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Deriving Data Dependence from/for UML State Machine Diagrams

Authors: HyeonJeong Kim; Doo-Hwan Bae; Vidroha Debroy; W. Eric Wong;

Deriving Data Dependence from/for UML State Machine Diagrams

Abstract

Slicing is a well-known reduction technique in many areas such as debugging, maintenance, and testing, and thus, there has been considerable research in the application of slicing techniques to models at the design level. UML state machine diagrams can properly describe the behavior of large software systems at the design level. The slicing of UML state machine diagrams is helpful for their maintenance. But it is difficult to apply a slicing algorithm to automatically reduce the diagrams with respect to slicing criteria, because of the unique properties of these diagrams, such as hierarchy and orthogonality. These properties make constructing a data dependence graph highly complicated. Hierarchy between states leads to implicit paths between states, which may affect data dependence. Also, orthogonality (i.e., parallelism) can cause an intransitivity problem when tracing data dependence. In this paper, we discuss an approach to address such problems. We first construct a control flow graph, which explicitly describes all possible transitions; and a hierarchy graph, which depicts the hierarchical structure of state machine diagram. Next we retrieve data dependence information and construct a dependence graph across different levels. We also show how data dependence information is retrieved, by virtue of ATM example.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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