ITIS 3310 Software Architecture and Design Chapter 15 Product Metrics for Software

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ITIS 3310 Software Architecture and Design
Chapter 15Product Metrics for Software
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Software Quality

• How do we define quality?

• Conformance to
• Explicitly stated functional and performance
  requirements
• Explicitly documented development standards
• Implicit characteristics that are expected of
  all professionally developed software

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Software Quality

• Mix of factors
• Varies for
• Different applications
• Different customers
• Three important points
• Requirements are the foundation of quality
• Standards need to be specified
• Implicit requirements are often as important as
  explicit ones

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Engine Analogy

• 3 base qualities
• Durability
• Economy of operation
• Power output
• Emphasize one at the expense of the others
• Optimize two at the loss of the third
• Example Power
• Power Economy Durability (longevity)
• Family car 200 hp, 25 mpg, 200,000 mi.
• NASCAR Cup 850 hp, 3-4 mpg, 600 miles
• NHRA Top Fuel 6000 hp, 5 gal per ¼ mile ¼
  mi

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McCalls Triangle of Quality

• Measurement of software quality can be
• Direct
• Defects uncovered during testing
• Performance
• Indirect
• Usability
• Maintainability
• In either case, measurement is important!
• Compare a metric to some goal

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McCalls Triangle of Quality

• McCall, Richards, Walters categorization of
  software quality factors
• Product operation
• Operational characteristics
• Product revision
• Ability to undergo change
• Product transition
• Adaptability to new environments

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McCalls Triangle of Quality
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McCalls Triangle of Quality

• Correctness
• Satisfies expectations
• Fulfills customers mission objectives
• Reliability
• Expectation of performing intended function with
  required precision
• Efficiency
• Consumption of resources to deliver functionality

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McCalls Triangle of Quality

• Integrity
• Access/control by unauthorized persons can be
  controlled
• Usability
• Effort required to learn, operate, prepare input
  for, and interpret output of
• Maintainability
• Effort required to locate and fix an error

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McCalls Triangle of Quality

• Flexibility
• Effort required to modify
• Testability
• Effort required to test for intended
  functionality
• Portability
• Effort required to transfer operations to a
  different hardware platform or operating system
  environment

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McCalls Triangle of Quality

• Reusability
• Extent to which parts of a system can be reused
  by other systems
• Interoperability
• Effort necessary to connect two systems together

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McCalls Triangle of Quality

• Can these be measured directly?
• Yes, if there is a single countable value
• Size of a program in bytes
• Transactions per second
• Some can only be measured indirectly
• Subjective assessment
• Effort required to learn how to operate a program
• Some may catch on quickly
• Some may require extensive instruction and
  practice
• Some many never get it

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A Comment
McCalls quality factors were proposed in
the early 1970s. They are as valid today as they
were in that time. Its likely that software
built to conform to these factors will exhibit
high quality well into the 21st century, even if
there are dramatic changes in technology.
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Resume 4/9
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ISO 9216 Quality Factors

• Functionality How the software satisfies stated
  needs
• Suitability
• Accuracy
• Interoperability
• Compliance
• Security
• Reliability Amount of time system is available
  for use
• Maturity
• Faulty tolerance
• Recoverability

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ISO 9216 Quality Factors

• Usability Ease of use
• Understandability
• Learnability
• Operability
• Efficiency Optimum use of system resources
• Time behavior
• Resource behavior

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ISO 9216 Quality Factors

• Maintainability Ease of repair
• Analyzability
• Changeability
• Stability
• Portability Ease of transposition to new
  environment
• Adaptability
• Installability
• Conformance
• Replaceability

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Measures, Metrics and Indicators

• Major problem in measurement subjectivity
• What is needed is a set of metrics
• Applicable to quantitative assessment of software
  quality
• Metrics necessarily represent indirect measure
• What is the unit of measure for quality?
• We can never directly measure quality
• Instead, we measure some manifestation of quality
• What counts is the precise relationship between
  the variable measured and the presumed quality of
  the software

