Chapter 15: Engineering Foundations - Revision history

Daniel Robbins: /* Techniques for Conducting Root Cause Analysis */

2015-08-29T06:36:11Z

‎Techniques for Conducting Root Cause Analysis

Daniel Robbins: /* Techniques for Conducting Root Cause Analysis */

2015-08-29T06:33:17Z

‎Techniques for Conducting Root Cause Analysis

Daniel Robbins: /* Techniques for Conducting Root Cause Analysis */

2015-08-29T06:32:12Z

‎Techniques for Conducting Root Cause Analysis

Daniel Robbins: /* Techniques for Conducting Root Cause Analysis */

2015-08-29T06:08:58Z

‎Techniques for Conducting Root Cause Analysis

Daniel Robbins: /* Levels (Scales) of Measurement */

2015-08-29T06:03:58Z

‎Levels (Scales) of Measurement

Show changes

Daniel Robbins: /* Levels (Scales) of Measurement */

2015-08-29T05:25:03Z

‎Levels (Scales) of Measurement

Daniel Robbins: /* Measurement */

2015-08-29T05:24:45Z

‎Measurement

Daniel Robbins: /* Measurement */

2015-08-29T05:12:56Z

‎Measurement

Daniel Robbins: /* Measurement */

2015-08-28T21:16:10Z

‎Measurement

Daniel Robbins: /* Unit of Analysis (Sampling Units), Population, and Sample */

2015-08-28T21:07:24Z

‎Unit of Analysis (Sampling Units), Population, and Sample

@@ Line 1,321: / Line 1,321: @@
 |number=8
 |author=Canadian Engineering Accreditation Board, Engineers Canada
-|article=“Accreditation Criteria and Procedures”
+|article=Accreditation Criteria and Procedures
 |publication=
 |edition=

@@ Line 1,333: / Line 1,333: @@
 |number=9
 |author=ABET Engineering Accreditation Commission
-|article=“Criteria for Accrediting Engineering Programs, 2012-2013”
+|article=Criteria for Accrediting Engineering Programs, 2012-2013
 |publication=
 |edition=

@@ Line 1,209: / Line 1,209: @@
 Methods and Experimental Techniques in this KA).
 * 4. Various kinds of correlation analyses may be used to understand the relationship between various causes and their impact. These techniques may be used in cases when conducting controlled experiments is difficult but data may be gathered (see Statistical Analysis in this KA).
 {{EndSection|title=Futher Readings|body=
 A. Abran, ''Software Metrics and Software Metrology''. [14]
@@ Line 1,225: / Line 1,226: @@
 concepts as used in real world engineering
 applications.}}

← Older revision		Revision as of 06:08, 29 August 2015
Line 1,209:		Line 1,209:
	Methods and Experimental Techniques in this KA).		Methods and Experimental Techniques in this KA).
	* 4. Various kinds of correlation analyses may be used to understand the relationship between various causes and their impact. These techniques may be used in cases when conducting controlled experiments is difficult but data may be gathered (see Statistical Analysis in this KA).		* 4. Various kinds of correlation analyses may be used to understand the relationship between various causes and their impact. These techniques may be used in cases when conducting controlled experiments is difficult but data may be gathered (see Statistical Analysis in this KA).
		+	{{EndSection\|title=Futher Readings\|body=
		+	A. Abran, ''Software Metrics and Software Metrology''. [14]
		+
		+	This book provides very good information on the
		+	proper use of the terms measure, measurement
		+	method and measurement outcome. It provides
		+	strong support material for the entire section on
		+	Measurement.
		+
		+	W.G. Vincenti, ''What Engineers Know and How They Know It''. [15]
		+
		+	This book provides an interesting introduction
		+	to engineering foundations through a series
		+	of case studies that show many of the foundational
		+	concepts as used in real world engineering
		+	applications.}}

@@ Line 512: / Line 512: @@
 define measures from an operational perspective.
-==Levels (Scales) of Measurement==
+===Levels (Scales) of Measurement===
 {{RightSection| {{referenceLink|number=4|parts=c3s2}} {{referenceLink|number=6|parts=c7s5}}}}

@@ Line 487: / Line 487: @@
 definitions do not facilitate measurements. For
 example, we may define a circle as a line forming
-''a closed loop such that the distance between any
+''a closed loop such that the distance between any point on this line and a fixed interior point called the center is constant''.
-point on this line and a fixed interior point called
+We may further say that the
 the center is constant''. We may further say that the
 fixed distance from the center to any point on the
 closed loop gives the radius of the circle. It may be
@@ Line 498: / Line 497: @@
 This can also be called the ''measurement method''; sometimes a measurement procedure may
 be required to be even more precise.

