Software Engineering

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Software Engineering
Software Metrics James Gain (jgain_at_cs.uct.ac.za) h
ttp//people.cs.uct.ac.za/jgain/courses/SoftEng/
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Objectives

• Introduce the necessity for software metrics
• Differentiate between process, project and
  product metrics
• Compare and contrast Lines-Of-Code (LOC) and
  Function Point (FP) metrics
• Consider how quality is measured in Software
  Engineering
• Describe statistical process control for managing
  variation between projects

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Measurement Metrics
Against Collecting metrics is too hard ... its
too time consuming ... its too political ...
they can be used against individuals ... it wont
prove anything
For In order to characterize, evaluate, predict
and improve the process and product a metric
baseline is essential. Anything that you need to
quantify can be measured in some way that is
superior to not measuring it at all Tom Gilb
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Terminology

• Measure Quantitative indication of the extent,
  amount, dimension, or size of some attribute of a
  product or process. A single data point
• Metrics The degree to which a system, component,
  or process possesses a given attribute. Relates
  several measures (e.g. average number of errors
  found per person hour)
• Indicators A combination of metrics that
  provides insight into the software process,
  project or product
• Direct Metrics Immediately measurable attributes
  (e.g. line of code, execution speed, defects
  reported)
• Indirect Metrics Aspects that are not
  immediately quantifiable (e.g. functionality,
  quantity, reliability)
• Faults
• Errors Faults found by the practitioners during
  software development
• Defects Faults found by the customers after
  release

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A Good Manager Measures
process
process metrics
project metrics
measurement
product metrics
product
What do we
use as a
Not everything that can be counted counts, and
not everything that counts can be counted. -
Einstein
basis?
size?
function?
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Process Metrics

• Focus on quality achieved as a consequence of a
  repeatable or managed process. Strategic and Long
  Term.
• Statistical Software Process Improvement (SSPI).
  Error Categorization and Analysis
• All errors and defects are categorized by origin
• The cost to correct each error and defect is
  recorded
• The number of errors and defects in each category
  is computed
• Data is analyzed to find categories that result
  in the highest cost to the organization
• Plans are developed to modify the process
• Defect Removal Efficiency (DRE). Relationship
  between errors (E) and defects (D). The ideal is
  a DRE of 1

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Project Metrics

• Used by a project manager and software team to
  adapt project work flow and technical activities.
  Tactical and Short Term.
• Purpose
• Minimize the development schedule by making the
  necessary adjustments to avoid delays and
  mitigate problems
• Assess product quality on an ongoing basis
• Metrics
• Effort or time per SE task
• Errors uncovered per review hour
• Scheduled vs. actual milestone dates
• Number of changes and their characteristics
• Distribution of effort on SE tasks

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Product Metrics

• Focus on the quality of deliverables
• Product metrics are combined across several
  projects to produce process metrics
• Metrics for the product
• Measures of the Analysis Model
• Complexity of the Design Model
• Internal algorithmic complexity
• Architectural complexity
• Data flow complexity
• Code metrics

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Metrics Guidelines

• Use common sense and organizational sensitivity
  when interpreting metrics data
• Provide regular feedback to the individuals and
  teams who have worked to collect measures and
  metrics.
• Dont use metrics to appraise individuals
• Work with practitioners and teams to set clear
  goals and metrics that will be used to achieve
  them
• Never use metrics to threaten individuals or
  teams
• Metrics data that indicate a problem area should
  not be considered negative. These data are
  merely an indicator for process improvement
• Dont obsess on a single metric to the exclusion
  of other important metrics

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Normalization for Metrics

• How does an organization combine metrics that
  come from different individuals or projects?
• Depend on the size and complexity of the projec
• Normalization compensate for complexity aspects
  particular to a product
• Normalization approaches
• Size oriented (lines of code approach)
• Function oriented (function point approach)

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Typical Normalized Metrics

• Size-Oriented
• errors per KLOC (thousand lines of code), defects
  per KLOC, R per LOC, page of documentation per
  KLOC, errors / person-month, LOC per
  person-month, R / page of documentation
• Function-Oriented
• errors per FP, defects per FP, R per FP, pages of
  documentation per FP, FP per person-month

Project LOC FP Effort (P/M) R(000) Pp. doc Errors Defects People
alpha 12100 189 24 168 365 134 29 3
beta 27200 388 62 440 1224 321 86 5
gamma 20200 631 43 314 1050 256 64 6
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Why Opt for FP Measures?

