Automated Planning for Software Architecture Evolution
Jeffrey M. Barnes,
Ashutosh Pandey and
David Garlan.
In The 28th IEEE/ACM International Conference on Automated Software Engineering, Silicon Valley, CA, 11-15 November 2013.
Online links: Plain Text
Abstract
In previous research, we have developed a theoretical
framework to help software architects make better decisions when
planning software evolution. Our approach is based on representation
and analysis of candidate evolution paths—sequences of transitional
architectures leading from the current system to a desired
target architecture. One problem with this kind of approach is
that it imposes a heavy burden on the software architect, who
must explicitly define and model these candidate paths. In this
paper, we show how automated planning techniques can be used
to support automatic generation of evolution paths, relieving this
burden on the architect. We illustrate our approach by applying
it to a data migration scenario, showing how this architecture
evolution problem can be translated into a planning problem and
solved using existing automated planning tools. |
Keywords: Architecture Evolution.
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