On the Application of Measurement Theory in Software Engineering
by
L. Briand,
K. El-Emam,
and
S. Morasca
Empirical Software Engineering: An international Journal, 1(1), 1996.
Abstract:
Elements of measurement theory have recently
been introduced into the software engineering discipline. It has been
suggested that these elements should serve as the basis for
developing, reasoning about, and applying measures. For example, it
has been suggested that software complexity measures should be
additive, that measures fall into a number of distinct types (i.e.,
levels of measurement: nominal, ordinal, interval, and ratio), that
certain statistical techniques are not appropriate for certain types
of measures (e.g., parametric statistics for less-than-interval
measures), and that certain transformations are not permissible for
certain types of measures (e.g., non-linear transformations for
interval measures). In this paper we argue that, inspite of the
importance of measurement theory, and in the context of software
engineering, many of these prescriptions and proscriptions are either
premature or, if strictly applied, would represent a substantial
hindrance to the progress of empirical research in software
engineering. This argument is based partially on studies that have
been conducted by behavioral scientists and by statisticians over the
last five decades. We also present a pragmatic approach to the
application of measurement theory in software engineering. While
following our approach may lead to violations of the strict
prescriptions and proscriptions of measurement theory, we demonstrate
that in practical terms these violations would have diminished
consequences, especially when compared to the advantages afforded to
the practicing researcher.
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