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Chapter: Software Engineering - Software Design

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Architectural styles, Architectural Design, Architectural Mapping using Data Flow

This section defines the term “software architecture” as a framework made up of the system structures that comprise the software components, their properties, and the relationships among these components.

Architectural styles, Architectural Design, Architectural Mapping using Data Flow

 

This section defines the term “software architecture” as a framework made up of the system structures that comprise the software components, their properties, and the relationships among these components. The goal of the architectural model is to allow the software engineer to view and evaluate the system as a whole before moving to component design.

 

What is Architecture?

 

The architecture is not the operational software. Rather, it is a representation that enables a software engineer to:

 

(1)             Analyze the effectiveness of the design in meeting its stated requirements,

 

(2)             Consider architectural alternatives at a stage when making design changes is still relatively easy, and

 

 

(3) Reduce the risks associated with the construction of the software.

 

Why is Architecture Important?

 

·        Representations of software architecture are an enabler for communication between all parties (stakeholders) interested in the development of a computer-based system.

 

·        The architecture highlights early design decisions that will have a profound impact on all software engineering work that follows and, as important, on the ultimate success of the system as an operational entity.

 

·        Architecture “constitutes a relatively small, intellectually graspable model of how the system is structured and how its components work together” [BAS03].

 

Data Design

 

This section describes data design at both the architectural and component levels. At the architecture level, data design is the process of creating a model of the information represented at a high level of abstraction (using the customer's view of data).

 

 

 

Data Design at the Architectural Level

 

The challenge is extract useful information from the data environment, particularly when the information desired is cross-functional.

 

To solve this challenge, the business IT community has developed data mining techniques, also called knowledge discovery in databases (KDD), that navigate through existing databases in an attempt to extract appropriate business-level information.

 

However, the existence of multiple databases, their different structures, the degree of detail contained with the databases, and many other factors make data mining difficult within an existing database environment.

 

An alternative solution, called a data warehouse, adds on additional layer to the data architecture.

 

A data warehouse is a separate data environment that is not directly integrated with day-to-day applications that encompasses all data used by a business.

 

In a sense, a data warehouse is a large, independent database that has access to the data that are stored in databases that serve as the set of applications required by a business.

 

Data Design at the Component Level

 

At the component level, data design focuses on specific data structures required to realize the data objects to be manipulated by a component.

 

n  refine data objects and develop a set of data abstractions

 

n  implement data object attributes as one or more data structures

 

n  review data structures to ensure that appropriate relationships have been established

 

n  simplify data structures as required

 

Set of principles for data specification:

 

 

1.    The systematic analysis principles applied to function and behavior should also be applied to data.

 

2.    All data structures and the operations to be performed on each should be identified.

 

3.    A data dictionary should be established and used to define both data and program design.

 

4.    Low level data design decisions should be deferred until late in the design process.

 

5.    The representation of data structure should be known only to those modules that must make direct use of the data contained within the structure.

 

6.    A library of useful data structures and the operations that may be applied to them should be developed.

 

7.    A software design and programming language should support the specification and realization of abstract data types.

 

Architectural Styles and Patterns

 

Each style describes a system category that encompasses:

 

(1)             A set of components (e.g., a database, computational modules) that perform a function required by a system,

 

(2)             A set of connectors that enable “communication, coordination and cooperation” among components,

 

(3)             Constraints that define how components can be integrated to form the system, and

 

(4)             Semantic models that enable a designer to understand the overall properties of a system by analyzing the known properties of its constituent parts.

 

An architectural style is a transformation that is imposed on the design of an entire system.

 

An architectural pattern, like an architectural style, imposes a transformation on the design of an architecture.

 

A pattern differs from a style in a number of fundamental ways:

 

1.     The scope of a pattern is less broad, focusing on one aspect of the architecture rather than the architecture in its entirety.

 

2.     A pattern imposes a rule on the architecture, describing how the S/W will handle some aspect of its functionality at the infrastructure level.

 

3.     Architectural patterns tend to address specific behavioral issues within the context of the architectural.

 

A Brief Taxonomy of Architectural Styles

 

Styles can be categorized as follows:

 

Data-Centered Architecture

 

A data store resides at the center of this architecture and is accessed frequently by other components that update, add, delete, or otherwise modify data within the store.

This architecture promotes integrability. Existing components can be changed and new client components can be added to the architecture without concern about other clients.


Architecture

This architecture is applied when input data are to be transformed through a series of computational or manipulative components into output data.

 

A pipe and filter structure has a set of components, called filters, connected by pipes that transmit data from one component to the next.




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