediction models. An empirical comparison, using data collected from four open source Java projects, is reported in the paper. Overall, results provide evidence that the multiple linear regression model, based on the combination of the use case metrics, is more accurate in terms of early prediction of SLOC and TLOC than: (1) the simple linear regression models based on e
ach use case metric, and (2) the simple linear regression model based on the OCP method.
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