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Redundant metrics provide no new design information. They can be discarded without loss of information, and should be discarded to facilitate the use of measurement data for decision making. Design measurement tools often come with a large set of metrics, and you should expect many of metrics to be redundant. SDMetrics is no exception here.
The difficulty is that, depending on design practices used in a development environment, two metrics may be redundant in one software system, but not in another. There is no such thing as a canonical set of non-redundant metrics that captures all important design properties and is valid for all systems. Therefore, SDMetrics opts for a rich set of metrics, to lower the risk of missing important design aspects, at the prize of some redundancy among the metrics.
Techniques such as principal component analysis (PCA) can be used to identify and eliminate redundant metrics. PCA is a standard technique to identify the underlying, orthogonal dimensions (which correspond to properties that are directly or indirectly measured) that explain relations between the variables in a data set. Examples of the application of PCA are demonstrated in [BWDP00,BWL01].
Using a technique such as PCA within a few projects, you can identify a reduced set of largely orthogonal metrics for your development environment.
|Section 6.3.1 "Descriptive Statistics"||Contents||Section 6.3.3 "Rankings"|