← Older revision		Revision as of 05:12, 29 August 2015
Line 482:		Line 482:
	attributes such as software size or complexity		attributes such as software size or complexity
	require clear definitions.		require clear definitions.
		+
		+	''Operational definitions''. The definition of attributes,
		+	to start with, is often rather abstract. Such
		+	definitions do not facilitate measurements. For
		+	example, we may define a circle as a line forming
		+	''a closed loop such that the distance between any
		+	point on this line and a fixed interior point called
		+	the center is constant''. We may further say that the
		+	fixed distance from the center to any point on the
		+	closed loop gives the radius of the circle. It may be
		+	noted that though the concept has been defined, no
		+	means of measuring the radius has been proposed.
		+	The operational definition specifies the exact steps
		+	or method used to carry out a specific measurement.
		+	This can also be called the ''measurement method''; sometimes a measurement procedure may
		+	be required to be even more precise.

← Older revision		Revision as of 21:16, 28 August 2015
Line 431:		Line 431:

	==Measurement==		==Measurement==
		+	{{RightSection\| {{referenceLink\|number=4\|parts=c3s1, c3s2}} {{referenceLink\|number=5\|parts=cc4s4}} {{referenceLink\|number=6\|parts=c7s5, c11s8}} {{referenceLink\|number=7\|parts=p442–447}}}}
		+
		+	Knowing what to measure and which measurement
		+	method to use is critical in engineering
		+	endeavors. It is important that everyone involved
		+	in an engineering project understand the measurement
		+	methods and the measurement results
		+	that will be used.
		+
		+	Measurements can be physical, environmental,
		+	economic, operational, or some other sort of
		+	measurement that is meaningful for the particular
		+	project. This section explores the theory of measurement
		+	and how it is fundamental to engineering.
		+	Measurement starts as a conceptualization
		+	then moves from abstract concepts to definitions
		+	of the measurement method to the actual application
		+	of that method to obtain a measurement
		+	result. Each of these steps must be understood,
		+	communicated, and properly employed in order
		+	to generate usable data. In traditional engineering,
		+	direct measures are often used. In software
		+	engineering, a combination of both direct and
		+	derived measures is necessary [6*, p273].
		+
		+	The theory of measurement states that measurement
		+	is an attempt to describe an underlying real empirical system. Measurement methods
		+	define activities that allocate a value or a symbol
		+	to an attribute of an entity.
		+
		+	Attributes must then be defined in terms of
		+	the operations used to identify and measure
		+	them— that is, the measurement methods. In this
		+	approach, a measurement method is defined to be
		+	a precisely specified operation that yields a number
		+	(called the ''measurement result'') when measuring
		+	an attribute. It follows that, to be useful,
		+	the measurement method has to be well defined.
		+	Arbitrariness in the method will reflect itself in
		+	ambiguity in the measurement results.
		+
		+	In some cases—particularly in the physical
		+	world—the attributes that we wish to measure are
		+	easy to grasp; however, in an artificial world like
		+	software engineering, defining the attributes may
		+	not be that simple. For example, the attributes of
		+	height, weight, distance, etc. are easily and uniformly
		+	understood (though they may not be very
		+	easy to measure in all circumstances), whereas
		+	attributes such as software size or complexity
		+	require clear definitions.

← Older revision		Revision as of 21:07, 28 August 2015
Line 380:		Line 380:
	falls in the critical region, the null hypothesis		falls in the critical region, the null hypothesis
	is rejected.		is rejected.
		+
		+	===Concepts of Correlation and Regression===
		+	{{RightSection\|{{referenceLink\|number=2\|parts=c11s2, c11s8}}}}
		+
		+	A major objective of many statistical investigations
		+	is to establish relationships that make it possible
		+	to predict one or more variables in terms of
		+	others. Although it is desirable to predict a quantity
		+	exactly in terms of another quantity, it is seldom
		+	possible and, in many cases, we have to be
		+	satisfied with estimating the average or expected
		+	values.
		+
		+	The relationship between two variables is studied
		+	using the methods of correlation and regression.
		+	Both these concepts are explained briefly in
		+	the following paragraphs.
		+
		+	''Correlation''. The strength of linear relationship
		+	between two variables is measured using
		+	the correlation coefficient. While computing the
		+	correlation coefficient between two variables, we
		+	assume that these variables measure two different
		+	attributes of the same entity. The correlation
		+	coefficient takes a value between –1 to +1. The
		+	values –1 and +1 indicate a situation when the
		+	association between the variables is perfect—i.e., given the value of one variable, the other can be
		+	estimated with no error. A positive correlation
		+	coefficient indicates a positive relationship—that
		+	is, if one variable increases, so does the other. On
		+	the other hand, when the variables are negatively
		+	correlated, an increase of one leads to a decrease
		+	of the other.
		+
		+	It is important to remember that correlation
		+	does not imply causation. Thus, if two variables
		+	are correlated, we cannot conclude that one
		+	causes the other.
		+
		+	''Regression''. The correlation analysis only
		+	measures the degree of relationship between
		+	two variables. The analysis to find the relationship
		+	between two variables is called regression
		+	analysis. The strength of the relationship between
		+	two variables is measured using the coefficient of
		+	determination. This is a value between 0 and 1.
		+	The closer the coefficient is to 1, the stronger the
		+	relationship between the variables. A value of 1
		+	indicates a perfect relationship.
		+
		+	==Measurement==