Software Quality Metrics Overview

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Software Quality Metrics Overview
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Types of Software Metrics

• Product metrics e.g., size, complexity, design
  features, performance, quality level
• Process metrics e.g., effectiveness of defect
  removal, response time of the fix process
• Project metrics e.g., number of software
  developers, cost, schedule, productivity

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Software Quality Metrics

• The subset of metrics that focus on quality
• Software quality metrics can be divided into
• End-product quality metrics
• In-process quality metrics
• The essence of software quality engineering is to
  investigate the relationships among in-process
  metric, project characteristics , and end-product
  quality, and, based on the findings, engineer
  improvements in quality to both the process and
  the product.

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Three Groups of Software Quality Metrics

• Product quality
• In-process quality
• Maintenance quality

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Product Quality Metrics

• Intrinsic product quality
• Mean time to failure
• Defect density
• Customer related
• Customer problems
• Customer satisfaction

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Intrinsic Product Quality

• Intrinsic product quality is usually measured by
• the number of bugs (functional defects) in the
  software (defect density), or
• how long the software can run before crashing
  (MTTF mean time to failure)
• The two metrics are correlated but different

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Difference Between Errors, Defects, Faults, and
Failures (IEEE/ANSI)

• An error is a human mistake that results in
  incorrect software.
• The resulting fault is an accidental condition
  that causes a unit of the system to fail to
  function as required.
• A defect is an anomaly in a product.
• A failure occurs when a functional unit of a
  software-related system can no longer perform its
  required function or cannot perform it within
  specified limits

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Whats the Difference between a Fault and a
Defect?
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The Defect Density Metric

• This metric is the number of defects over the
  opportunities for error (OPE) during some
  specified time frame.
• We can use the number of unique causes of
  observed failures (failures are just defects
  materialized) to approximate the number of
  defects.
• The size of the software in either lines of code
  or function points is used to approximate OPE.

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Lines of Code

• Possible variations
• Count only executable lines
• Count executable lines plus data definitions
• Count executable lines, data definitions, and
  comments
• Count executable lines, data definitions,
  comments, and job control language
• Count lines as physical lines on an input screen
• Count lines as terminated by logical delimiters

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Lines of Code (Contd)

• Other difficulties
• LOC measures are language dependent
• Cant make comparisons when different languages
  are used or different operational definitions of
  LOC are used
• For productivity studies the problems in using
  LOC are greater since LOC is negatively
  correlated with design efficiency
• Code enhancements and revisions complicates the
  situation must calculate defect rate of new and
  changed lines of code only

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Defect Rate for New and Changed Lines of Code

• Depends on the availability on having LOC counts
  for both the entire produce as well as the new
  and changed code
• Depends on tracking defects to the release origin
  (the portion of code that contains the defects)
  and at what release that code was added, changed,
  or enhanced

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Function Points

• A function can be defined as a collection of
  executable statements that performs a certain
  task, together with declarations of the formal
  parameters and local variables manipulated by
  those statements.
• In practice functions are measured indirectly.
• Many of the problems associated with LOC counts
  are addressed.

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Measuring Function Points

• The number of function points is a weighted total
  of five major components that comprise an
  application.
• Number of external inputs x 4
• Number of external outputs x 5
• Number of logical internal files x10
• Number of external interface files x 7
• Number of external inquiries x 4

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Measuring Function Points (Contd)

• The function count (FC) is a weighted total of
  five major components that comprise an
  application.
• Number of external inputs x (3 to 6)
• Number of external outputs x (4 to 7)
• Number of logical internal files x (7 to 15)
• Number of external interface files x (5 to 10)
• Number of external inquiries x (3 to 6)
• the weighting factor depends on complexity

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Measuring Function Points (Contd)

• Each number is multiplied by the weighting factor
  and then they are summed.
• This weighted sum (FC) is further refined by
  multiplying it by the Value Adjustment Factor
  (VAF).
• Each of 14 general system characteristics are
  assessed on a scale of 0 to 5 as to their impact
  on (importance to) the application.