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Measures, Metrics and Indicators

• A measure provides a quantitative indication of
  the extent, amount, dimension, capacity, or size
  of some attribute of a product or process
• The IEEE glossary defines a metric as a
  quantitative measure of the degree to which a
  system, component, or process possesses a given
  attribute.
• An indicator is a metric or combination of
  metrics that provide insight into the software
  process, a software project, or the product
  itself

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Measures, Metrics and Indicators

• We

Collect measures in order to
Develop metrics that
Reveal indicators of quality
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Measurement Principles

• The objectives of measurement should be
  established before data collection begins
• Each technical metric should be defined in an
  unambiguous manner
• Metrics should be based on a theory that is valid
  for the domain of application (e.g., metrics for
  design should draw upon basic design concepts and
  principles)
• Metrics should be tailored to best accommodate
  specific products and processes

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Measurement Process

• Formulation. The derivation of software measures
  and metrics appropriate for the representation of
  the software that is being considered
• Collection. The mechanism used to accumulate data
  required to derive the formulated metrics
• Analysis. The computation of metrics and the
  application of mathematical tools

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Measurement Process

• Interpretation. The evaluation of metrics results
  in an effort to gain insight into the quality of
  the representation
• Feedback. Recommendations derived from the
  interpretation of product metrics transmitted to
  the software team

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Goal-Oriented Software Measurement

• The Goal/Question/Metric Paradigm
• Technique for identifying meaningful metrics for
  any part of software process
• Steps
• Establish an explicit measurement goal that is
  specific to the process activity or product
  characteristic that is to be assessed
• Define a set of questions that must be answered
  in order to achieve the goal, and
• Identify well-formulated metrics that help to
  answer these questions.

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Goal-Oriented Software Measurement

• Goal definition template
• Analyze the name of activity or attribute to be
  measured
• for the purpose of the overall objective of the
  analysis
• with respect to the aspect of the activity or
  attribute that is considered
• from the viewpoint of the people who have an
  interest in the measurement
• in the context of the environment in which the
  measurement takes place.

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Goal-Oriented Software Measurement

• Analyze the SafeHome software architecture for
  the purpose of evaluating architectural
  components with respect to the ability to make
  SafeHome more extensible from the viewpoint of
  the software engineers performing the work in the
  context of product enhancement over the next
  three years.

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Goal-Oriented Software Measurement

• Now develop questions

Are architectural components characterized in a
manner that compartmentalizes function and
related data?
Is the complexity of each component within bounds
that will facilitate modification and extension?

• Now identify metrics that answer each question
  quantitatively

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Metrics Attributes

• Simple and computable
• Relatively easy to learn how to derive the metric
• Its computation should not demand inordinate
  effort or time
• Empirically and intuitively persuasive
• Should satisfy an intuitive notion about the
  product attribute under consideration
• Consistent and objective
• Should always yield results that are
• The same from measurement to measurement
• Unambiguous

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Metrics Attributes

• Consistent in its use of units and dimensions
• The mathematical computation of the metric should
  use measures that do not lead to bizarre
  combinations of unit
• Programming language independent
• Metrics should be based on the analysis model,
  the design model, or the structure of the program
  itself
• An effective mechanism for quality feedback
• The metric should provide a software engineer
  with information that can lead to a higher
  quality end product

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Collection and Analysis Principles

• Whenever possible, data collection and analysis
  should be automated
• Valid statistical techniques should be applied to
  establish relationship between internal product
  attributes and external quality characteristics
• Interpretative guidelines and recommendations
  should be established for each metric

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Analysis Metrics

• Function-based metrics
• Use the function point as a normalizing factor or
  as a measure of the size of the specification
• Specification metrics
• Used as an indication of quality by measuring
  number of requirements by type

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Function-Based Metrics

• The function point metric (FP)
• Can be used effectively as a means for measuring
  the functionality delivered by a system
• Function points are derived using
• An empirical relationship based on countable
  (direct) measures of software's information
  domain
• Assessments of software complexity