• Independent of programming language. Some
  programming languages are more compact, e.g. C
  vs. Assembler
• Use readily countable characteristics of the
  information domain of the problem
• Does not penalize inventive implementations
  that require fewer LOC than others
• Makes it easier to accommodate reuse and
  object-oriented approaches
• Original FP approach good for typical Information
  Systems applications (interaction complexity)
• Variants (Extended FP and 3D FP) more suitable
  for real-time and scientific software (algorithm
  and state transition complexity)

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Computing Function Points
Analyze information domain of the application and
develop counts
Establish count for input domain and system
interfaces
Assign level of complexity (simple, average,
complex) or weight to each count
Weight each count by assessing complexity

Assess the influence of global factors that
affect the application
Grade significance of external factors, F_i, such
as reuse, concurrency, OS, ...
FP SUM(count x weight) x C where
complexity multiplier C (0.650.01 x N) degree
of influence N SUM(F_i)
Compute function points
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Analyzing the Information Domain
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Taking Complexity into Account

• Complexity Adjustment Values (F_i) are rated on a
  scale of 0 (not important) to 5 (very important)
• Does the system require reliable backup and
  recovery?
• Are data communications required?
• Are there distributed processing functions?
• Is performance critical?
• System to be run in an existing, heavily utilized
  environment?
• Does the system require on-line data entry?
• On-line entry requires input over multiple
  screens or operations?
• Are the master files updated on-line?
• Are the inputs, outputs, files, or inquiries
  complex?
• Is the internal processing complex?
• Is the code designed to be reusable?
• Are conversion and instillation included in the
  design?
• Multiple installations in different
  organizations?
• Is the application designed to facilitate change
  and ease-of-use?

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Exercise Function Points

• Compute the function point value for a project
  with the following information domain
  characteristics
• Number of user inputs 32
• Number of user outputs 60
• Number of user enquiries 24
• Number of files 8
• Number of external interfaces 2
• Assume that weights are average and external
  complexity adjustment values are not important.
• Answer

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Example SafeHome Functionality
Test Sensor
Password
SafeHome System
User
Sensors
Zone Setting
Zone Inquiry
Messages
Sensor Inquiry
User
Sensor Status
Panic Button
(De)activate
(De)activate
Monitor and Response System
Password, Sensors, etc.
Alarm Alert
System Config Data
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Example SafeHome FP Calc
weighting factor
count
simple avg. complex
measurement parameter
9
3
number of user inputs
3
4
6

X






8
number of user outputs
2
4
5
7

X






number of user inquiries
2
6
3
4
6

X






1
7
number of files
7
10
15

X






22
4
number of ext.interfaces
5
7
10

X
52
count-total
1.11
complexity multiplier
58
function points
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Exercise Function Points

• Compute the function point total for your
  project. Hint The complexity adjustment values
  should be low ( )
• Some appropriate complexity factors are (each
  scores 0-5)
• Is performance critical?
• Does the system require on-line data entry?
• On-line entry requires input over multiple
  screens or operations?
• Are the inputs, outputs, files, or inquiries
  complex?
• Is the internal processing complex?
• Is the code designed to be reusable?
• Is the application designed to facilitate change
  and ease-of-use?

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OO Metrics Distinguishing Characteristics

• The following characteristics require that
  special OO metrics be developed
• Encapsulation Concentrate on classes rather
  than functions
• Information hiding An information hiding metric
  will provide an indication of quality
• Inheritance A pivotal indication of complexity
• Abstraction Metrics need to measure a class at
  different levels of abstraction and from
  different viewpoints
• Conclusion the class is the fundamental unit of
  measurement

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OO Project Metrics

• Number of Scenario Scripts (Use Cases)
• Number of use-cases is directly proportional the
  number of classes needed to meet requirements
• A strong indicator of program size
• Number of Key Classes (Class Diagram)
• A key class focuses directly on the problem
  domain
• NOT likely to be implemented via reuse
• Typically 20-40 of all classes are key, the rest
  support infrastructure (e.g. GUI, communications,
  databases)
• Number of Subsystems (Package Diagram)
• Provides insight into resource allocation,
  scheduling for parallel development and overall
  integration effort