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The 14 System Characteristics

1. Data Communications
2. Distributed functions
3. Performance
4. Heavily used configuration
5. Transaction rate
6. Online data entry
7. End-user efficiency

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The 14 System Characteristics (Contd)

1. Online update
2. Complex processing
3. Reusability
4. Installation ease
5. Operational ease
6. Multiple sites
7. Facilitation of change

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The 14 System Characteristics (Contd)

• VAF is the sum of these 14 characteristics
  divided by 100 plus 0.65.
• Notice that if an average rating is given each of
  the 14 factors, their sum is 35 and therefore VAF
  1
• The final function point total is then the
  function count multiplied by VAF
• FP FC x VAF

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Customer Problems Metric

• Customer problems are all the difficulties
  customers encounter when using the product.
• They include
• Valid defects
• Usability problems
• Unclear documentation or information
• Duplicates of valid defects (problems already
  fixed but not known to customer)
• User errors
• The problem metric is usually expressed in terms
  of problems per user month (PUM)

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Customer Problems Metric (Contd)

• PUM Total problems that customers reported for
  a time period ltdivided bygt Total number of
  license-months of the software during the period
• where
• Number of license-months Number of the
  install licenses of the software x Number of
  months in the calculation period

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Approaches to Achieving a Low PUM

• Improve the development process and reduce the
  product defects.
• Reduce the non-defect-oriented problems by
  improving all aspects of the products (e.g.,
  usability, documentation), customer education,
  and support.
• Increase the sale (number of installed licenses)
  of the product.

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Defect Rate and Customer Problems Metrics
Defect Rate Problems per User-Month (PUM)
Numerator Valid and unique product defects All customer problems (defects and nondefects, first time and repeated)
Denominator Size of product (KLOC or function point) Customer usage of the product (user-months)
Measurement perspective Producersoftware development organization Customer
Scope Intrinsic product quality Intrinsic product quality plus other factors
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Customer Satisfaction Metrics
Customer Satisfaction Issues
Customer Problems
Defects
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Customer Satisfaction Metrics (Contd)

• Customer satisfaction is often measured by
  customer survey data via the five-point scale
• Very satisfied
• Satisfied
• Neutral
• Dissatisfied
• Very dissatisfied

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IBM Parameters of Customer Satisfaction

• CUPRIMDSO
• Capability (functionality)
• Usability
• Performance
• Reliability
• Installability
• Maintainability
• Documentation
• Service
• Overall

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HP Parameters of Customer Satisfaction

• FURPS
• Functionality
• Usability
• Reliability
• Performance
• Service

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Examples Metrics for Customer Satisfaction

1. Percent of completely satisfied customers
2. Percent of satisfied customers (satisfied and
   completely satisfied)
3. Percent of dissatisfied customers (dissatisfied
   and completely dissatisfied)
4. Percent of nonsatisfied customers (neutral,
   dissatisfied, and completely dissatisfied)

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In-Process Quality Metrics

• Defect density during machine testing
• Defect arrival pattern during machine testing
• Phase-based defect removal pattern
• Defect removal effectiveness

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Defect Density During Machine Testing

• Defect rate during formal machine testing
  (testing after code is integrated into the system
  library) is usually positively correlated with
  the defect rate in the field.
• The simple metric of defects per KLOC or function
  point is a good indicator of quality while the
  product is being tested.

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Defect Density During Machine Testing (Contd)

• Scenarios for judging release quality
• If the defect rate during testing is the same or
  lower than that of the previous release, then
  ask Does the testing for the current release
  deteriorate?
• If the answer is no, the quality perspective is
  positive.
• If the answer is yes, you need to do extra
  testing.

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Defect Density During Machine Testing (Contd)

• Scenarios for judging release quality (contd)
• If the defect rate during testing is
  substantially higher than that of the previous
  release, then ask Did we plan for and actually
  improve testing effectiveness?
• If the answer is no, the quality perspective is
  negative.
• If the answer is yes, then the quality
  perspective is the same or positive.

