1.Introduction Creating software systems is both a skill and an art form. Throug
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1.Introduction
Creating software systems is both a skill and an art form. Throughout the years, system designers have found ingenious ways to build software that is highly useful to its consumers and also intuitive. However, some systems have been delivered that perform the exact opposite of what their users want or need to do. Therefore, it is imperative that developers understand the importance of good and bad characteristics of software design so they can produce a system of high value to their stakeholders and end-users.
Describe New and Traditional Design Metrics and Explain How These Metrics Can Be Used to Evaluate the Quality of the Design.
Design metrics can be identified in different ways. In the early days of software design, metrics were evaluated in several ways. The Halstead Complexity Metric looked to measure design metrics by analyzing the source code of the system, while McCabe’s Cyclomatic Complexity method examines a system’s metrics by the complexity of its flow control. The Henry-KafuraInformation Flow method determines metrics on a system’s information flow. Lastly, the Higher-Level Complexity Measure sets metrics based on data passed within the system (Tsui, Karam, & Bernal, 2018).
Newer metrics define a software’s metrics by how connected the elements of a particular module are crafted, also known as cohesion (Ingeno, 2019). Another newer metric iscoupling, the measure of the reliance on one method in one class that is defined in another. The looser the coupling is for a system, the less likely many changes will need to occur on maintenance or an upgrade (Sommerville, 2016).
Discuss How the Metrics Relate to One Another.
Traditional design metrics are potentially the least related to each other as they specifically target parts of the system related to various aspects. One metric focuses on the source code, while another centers around data transmission. Newer metrics of cohesion and coupling are far more closely related to each other. If a system is designed well, it will have higher cohesion and looser coupling, resulting in a higher-quality design (Ingeno, 2019).
Evaluate the Concepts of Completeness and Consistency Related to Software Requirements.
Completeness in regards to software requirements leans towards being able to have all or most of the functional requirements working on deployment. It assumes that no requirements are left out, and they are carried out to completion. Consistency certifies that common vocabulary is being used throughout the building of systems. Consistency must be maintained in all aspects of the software build, including process logic, database elements, reports, and errors, to ensure the accuracy of the requirements (Tsui, Karam, & Bernal, 2018).
Conclusion
Although it is not always the main topic of focus when starting on a software build, knowing the value of good and bad characteristics of software design is a must. By understanding the traditional and new design metrics, how they are related to each other, and knowing the concepts of completeness and consistency, developers will be able to build a high-quality system that can scale and help future programmers understand how it works. These characteristics are essential for all developers to thrive in an already competitive industry.
2.During the design phase of the software development lifecycle, the architecture, and artifacts produced during the requirements elicitation are brought together to form the detailed design of a system. The type of decomposition used on theseelements of design, dictate the completeness and consistency of system components.The quality and complexity of these components are evaluated with design metrics.Although the criteria for quality and complexityhave evolved over time,the aim of all software has always been to be complete and consistent.The use of design metrics ensures that the quality of software remains high, while keeping complexity to a minimum.
Design Metrics: New & Traditional
In software engineering, consistency describes a level of continuity in the naming conventions used across all aspect of a system; this includes navigational flow and depth of logic. Consistency also extends to mean software that implements error handling. Completeness is divided into two domains, requirements fulfillment, and design completion. In order to fulfill requirements, all artifacts generated during the elicitation phase must be implemented in code. Design completion is satisfied by ensuring all modules, components, subsystems, and systems are engineered with the same level of detail (Tsui et al., 2018).
Developers characterize the quality and complexity of their designs with design metrics. Early on in their development, traditional design metrics focused primarily on the particulars of coding attributes and largely ignored system architecture. One of the earliest design metrics used was the Halstead complexity metric. This design metric identified four domains of a source codes composition: the operators, operands, the sum of the occurrences of both, tokens, and program length. This design metric arose during the time of procedural programming paradigms.Procedural programming languages weretypically written as one large block of code, and was only sub-divided by procedures and methods (McLaughlin et al., 2007).
As the Object-Oriented programming paradigm arose, and software architecture became more of a consideration during the detailed design phase, cohesion and coupling became strong considerations of evaluation. Cohesion refers to what extent the parts of a module should be contained within that same module. It is a measure of how related the parts within a module are to one another. Coupling refers to the interdependency of classes and modules with one another. As dependency increases, so to does the coupling associated between the components (Richards, 2020).
Conclusion
Good software is complete and consistent. To ensure that software exemplifies these qualities, naming conventions, error handling and requirements fulfillment via design completion must be analyzed during static analysis. Design metrics help to ensure that the quality of a software is high, while its complexity remains low. Older design metrics sported features that catered to programming paradigms that are rarely used in modern software development. Modern programming paradigms leverage simpler analysis techniques such as strong cohesion and weak coupling. Although older design metrics are less applicable, they can still be used in very specific cases.
