Polymetric views are the perspectives in reverse engineering where the software system manifests in the form of multiple metrics at once. Essentially, polymetric views combine several parameters and draw them in a single graph that will give the end-user an overview of the structural and functional arrangement of the software system.

In this article, we will discuss polymetric views in detail along with their challenges, limitations, and types.

What Are Polymetric Views?

Polymetric views use graphical representations of the whole software where various elements of the system such as classes, methods, or modules are drawn and their interrelated associations depicted. These views use more quantitative parameters, which include lines of code, cyclomatic complexity, coupling coefficients, and many other parametric attributes, to add annotations to the graphical elements. With polymetric views, a number of different metrics enable a multi-faceted view of the system, thus allowing for greater potential for analysis beyond the visual examination of codes.

Key Components of Polymetric Views

• Graphical Elements: Polymetric views are a set of diagrams implemented with nodes and edges. Nodes relate to a software entity while edges refer infers to some form of interaction/dependence between the nodes.
• Metrics: Metrics are quantitative characteristics that are employed as labels of the graphical components included in polymetric views. These may comprise LOC, CC, coupling coefficient, number of layers of inheritance, and others.
• Visual Properties: Static/structural metrics: size, color, position, and shape of nodes and edges are used to represent assigned metrics with visualization. For instance, the size of a node may be the number of the lines of code of a class; and the color could be cyclomatic complexity.
• Layout Algorithms: Graph lay outing functions define placements of nodes and edges on the screen with the aim of getting good readability and avoiding crowding. The arrangement of graphs may vary depending on the analysis objectives and the complexity of the software system in question.
• Interactivity: Polymetric views often are linked to interaction, such as, zoom, filtering of the elements with respect to the metrics and dynamic changes to the appearance attributes of graphics. This interactivity adds to the polymetric view’s first principle of exploration because it allows users to zoom in on interesting regions and examine them more closely.

Types of Polymetric Views

• Class Blueprints: These views are based on class internals and the nodes in this type of diagrams depict methods, and the edges, relations such as method call, inheritance, etc.
• System Complexity View: Create system-level view that shows the whole picture of the software system to depict the classes and modules to be developed and the relationships in between them. To represent factors like size and complexity, elements like gears that heavily into important aspects are depicted.
• Inheritance Tree View: Diagram the classes and their inheritance relationships so as to create a tree with classes as nodes and edges as the inheritance that links them. Some of these metrics, include; depth of inheritance can be represented using node size or coloration.
• Dependency View: What is prominent in this diagram: As dependencies between different pieces of software, in which nodes can represent classes, while edges may depict, for instance, method invocation or data access. This is because degrees of coupling and cohesion may be mapped and visualized using node and consequently edge property.
• Evolution View: Present the software system, it changes seen in terms of size, complexity, and structural as it developed over the specified time. This view can help us reveal emerging or evolving growth areas in the system or in the system architecture.

Challenges and Limitations

• Scalability: Visualizing large software systems is cumbersome by nature because there may be several nodes and many edges. This can cause problems of scalability, where system’s view will end up being filled up with too many items that make the view unreadable.
• Metric Selection: Selecting the right metrics fundamentally alters how useful polymetric views are. These observations suggest that much like sampling, the use of the correct metrics entails an understanding of the domain area in question as well as the purpose of the analysis at hand.
• Interpretation: Polymetric views are very useful, although understanding them allows gleaning new information through these types of visualization, a certain amount of experience and the ability to make correct decisions are needed. They also posed risks such as: complexity; for users new to the system, it might be difficult to attain information from the complex views.
• Tool Support: This means for polymetric views to be truly useful practical tools need to be developed to support the creation and management of these polymetric views. Establishing and sustaining such tools may prove to be costly Eventually, it is vital to note that developing and sustaining these and other such tools require significant resources.
• Static Nature: Some of the polymetric views does not have the temporal dimension, or in other words, they do not represent how the polymetric views change over time. Including runtime information can certainly improve the analysis, but this comes at the cost of having to deal with an additional source of data.