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Defect Arrival Pattern During Machine Testing

• The pattern of defect arrivals gives more
  information than defect density during testing.
• The objective is to look for defect arrivals that
  stabilize at a very low level, or times between
  failures that are far apart before ending the
  testing effort and releasing the software.

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Two Contrasting Defect Arrival Patterns During
Testing
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Three Metrics for Defect Arrival During Testing

• The defect arrivals during the testing phase by
  time interval (e.g., week). These are raw
  arrivals, not all of which are valid.
• The pattern of valid defect arrivals when
  problem determination is done on the reported
  problems. This is the true defect pattern.
• The pattern of defect backlog over time. This is
  needed because development organizations cannot
  investigate and fix all reported problems
  immediately. This metric is a workload statement
  as well as a quality statement.

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Phase-Based Defect Removal Pattern

• This is an extension of the test defect density
  metric.
• It requires tracking defects in all phases of the
  development cycle.
• The pattern of phase-based defect removal
  reflects the overall defect removal ability of
  the development process.

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Defect Removal by Phase for Two Products
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Defect Removal Effectiveness

• DRE (Defects removed during a development phase
  ltdivided bygt Defects latent in the product) x
  100
• The denominator can only be approximated.
• It is usually estimated as
• Defects removed during the phase
• Defects found later

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Defect Removal Effectiveness (Contd)

• When done for the front end of the process
  (before code integration), it is called early
  defect removal effectiveness.
• When done for a specific phase, it is called
  phase effectiveness.

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Phase Effectiveness of a Software Product
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Metrics for Software Maintenance

• The goal during maintenance is to fix the defects
  as soon as possible with excellent fix quality
• The following metrics are important
• Fix backlog and backlog management index
• Fix response time and fix responsiveness
• Percent delinquent fixes
• Fix quality

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Fix Backlog

• Fix backlog is a workload statement for software
  maintenance.
• It is related to both the rate of defect arrivals
  and the rate at which fixes for reported problems
  become available.
• It is a simple count of reported problems that
  remain at the end of each time period (week,
  month, etc.)

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Backlog Management Index (BMI)

• BMI (Number of problems closed during the month
  ltdivided bygt Number of problem arrivals during
  the month) x 100.
• If BMI is larger than 100, it means the backlog
  is reduced.
• If BMI is less than 100, then the backlog is
  increased.

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Opened Problems, Closed Problems, and Backlog
Management Index by Month
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Fix Response Time and Fix Responsiveness

• The fix response time metric is usually
  calculated as
• Mean time of all problems from open to closed
• Metric may be used for different defect severity
  levels.
• Fix response time relates to customer
  satisfaction.
• But meeting agreed-to fix time is more than just
  achieving a short fix time.
• A possible metric is the percentage of delivered
  fixes meeting committed dates to customers.

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Percent Delinquent Fixes

• The mean response time metric is a central
  tendency measure.
• A more sensitive metric is the percentage of
  delinquent fixes (for each fix, if the turnaround
  time greatly exceeds the required response time,
  it is classified as delinquent).
• Percent delinquent fixes (Number of fixes that
  exceeded the response time criteria by severity
  level ltdivided bygt Number of fixes delivered in a
  specified time) x 100

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Percent Delinquent Fixes (Contd)

• This is not a real-time metric because it is for
  closed problems only.
• For a real-time metric we must factor in problems
  that are still open.
• We can use the following metric
• Real-Time Delinquency Index 100 x Delinquent
  / (Backlog Arrivals)

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Real-Time Delinquency Index
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Fix Quality

• The number of defective fixes is another quality
  metric for maintenance.
• The metric of percent defective fixes is simply
  the percentage of all fixes in a time interval
  that are defective.
• Recording both the time the defective fix was
  discovered and the time the fix was made to be
  able to calculate the latent period of the
  defective fix.

