Information Technology Reference
In-Depth Information
6.6.1 Identifying Entity, Boundary, and Control Objects
Transforming use cases and scenarios into an effective analysis model begins with
the identification and classification of the concepts enunciated in the requirements
specification. These models must be broken down and torn apart in order to
pinpoint the entity, boundary, and control objects that make them up. Great care
must be taken to ensure that no objects are missed, or, conversely, that an
excessive number of objects are not created. It is also vital to ensure that the
objects identified are correctly interpreted and classified.
This process begins with the identification and determination of participating
objects, which are candidate objects assigned to use cases. These participating
objects are used to determine which concepts are to be used and manipulated in
each use case, and should be standardized across all use cases. Because these
objects are defined based on the requirements elicited during the various elicitation
and analysis interviews, natural language analysis can be used for identification.
The system of natural language analysis, put forth by Abbott (
1983
), is is ''an
intuitive set of heuristics for identifying objects, attributes, and associations'' from
within the requirements specification (Bruegge and Dutoit
2004
). This system is
used to map various language constructs, such as nouns, verbs, and adjectives, to
component models. The following list illustrates related examples:
• Proper Noun: Instance of a class (an object). For example, Whiskers the cat.
• Common Noun: Class. For example, a cat.
• Doing Verb: Operation. For example, Whiskers eats.
• Being Verb: Inheritance. For example, a cat is a kind of animal.
• Having Verb: Aggregation. For example, a cat has the fur of a mammal.
• Modal Verb: Constraints. For example, a cat must be a mammal.
• Adjective: Attribute. For example, a cat is cute.
When a use case is analyzed, these concepts can be used for object identifi-
cation. Unfortunately, natural language analysis is an imprecise method. The
outcome hinges on the writing style and ability of the analyst, as well as his or her
consistency and level of language competence. We said earlier that inconsistency
and ambiguity are highly detrimental to a requirements specification. This is never
more true than when analyzing those requirements for object identification.
Developers must be sure to refine and clarify the requirements specification during
the identification process in order to establish a standard set of objects and ensure
the use of a standard set of terms. However, even after such clarification, natural
language analysis still faces another issue: ''there are many more nouns than
relevant classes'' (Bruegge and Dutoit
2004
). Beyond this, the use of nouns in
normal speech does not always correspond with the concepts laid out in Abbott's
heuristics. Many nouns are used not to identify objects or concepts, but to convey
attributes or likenesses. In addition, multiple nouns can be used to describe the
same concept, further complicating the identification process. Tearing apart the
various meanings and uses of such nouns can be a very lengthy and repetitive
