Shariat Yazdi, Hamed;
Analysis and Prediction of Design
Model Evolution Using Time Series;
p.1-15 in: Proc. CAiSE Workshops 2014,
Thessaloniki, Greece, June 16-20, 2014. LNBIP,
Abstract: Tools which support Model-Driven Engineering
have to be evaluated and tested.
In the domain of model differencing and model versioning,
sequences of software models (model histories), in which a
model is obtained from its immediate predecessor by some
modification, are of special interest.
Unfortunately, in this application domain adequate real
test models are scarcely available and must be
artificially created. To this end, model generators were
proposed in recent years.
Generally, such model generators should be configured in
a way that the generated sequences of models are as
realistic as possible, i.e. they should mimic the changes
that happen in real software models.
Hence, it is a necessary prerequisite to analyze and to
stochastically model the evolution (changes) of real
software systems at the abstraction level of models.
In this paper, we present a new approach to statistically
analyze the evolution of models.
Our approach uses time series as a statistical method to
capture the dynamics of the evolution.
We applied this approach to several typical projects and
we successfully modeled their evolutions.
The time series models could predict the future changes
of the next revisions of the systems with good accuracies.
The obtained time series models are used to create more
realistic model histories for model versioning and model