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Examples of Metrics Programs

• Motorola
• Follows the Goal/Question/Metric paradigm of
  Basili and Weiss
• Goals
• Improve project planning
• Increase defect containment
• Increase software reliability
• Decrease software defect density
• Improve customer service
• Reduce the cost of nonconformance
• Increase software productivity

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Examples of Metrics Programs (Contd)

• Motorola (contd)
• Measurement Areas
• Delivered defects and delivered defects per size
• Total effectiveness throughout the process
• Adherence to schedule
• Accuracy of estimates
• Number of open customer problems
• Time that problems remain open
• Cost of nonconformance
• Software reliability

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Examples of Metrics Programs (Contd)

• Motorola (contd)
• For each goal the questions to be asked and the
  corresponding metrics were formulated
• Goal 1 Improve Project Planning
• Question 1.1 What was the accuracy of estimating
  the actual value of project schedule?
• Metric 1.1 Schedule Estimation Accuracy (SEA)
• SEA (Actual project duration)/(Estimated
  project duration)

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Examples of Metrics Programs (Contd)

• Hewlett-Packard
• The software metrics program includes both
  primitive and computed metrics.
• Primitive metrics are directly measurable
• Computed metrics are mathematical combinations of
  primitive metrics
• (Average fixed defects)/(working day)
• (Average engineering hours)/(fixed defect)
• (Average reported defects)/(working day)
• Bang A quantitative indicator of net usable
  function from the users point of view

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Examples of Metrics Programs (Contd)

• Hewlett-Packard (contd)
• Computed metrics are mathematical combinations of
  primitive metrics (contd)
• (Branches covered)/(total branches)
• Defects/KNCSS (thousand noncomment source
  statements)
• Defects/LOD (lines of documentation not included
  in program source code)
• Defects/(testing time)
• Design weight sum of module weights (function
  of token and decision counts) over the set of all
  modules in the design

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Examples of Metrics Programs (Contd)

• Hewlett-Packard (contd)
• Computed metrics are mathematical combinations of
  primitive metrics (contd)
• NCSS/(engineering month)
• Percent overtime (average overtime)/(40 hours
  per week)
• Phase (engineering months)/(total engineering
  months)

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Examples of Metrics Programs (Contd)

• IBM Rochester
• Selected quality metrics
• Overall customer satisfaction
• Postrelease defect rates
• Customer problem calls per month
• Fix response time
• Number of defect fixes
• Backlog management index
• Postrelease arrival patterns for defects and
  problems

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Examples of Metrics Programs (Contd)

• IBM Rochester (contd)
• Selected quality metrics (contd)
• Defect removal model for the software development
  process
• Phase effectiveness
• Inspection coverage and effort
• Compile failures and build/integration defects
• Weekly defect arrivals and backlog during testing
• Defect severity

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Examples of Metrics Programs (Contd)

• IBM Rochester (contd)
• Selected quality metrics (contd)
• Defect cause and problem component analysis
• Reliability (mean time to initial program loading
  during testing)
• Stress level of the system during testing
• Number of system crashes and hangs during stress
  testing and system testing
• Various customer feedback metrics
• S curves for project progress

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Collecting Software Engineering Data

• The challenge is to collect the necessary data
  without placing a significant burden on
  development teams.
• Limit metrics to those necessary to avoid
  collecting unnecessary data.
• Automate the data collection whenever possible.

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Data Collection Methodology (Basili and Weiss)

1. Establish the goal of the data collection
2. Develop a list of questions of interest
3. Establish data categories
4. Design and test data collection forms
5. Collect and validate data
6. Analyze data

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Reliability of Defect Data

• Testing defects are generally more reliable than
  inspection defects since inspection defects are
  more subjective
• An inspection defect is a problem found during
  the inspection process that, if not fixed, would
  cause one or more of the following to occur
• A defect condition in a later inspection phase

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Reliability of Defect Data

• An inspection defect is one which would cause
  (contd)
• A defect condition during testing
• A field defect
• Nonconformance to requirements and specifications
• Nonconformance to established standards

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An Inspection Summary Form