Case Study – Applying Polymetric Views

• Class Blueprint View: Create a class blueprint view to see the layout of classes and their components in Objected-Oriented Programming. Methods, with the size of nodes reflecting the number of lines of code or the area of a class in pixels, and the color of nodes disclosing cyclomatic complexity. It enables a quick view on the large, complicated methods that should be looked at more carefully.
• System Complexity View: You need to produce a system-high-level picture by creating a view of the system complexity. Nodes depict classes and object size and color is proportional to the number of methods contained in the class as well the number of levels in the hierarchy respectively. Dependencies between classes are represented through edges. This picture seems to show classes with too many references, which may indicate problems with the design.
• Dependency View: Take a dependency view that serves to determine the connectivity between classes and objects. A node signifies a class, while an edge shows a method being called. Some of them include ‘coupling between object (CBO)” and ‘response for a class (RFC). This view brings out some aspects in closely coupled classes that make maintainability hard to achieve.
• Evolution View: To see the evolution view, and therefore understand how the system has evolved over time, one has to follow the steps below: A node contains definitions of some software elements and an edge shows their state at some stages of evolution, evolution of size and other properties.
• Interactive Exploration: When self-navigating through the graphical representation, use functionality tools like zoom to focus on specific classes, isolate components based on values and properties, and modify the appearance of the graphical model. This interactivity enables a thorough and dynamic approach as it is with other SW approaches.

Benefits of Polymetric Views

• Enhanced Understanding: Polymetric views help generate cognitive abstractions about software systems; they are also a great help in understanding the topography, dynamics and interaction protocols of software systems.
• Anomaly Detection: The use of a polymetric view suggests that the various metrics are examined all at once, and hence, any defects such as a complicated class, or else great cohesion in between elements, can be easily observed.
• Improved Maintenance: Acquiring knowledge about the architecture and the dependencies in a software application is important specifically for maintenance and future evolutional processes. Multidimensional views assist in determining the key elements as well as the dependencies of the system and serve to assess the effects of planned changes and modifications.
• Support for Refactoring: Originally, polymetric views are used to show high-effort and bad- design points of the program to guide the refactoring process. This allows developers to prioritize refactoring tasks citing that the views unveiled by the visualizations are important.
• Documentation and Communication: It is important that polymetric views are used as documentation because it gives details of the appearance of the software system, which can be easily explained to all the stakeholders involved. They assist the members in the delivery of information within the team and also in the transmission of information.

Conclusion

In conclusion, polymetric views be powerful for virtual experience, especially when it concerns with reverse engineering and software system analysing. These views incorporate data from several forms of graphs so that they can offer a more broader picture of the software structure and behavior. Nevertheless, issues regarding scalability, metrics selection, and metrics interpretation are present when working with polymorphic views, but, it has to be noted that the overall increase in the ability to detect anomalies, in supporting maintenance, in aiding in the refactoring process, and in communication is noticeable and quite valuable. Nonetheless, the said tools and techniques are still changing, which means that polymetric views are still a basic aspect of reverse engineering and analysis of software products.

Frequently Asked Questions on Polymetric Views – FAQs

What are considered as polymetric views in reverse engineering?

Polymetric views are an extension of traditional visualization techniques, where specific values from a set of metrics are used to annotate graphical objects. They offer a good picture on how a multi-layered software system is structured and how these structures and sub-structures behave and interact with each other.

How does the polymetric view help the processes of software maintenance and refactoring.

From polymetric views, one can easily determine many criticalities, components and even anomalous factors regarding developed software systems as well as areas of high complexity levels. Such information is used to manage and drive maintenance as well as use in decision making concerning refactoring tasks.

Polymetric views can be applied to large software systems?

Despite the fact that visualizing large software systems may impose scalability concerns polymetric views are useful where appropriate tools in acceptable visualization are provided. More about the nature of the system and the chosen metrics may impede or facilitate its effectiveness of use as a visualizer.