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Top1. Introduction
Software metrics are units of measurements that describe one or more attributes of the software. An attribute is a property or a characteristic that the software have. For example, size is a software attribute that gives an indication on how large is a code project (i.e., whether it is small, medium or large). Lines of Code (LOC) metric is a software metric that is used as an indicator or one of the metrics for this size. Metrics in this sense work as units of measure where you can have several different metrics or units of measure as indicators for size where while they can be different in numbers but generally they should have high positive correlation.
Another related term in the software metrics field is “measurement”. While some references do not show differences between the terms: “metrics” and “measurements”, other references distinguish the “metrics” from “measurements” where metrics represent more complex formulas relative to measurements that may include one or more of the software attributes.
Attributes can be further divided into external and internal attributes. While literature also may include different definitions of those terminologies, however, generally internal attributes are the actual characteristics that a software or website have and in which, a numerical value can be collected or calculated from the software or website for that attribute. On the other hand, external attributes are high level attributes that can be measured indirectly from one or more internal attributes. In other words, we can say that “external attributes are the goals or what we want to know or measure, while internal attributes is what we can directly measure). For example, website usability is a popular website external attribute. This attribute cannot be measured directly (i.e., we can’t say for example that for website A, usability = 5, etc. i.e., a numerical value). On the other hand, several website internal attributes (e.g., time to learn, number of help features, documents, etc.) can be measured from a website which collectively can be used to assess the website usability.
Website metrics can be collected manually or automatically through tools. Many tools are developed to collect metrics and attributes automatically. On the other hand, due to their subjective natures, some attributes and metrics require the help of surveyed users or testers to give their “personal” opinion on those attributes.
In the domain of websites, we conducted an extensive survey to find all used and described attributes and metrics for websites. The experiment showed that there is an extensive mix in research documents and published articles between websites attributes and metrics. In the next section, we will provide a list of several described attributes and metrics. Later on, we will classify them according to our proposed conceptual model.