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Objective
To understand the various methods for
quantitative measurement of quality.
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Scope
Quality management by measurement
Management tools for Quality Improvement
The Seven QC Tools
Problem identification tools
Problem analysis tools
Statistical Process Control
Software Quality Measurements Productivity measures
Complexity measures
Baselining
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Quality management by measurement
Measurement strongly advocated as a crucial factor on the
road to quality improvement.
Measurement of quality is key to improvement of quality.
Quality of the product as well as the process has to bemeasured and monitored.
The initial step in quality improvement is
To measure the product or process and find the baseline or
the current situation. Plans for improvement are then based on this.
To monitor by keeping track of the progress, measurement
has to be iterated.
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Quality management by measurement
Dr. Deming emphasized the use of statistical methods to find
out the sources of defects, in his 14 quality principles.
No exact way for determining that quality is being delivered.
Measurement provides a quantified record of quality. Tells the organization performance.
This information essential to manage quality.
Measurement must be an integral part of the organizations
work process.
There are seven basic QC tools for problem identification and
analysis
Seven management tools for quality improvement are also
there.
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Quality management by measurement
Statistical process control makes use of statistical methods to
monitor process performance.
Statistical methods are used.
Determining whether or not the process is under control
To help identify root causes that are causing defects.
These tools can be used individually as well as in
combination.
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Management Tools for Quality Improvement
Relations Diagram
Identifies relationships between specific items, factors orideas related to a central issue to be addressed.
Provides clarity to intertwined relationships in a complex
situation in order to find an appropriate solution.
Affinity Diagram
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Management Tools for Quality Improvement
A network of cause-effect relations.
Often used to trace through answers to why is it
happening? questions. Useful in designing steps to counter market complaints,
planning to eliminate latent defects in products etc.
Relation Diagram
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Management Tools for Quality Improvement
Tree Diagram Enables the development of specific action items to
address a subjective goal.
Often used to relate means to ends, which in turn are
means to more general ends. Helps answer how? questions. How do we do this?
Tree Diagram
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Management Tools for Quality Improvement
Matrix Diagram
Allows a logical look at the relationships between a group
of items.
Items might represent a cause-effect relationship forsolving problems or what-how relationship for attaining
goals and objectives.
Method identifies corresponding elements involved in a
problem situation or event. Elements are arranged in rows and columns on a chart that
shows the presence or absence of relationships among
collected pairs of elements.
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Management Tools for Quality Improvement
Popularly used for quality function deployment to transformthe desires of the customer into the language required to
implement a product.
Relates multiple alternatives to multiple consequences of
each. Often used to answer which? questions.
Which things to do to satisfy customersrequirements?
Matrix Diagram
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Management Tools for Quality Improvement
Matrix data analysis
Quantifies and arranges matrix diagram data and
mathematically analyzes it.
Makes information easy to visualize and comprehend too. The only numerical analysis method.
Matrix Data Analysis Diagram
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Management Tools for Quality Improvement
Canbe used to analyze
Production processes where factors are complexly
intertwined
Causes of non conformities that involve a large volume ofdata.
Process decision program chart
Enables potential problems to be identified during the
planning phase of a complex and critical project Helps to develop contingencies and preventive measures.
A diagram of the flow of alternative possibilities and
counter measures for each.
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Management Tools for Quality Improvement
Used to design responses to possible setbacks, answers to
whatif? questions.
Can be used to implement countermeasures to minimize non
conformities in the manufacturing process etc.
Simple graphical tool that can be used to mitigate risk in
virtually any undertaking.
Process Decision
Program Chart
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Management Tools for Quality Improvement
Activity network diagram
Called the arrow diagram
Utilized by PERT or CPM methods.
Very useful for scheduling events and identifying
bottlenecks.
Typically used to answer when? question.
Activity Network
Diagram
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The Seven QC Tools Used for measurement and control. Meant to be used by everyone.
Strategy for mass movement.
The seven QC tools used for problem identification and
analysis are:
Problem identification
Pareto Analysis
Cause and effect diagram
Problem analysis
Check sheet
Control charts
Histograms
h d l
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The Seven QC Tools Scatter diagram Stratification
Tools introduced in the 1950s by Dr. Deming and Dr. Juran.
Problem Identification Tools Pareto Analysis
In problem solving, the first task is to identify the problem
areas.
There will be numerous problems.
Impractical to attack all these problems at once.
Common question that arises is Which problem do I
tackle first?
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The Seven QC Tools
Rule of statistics called the 80-20 rule states that 20percent of any activity will account for 80 percent of the
frequency.
Pareto analysis one of the most powerful quality tools.
Four steps to be followed in preparing a Pareto analysisare as follows:
Step 1Identify problem area.
Step 2Identify and name causes of the problem.
Step 3 Document the occurrence of the cause of theproblem.
Step 4Rank the causes by frequency using the Paretochart.
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The Seven QC Tools
The Pareto chart is a bar chart.
After Pareto Analysis, then action can be taken to firstaddress the largest cause of undesirable activity.
Once that is solved the next biggest cause can be handled and
so on.