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OO Analysis and Design Metrics

• Related to Analysis and Design Principles
• Complexity
• Weighted Methods per Class (WMC) Assume that n
  methods with cyclomatic complexity
  are defined for a class C
• Depth of the Inheritance Tree (DIT) The maximum
  length from a leaf to the root of the tree. Large
  DIT leads to greater design complexity but
  promotes reuse
• Number of Children (NOC) Total number of
  children for each class. Large NOC may dilute
  abstraction and increase testing

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Further OOAD Metrics

• Coupling
• Coupling between Object Classes (COB) Total
  number of collaborations listed for each class in
  CRC cards. Keep COB low because high values
  complicate modification and testing
• Response For a Class (RFC) Set of methods
  potentially executed in response to a message
  received by a class. High RFC implies test and
  design complexity
• Cohesion
• Lack of Cohesion in Methods (LCOM) Number of
  methods in a class that access one or more of the
  same attributes. High LCOM means tightly coupled
  methods

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OO Testability Metrics

• Encapsulation
• Percent Public and Protected (PAP) Percentage of
  attributes that are public. Public attributes can
  be inherited and accessed externally. High PAP
  means more side effects
• Public Access to Data members (PAD) Number of
  classes that access another classes attributes.
  Violates encapsulation
• Inheritance
• Number of Root Classes (NRC) Count of distinct
  class hierarchies. Must all be tested separately
• Fan In (FIN) The number of superclasses
  associated with a class. FIN gt 1 indicates
  multiple inheritance. Must be avoided
• Number of Children (NOC) and Depth of Inheritance
  Tree (DIT) Superclasses need to be retested for
  each subclass

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Quality Metrics

• Measures conformance to explicit requirements,
  following specified standards, satisfying of
  implicit requirements
• Software quality can be difficult to measure and
  is often highly subjective
• Correctness
• The degree to which a program operates according
  to specification
• Metric Defects per FP
• Maintainability
• The degree to which a program is amenable to
  change
• Metric Mean Time to Change. Average time taken
  to analyze, design, implement and distribute a
  change

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Quality Metrics Further Measures

• Integrity
• The degree to which a program is impervious to
  outside attack
• Summed over all types of security attacks, i,
  where t threat (probability that an attack of
  type i will occur within a given time) and s
  security (probability that an attack of type i
  will be repelled)
• Usability
• The degree to which a program is easy to use.
• Metric (1) the skill required to learn the
  system, (2) the time required to become
  moderately proficient, (3) the net increase in
  productivity, (4) assessment of the users
  attitude to the system
• Covered in HCI course

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Quality Metrics McCalls Approach
Maintainability
Portability
Flexibility
Reusability
Testability
Interoperability
Correctness
Usability
Efficiency
Reliability
Integrity
McCalls Triangle of Quality
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Quality Metrics Deriving McCalls Quality Metrics

• Assess a set of quality factors on a scale of 0
  (low) to 10 (high)
• Each of McCalls Quality Metrics is a weighted
  sum of different quality factors
• Weighting is determined by product requirements
• Example
• Correctness Completeness Consistency
  Traceability
• Completeness is the degree to which full
  implementation of required function has been
  achieved
• Consistency is the use of uniform design and
  documentation techniques
• Traceability is the ability to trace program
  components back to analysis
• This technique depends on good objective
  evaluators because quality factor scores can be
  subjective

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Managing Variation

• How can we determine if metrics collected over a
  series of projects improve (or degrade) as a
  consequence of improvements in the process rather
  than noise?
• Statistical Process Control
• Analyzes the dispersion (variability) and
  location (moving average)
• Determine if metrics are
• (a) Stable (the process exhibits only natural
  or controlled changes) or
• (b) Unstable (process exhibits out of control
  changes and metrics cannot be used to predict
  changes)

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Control Chart
std. dev.

• Compare sequences of metrics values against mean
  and standard deviation. e.g. metric is unstable
  if eight consecutive values lie on one side of
  the mean.

Mean
- std. dev.