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Integration in Systems Engineering Context
have traits of being relevant and appropriate. Allowing for the possibility
that not all measures capture key factors that are directly causal, we define
key measures as also contributing in a central way to the essential character
of the phenomena—that which is directly causal. Measures must be quantifiable with some precision. Good measures have relatively high accuracy,
that is, low variance and high precision. Measures are distinct qualitatively
and quantifiable as an attribute of a phenomenon or matter (IEEE 1991).
Measures need not be applicable to all parts or the whole of the system.
Optimizations do not occur through analyses of measures.
Lifecycle measures that focus on low-level determinants may be useful for
estimating other development projects for budgeting, scheduling, and planning. Comparisons with other projects: An example of such a lifecycle measure is the number of source lines of code of software. Over the course of the
product or service lifecycle, the number of source lines of codes grows.
A comparable measure of the number of source lines of code for a specific
function for a project is the absolute number, growth rate in the number, the
ratio of the growth rate for one stage of the lifecycle versus another, the
rework number, and the same measures from one project to like-kind projects.
Other lifecycle measures focus on the mean time between failure, while still
others are concerned with a measure of effectiveness (which is only determinable at the system level). For smaller projects, these lifecycle measures
are quite useful. However, for larger, complex projects lifecycle measures are
often inadequate due to the quite dissimilar nature of the prior projects.
Comparisons of new projects with non-like-kind projects are problematic.
There is another way, but it is also fraught with uncertainty.
Measurements, measures, frameworks, theory, variables (and their dependencies), metrics, and causality are all essential ingredients for comparing
projects for both estimating purposes (in the case of new development
efforts) and planning purposes (in the case of operational issues).
An introduction to concepts of measurement suggests that there is an
inherent error in all that we measure. Measurement is the interpretation of
observations, where the interpretation requires a context and a conceptualization of meaning. The interpretation is expressed through a framework—
the relationships, dimensions, interfaces, form, and fitness that act according
to the accepted standards. The framework is the logics of a scale by which to
compare various constructs. In total, the essence of a theory is made simple
and comprehensible through a framework. A theory, broadly defined and
widely recognized, is expressed in cognitive substance that explains phenomena and guides our actions and experiences. Reasoned and rational
measurement premises a serious-minded, deeply rooted theory. At the heart
of any scientific explanation is a mechanism, the cardinal enactment of a
function; or an activity, the central workings of a process. Measurement is
fundamental to comparing measures.
When investigating potentially causal factors, it is posited that the mechanisms and key activities that characterize various acts (i.e., events or
Integration in Systems Engineering Context
have traits of being relevant and appropriate. Allowing for the possibility
that not all measures capture key factors that are directly causal, we define
key measures as also contributing in a central way to the essential character
of the phenomena—that which is directly causal. Measures must be quantifiable with some precision. Good measures have relatively high accuracy,
that is, low variance and high precision. Measures are distinct qualitatively
and quantifiable as an attribute of a phenomenon or matter (IEEE 1991).
Measures need not be applicable to all parts or the whole of the system.
Optimizations do not occur through analyses of measures.
Lifecycle measures that focus on low-level determinants may be useful for
estimating other development projects for budgeting, scheduling, and planning. Comparisons with other projects: An example of such a lifecycle measure is the number of source lines of code of software. Over the course of the
product or service lifecycle, the number of source lines of codes grows.
A comparable measure of the number of source lines of code for a specific
function for a project is the absolute number, growth rate in the number, the
ratio of the growth rate for one stage of the lifecycle versus another, the
rework number, and the same measures from one project to like-kind projects.
Other lifecycle measures focus on the mean time between failure, while still
others are concerned with a measure of effectiveness (which is only determinable at the system level). For smaller projects, these lifecycle measures
are quite useful. However, for larger, complex projects lifecycle measures are
often inadequate due to the quite dissimilar nature of the prior projects.
Comparisons of new projects with non-like-kind projects are problematic.
There is another way, but it is also fraught with uncertainty.
Measurements, measures, frameworks, theory, variables (and their dependencies), metrics, and causality are all essential ingredients for comparing
projects for both estimating purposes (in the case of new development
efforts) and planning purposes (in the case of operational issues).
An introduction to concepts of measurement suggests that there is an
inherent error in all that we measure. Measurement is the interpretation of
observations, where the interpretation requires a context and a conceptualization of meaning. The interpretation is expressed through a framework—
the relationships, dimensions, interfaces, form, and fitness that act according
to the accepted standards. The framework is the logics of a scale by which to
compare various constructs. In total, the essence of a theory is made simple
and comprehensible through a framework. A theory, broadly defined and
widely recognized, is expressed in cognitive substance that explains phenomena and guides our actions and experiences. Reasoned and rational
measurement premises a serious-minded, deeply rooted theory. At the heart
of any scientific explanation is a mechanism, the cardinal enactment of a
function; or an activity, the central workings of a process. Measurement is
fundamental to comparing measures.
When investigating potentially causal factors, it is posited that the mechanisms and key activities that characterize various acts (i.e., events or