0
20
40
60
80
100
1 2 3 4 5 6 7 8
Series1Frequency of
Occurrence
Category of Errors
Pareto Analysis
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The Seven QC Tools Cause and effect diagram
A problem identification tool.
Helps to know the real causes and the interrelations amongcauses.
A cause and effect diagram shows an effect at the right andmain causes of the effects off the horizontal axis.
Main causes are in turn having effects that have sub causesand so on down many levels.
Enumerates the variety of causes rather than the frequencyof events.
To solve a problem involves construction of a cause andeffect diagram.
Also called a fishbone diagram or Ishikawa diagram
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The Seven QC Tools
Named after the champion of this tool, Kaoru Ishikawaa quality expert from Japan.
MAN MACHINE
Cause and Effect Diagram
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The Seven QC Tools Problem Analysis Tools
Check Sheet
Basic data collection mechanisms.
Forms to gather and record data in an organized manner.
Helps to determine the occurrence of an event or cause.
Often referred to as checklist or tally sheet of events.
AX XB XCX X X XDX X X
Check Sheet
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The Seven QC Tools Control Charts
A statistical technique.
Assesses, monitors and maintains the stability of a process.
Developed in 1924 by Control Walter Shewhart.
Objective to monitor a continuous repeatable process and
the process variation from specifications or limits.
A graph with limit lines to show acceptable range of quality
production.
Helpful for spotting abnormal situations in standard
processes.
Used to plot over time (left to right) the observed output or
status of a process.
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The Seven QC Tools
In the figure the circled point is outside the control limits
and indicates that action should be taken to control such
variations.
Does not actually control anything.
A decision support mechanism that provides a basis for
action.
Extensively used in Statistical Process Control.
Upper Limit
Lower Limit
TIME
Control Chart
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The Seven QC Tools Histograms
Also called frequency distribution chart
A bar graph of measured values (data sets) to display the
frequency of occurrences of the measured data.
Provides a way to measure and analyze data collected abouta process or problem.
0
5
10
15
20
25
1 2 3 4
Histogram
CPU sec. between
Frequency ofOccurrence
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The Seven QC Tools Scatter diagrams
Also called as plot.
Shows the relationship that might exist between two
variables / factors.
Can test for possible causeeffect relationships.
Often referred to as correlation diagrams.
Scatter
Diagram
Inspection Mistakes
Illuminat
ion Level
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The Seven QC Tools Used to display what happens to one variable when another
variable changes, in order to test a theory or make forecasts.
Stratification
A technique used to analyze / divide a universe of data into
homogenous groups.
Plots many data points, typically with a measure of quality
on one axis and a variable that influence quality on the other
axis.
The Japanese work force mastered these elementary toolsfor quality control and got a head start in producing quality
products.
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Statistical Process Control Key element of the quality process.
Fundamental reality that the outputs of all processes vary
over time.
According to Dr.Deming- the differences in the results of
processes are due to special causes or common causes ofvariation.
Special causes represent forces outside the process, acting
with an adverse effect.
These are variations caused by special circumstances.
Can be pinpointed to a specific location or time.
If special causes are numerous then it follows that the process
is unstable and out of control.
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Statistical Process Control Common causes lead to variations.
These are the net effect of the numerous sources of variation
inherent in the current system.
Determination of the type of cause is key to effective
intervention.
The type of cause designates the responsibility for action.
Correction of special causes requires action on the process.
Common causes are inherent in the system.
Must be resolved by those who create the system within
which the operations are performed.
Hence SPC- a scientific measurement and decision-making
approach
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Statistical Process Control
Decisions and plans are formulated on the basis of actual
collection and evaluation of the correct measurement data.
For repetitive processes subject to some kind of variability:
Statistical process control keeps track of how the process isperforming.
A sophisticated technique takes time, expertise and money to
introduce properly.
Cannot be used as a casual way of monitoring process. Most effective method for determining cause is the control chart.
Signals the existence of special causes, when they appear.
Indicates the extent of variation resulting from common causes.
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Statistical Process Control
In the model given, the control limits serve as decision lines.
These are limits derived from past experience.
Denotes the range within which fluctuations are expected tofall.
Special causes are denoted by instances (circled) that fall
outside of control limits (Fig.A) or special patterns in the
plotted points (Fig.B).
special pattern
lower
limit
upper
limit
Fig. A Fig. B
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Statistical Process Control SPC is a highly defined technique.
Has a fundamental effect on the way in which the organization
operates.
A process in statistical control enables prediction of future
performance.
Brought huge advances in reliability of both machines and
processes, helping to control their inherent variability.
In software development field, application of statistical process
control has found scope particularly in the following:
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Statistical Process Control
Studying previous programs in order to find the deficiencies
and improve the process that produced them.
Real time monitoring of the present program to enable
signaling potential problems and institute appropriate
countermeasures.
Assessment of schedule, cost and human resources of
previous projects in order to develop realistic schedules andestimates.
SPC methods facilitate process improvement by disclosing
the causes of variation.
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Software Quality Measurement Measurement of software quality has faced difficulty since itis not based on quantitative laws.
Software metrics is still a complex field continuouslyevolving.
