Forecasting Java Software Evolution Trends Employing Network Models

The evolution of networks representing systems in various domains, including social networks, has been extensively studied enabling the development of growth models which govern their behavior over time. The architecture of software systems can also be naturally represented in the form of networks,...

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Bibliographic Details
Published in:IEEE transactions on software engineering Vol. 41; no. 6; pp. 582 - 602
Main Authors: Chaikalis, Theodore, Chatzigeorgiou, Alexander
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2015
IEEE Computer Society
Subjects:
Analytical models
Forecasting
Graphs and networks
Growth models
Java
Object oriented modeling
Object oriented programming
Objectoriented design methods
Open source software
Predictive models
Principles
reengineering
Restructuring
reverse engineering
Social networks
Software Architectures
Software engineering
Software systems
Studies
Trends
object-oriented design methods
reverse engineering
restructuring
and reengineering
software architectures
Graphs and networks
ISSN:0098-5589, 1939-3520
Online Access:Get full text
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Summary:The evolution of networks representing systems in various domains, including social networks, has been extensively studied enabling the development of growth models which govern their behavior over time. The architecture of software systems can also be naturally represented in the form of networks, whose properties change as software evolves. In this paper we attempt to model several aspects of graphs representing object-oriented software systems as they evolve over a number of versions. The goal is to develop a prediction model by considering global phenomena such as preferential attachment, past evolutionary trends such as the tendency of classes to create fewer relations as they age, as well as domain knowledge in terms of principles that have to be followed in object-oriented design. The derived models can provide insight into the future trends of software and potentially form the basis for eliciting improved or novel laws of software evolution. The forecasting power of the proposed model is evaluated against the actual evolution of 10 open-source projects and the achieved accuracy in the prediction of several network and software properties, which reflect the underlying system design, appears to be promising.
Bibliography:SourceType-Scholarly Journals-1
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ISSN:0098-5589
1939-3520
DOI:10.1109/TSE.2014.2381249