Wu, Hao (2013) Automated Metamodel Instance Generation Satisfying Quantitative Constraints. PhD thesis, National University of Ireland Maynooth.
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Abstract
Metamodels are the core of the metamodeling approach and widely
used in model driven architecture. The high abstraction level provided
by metamodels makes the metamodeling approach popular among
software modelers and language designers. However, the metamodeling
approach has one big drawback: it does not support instance
generation. Instances are particularly important for software modelers
and language designers to test or verify their metamodels. Unfortunately,
automatically generating metamodel instances is a very
challenging task. Furthermore, the generated instances should ideally
cover a more generic or specific feature of a metamodel as required by
modelers for different purposes.
This thesis presents a solution that combines both graph representation
and Satisfiability Modulo Theories (SMT) to the problem of
metamodel instance generation. The solution consists of two approaches,
the first approach presents a new foundation for generating
metamodel instances by translating a metamodel to an SMT problem
via a bounded graph representation. The second approach investigates
generating meaningful metamodel instances by using two
new techniques. The first technique generates instances that meet
partition-based coverage criteria by using criteria formulas to further
constrain the entire generation process. The second technique generates
instances that satisfy graph-property based criteria by introducing
different scenarios. These two approaches have been prototyped into a
new tool, named ASMIG, to demonstrate the feasibility of automatic
metamodel instance generation.
Item Type: | Thesis (PhD) |
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Keywords: | Automated Metamodel Instance; Quantitative Constraints; |
Academic Unit: | Faculty of Science and Engineering > Computer Science |
Item ID: | 5614 |
Depositing User: | IR eTheses |
Date Deposited: | 15 Dec 2014 11:43 |
URI: | https://mural.maynoothuniversity.ie/id/eprint/5614 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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