Software quality is a complex mix of various factors that vary
across different applications and projects.
Need for quality measurement has given rise to a number ofmetrics that would measure different aspects of the software.
Measurement gives a tool to communicate to your customers
the size of their request and extrapolate productivity, qualityand estimating accuracy.
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Software Quality Measurement Quality characteristics desired in software products defined in the
ISO standard guidelines for Information Technology Software
Product Evaluation Quality Characteristics are:
Functionality
Suitability Accuracy
Interoperability
Compliance
Security
Reliability
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Software Quality Measurement
Maturity
Fault tolerance
Recoverability
Usability Understandability
Learnability
Operability
Efficiency Time behavior
Resource behavior
Maintainability
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Software Quality Measurement
Analyzability
Changeability
Stability
Testability Portability
Adaptability
Installability
Conformance
Replaceability
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Software Quality Measurement
Metrics based on different attributes has been devised to measure
quality.
Can be used to help estimate projects, measure project progress
and performance and quantify product attributes.
Also required to compare softwares or projects.
Examples of metrics include
Product metrics, e.g. productivity measures and complexity
measures. Software development resource metrics, no. of people working
on a project.
Software development process metrics, e.g. no. of lines of code
inspected.
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Software Quality Measurement
Some of the metrics used commonly in projects are:
Attributes Metrics
Size SLOC (sourcelines of code)
Effort person hoursCost currency
Complexity software (system software, embedded, GUI, datacategory communications etc)
Productivity Measures.
Two popular productivity measures are:
Size-oriented measures.
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Software Quality Measurement Function- oriented measures.
Productivity defined as the amount of work that can be done in
a unit of time.
Step 1
Each function is classified as one of the following function types.
External input type
External output type
Logical internal file type External interface file type
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Software Quality Measurement
Step 2
The system being measured is looked at in terms of 14 general
system characteristics.
The degree of each characteristics influence on the system isestimated.
The 14 characteristics are:
Data communications
Distributed functions
Performance
Heavily used configuration
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Software Quality Measurement Transaction rate On-line data entry
End user efficiency
On-line update
Complex processing
Reusability
Installation ease
Operation ease Multiple sites
Facilitate change
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Software Quality Measurement The degree of influence is assigned to each of the above
characteristics from the following rating.
0 = no influence 1 = insignificant 2 = moderate 3 = average
4 = significant 5 = strong influence throughout.
Step 3
After quantifying the function and system characteristics, an
unadjusted function count is produced by :
The number of functions in each of the 15 categories is
multiplied by a predefined Weighting Factor.
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Software Quality Measurement
The weighting figures from each category are added together
to produce an adjusted function count (A).
The degree of influence ratings (0-5) for the 14 general
characteristics are added together to produce a total. The total is multiplied by .01, then the result is added to .65
(B).
The actual function point metric is now calculated by
multiplying the adjusted function count A with B.
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Software Quality Measurement
Complexity Measures
Software quality is measured by looking at it from various
attributes points of view, like reliability, stability,
maintainability etc. Another good indicator of quality is correctness.
This is looked at by determining the number of defects per size
unit of code.
Referred to as the defect rate. Operational quality of the software is determined by measuring
the characteristics that define its performance.
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Software Quality Measurement
These are:
Processor utilization
Memory utilization
I/O performance Complexity.
An appropriate measure of software complexity should cover
measurements of cost, time and reliability.
McCabes complexity metric: Count of the number of decision statements in a program.
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Software Quality Measurement
Gives a fairly good correlation between complexity to bugs anddebugging difficulty.
Advises partitioning routines whose metric exceeds ten.
Good aid to productivity and complexity evaluation and should
be used as a rule of thumb measure. Some of the aspects, which are not taken into account for
measurements are:
Severity of a bug is not taken into account.
Some bugs would appear only under stress testing and theseare not identified in the present scenario.
The impact of maintenance on the number of bugs is ignored.
The number of bugs does not remain constant.
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Baselining Method used to determine the current performance, accuracy
or efficiency of products or processes.
Used for the purpose of measuring progress or variances
from past performances. Baseline is a product or performance specification.
Been formally reviewed and agreed upon, and documented.
Forms the basis for any further development.
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Summary Measurement is a crucial factor on the road to quality
improvement. Measurement provides a quantified record of
quality.
The seven management tools being widely used for quality
improvement are: Affinity diagram
Relations diagram
Tree diagram
Matrix diagram Matrix data analysis
Process decision program chart
Activity network diagram
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Summary
The seven QC tools used for problem identification andanalysis are:
Problem identification
Pareto AnalysisCause and effect diagram
Problem analysis
Check sheet
Control charts
Histograms
Scatter diagram
Stratification
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Summary Statistical Process Control is a scientific measurement and
decision-making approach.
The quality characteristics that is desired in software products
are: Functionality, Reliability, Usability, Efficiency,
Maintainability and Portability. Two popular productivity measures are. Size-oriented
measures and Function- oriented measures.
McCabes complexity metric is a count of the number of
decision statements in a program. Baselining is the method used to determine the current
performance, accuracy or efficiency of products or processes
for the purpose of measuring progress or variances from past
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