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Function-Based Metrics

• Information domain values are defined in the
  following manner
• Number of external inputs (EIs)
• Number of external outputs (EOs)
• Number of external inquiries (EQs)
• Number of internal logical files (ILFs)
• Number of external interface files (EIFs)

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Function Points
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Architectural Design Metrics

• Architectural design metrics
• Structural complexity g(fan-out)
• Data complexity f(input output variables,
  fan-out)
• System complexity h(structural data
  complexity)
• HK metric architectural complexity as a function
  of fan-in and fan-out
• Morphology metrics a function of the number of
  modules and the number of interfaces between
  modules

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Metrics for OO Design-I

• Size
• Size is defined in terms of four views
  population, volume, length, and functionality
• Complexity
• How classes of an OO design are interrelated to
  one another
• Coupling
• The physical connections between elements of the
  OO design
• Sufficiency
• The properties of a class that make it useful for
  a purpose
• Completeness
• A measure of the properties required to fully
  represent a problem domain object

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Metrics for OO Design-II

• Cohesion
• The degree to which all operations working
  together to achieve a single, well-defined
  purpose
• Primitiveness
• Applied to both operations and classes, the
  degree to which an operation is atomic
• Similarity
• The degree to which two or more classes are
  similar in terms of their structure, function,
  behavior, or purpose
• Volatility
• Measures the likelihood that a change will occur

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Distinguishing Characteristics
Berard argues that the following characteristics
require that special OO metrics be developed

• Localizationthe way in which information is
  concentrated in a program
• Encapsulationthe packaging of data and
  processing
• Information hidingthe way in which information
  about operational details is hidden by a secure
  interface
• Inheritancethe manner in which the
  responsibilities of one class are propagated to
  another
• Abstractionthe mechanism that allows a design to
  focus on essential details

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Class-Oriented Metrics
Proposed by Chidamber and Kemerer

• Weighted methods per class
• Depth of the inheritance tree
• Number of children
• Coupling between object classes
• Response for a class
• Lack of cohesion in methods

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Class-Oriented Metrics
Proposed by Lorenz and Kidd LOR94

• Class size
• Number of operations overridden by a subclass
• Number of operations added by a subclass
• Specialization index

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Class-Oriented Metrics
The MOOD Metrics Suite

• Method inheritance factor
• Coupling factor
• Polymorphism factor

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Operation-Oriented Metrics
Proposed by Lorenz and Kidd LOR94

• Average operation size
• Operation complexity
• Average number of parameters per operation

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Component-Level Design Metrics

• Cohesion metrics a function of data objects and
  the locus of their definition
• Coupling metrics a function of input and output
  parameters, global variables, and modules called
• Complexity metrics hundreds have been proposed
  (e.g., cyclomatic complexity)

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Interface Design Metrics

• Layout appropriateness
• A function of
• Layout entities
• Their geographic position
• The cost of making transitions among entities

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Code Metrics

• Halsteads Software Science a comprehensive
  collection of metrics all predicated on the
  number (count and occurrence) of operators and
  operands within a component or program
• It should be noted that Halsteads laws have
  generated substantial controversy, and many
  believe that the underlying theory has flaws.
  However, experimental verification for selected
  programming languages has been performed (e.g.
  FEL89).

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Metrics for Testing

• Testing effort can also be estimated using
  metrics derived from Halstead measures
• Binder BIN94 suggests a broad array of design
  metrics that have a direct influence on the
  testability of an OO system.
• Lack of cohesion in methods (LCOM).
• Percent public and protected (PAP).
• Public access to data members (PAD).
• Number of root classes (NOR).
• Fan-in (FIN).
• Number of children (NOC) and depth of the
  inheritance tree (DIT).

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Summary

• Metrics for software is still in its infancy
• Different fields also have different maturity
• OO techniques still immature
• Testing and Maintenance immature
• Many of the methods are hotly debated
• Field will mature
• Start now with some easily defined and measured
  aspects
• OVERALL use metrics as a tool, not a weapon!