Process Improvement
CIS 376
Bruce R. Maxim
UM-Dearborn
Process Improvement Goals
• Understanding existing processes
• Introduce process changes to improve quality,
reduce costs, or accelerate schedules
• Industry is demanding increased attention to
quality in general
• Most process improvement work focuses on defect
reduction and prevention
• There are other process attributes that deserve our
attention
Process Improvement Attributes - part 1
• Understandability - degree to which a process is
well defined and understood
• Visibility - process activities have results that are
externally recognizable
• Supportability - process activities supported by
CASE tools
• Acceptability - defined processes are used and
accepted by software engineers
Process Improvement Attributes - part 2
• Reliability - process is defined so that errors are
avoided or trapped before product errors result
• Robustness - process can continue despite
unexpected problems
• Maintainability - process can evolve to reflect
changing organizational requirements or identified
process improvements
• Rapidity - the time required to complete a system
from specification to delivery
Process Improvement Stages
• Process analysis
– modeling and quantitative analysis of existing processes
• Improvement identification
– quality, cost, and scheduling bottlenecks located
• Process change introduction
– modify process to remove bottlenecks
• Process change training
– train staff involved in process revision proposals
• Change tuning
– process improvements are revised and allowed to evolve
Process Improvement Activities
Introduce
process change
Analyse
process
Identify
improvements
Tune
process changes
Train
engineers
Process
model
Process change
plan
Training
plan
Feedback on
improvements
Revised process
model
Process and Product Quality
• Closely related to one another
• Good processes are usually required to
produce good products
• In manufacturing applications, process is
principle determinant of quality
• For design-based activities, the capabilities
of the designers are also important
Product Quality Factors
• Development technology
– for large projects with average capability this is the main
determinant of product quality
• Quality of people involved
– for small projects the developer capability is the main determinant
of product quality
• Process quality
– significant for both small and large projects
• Cost, time, and schedule constraints
– unrealistic schedules can doom the quality of most products
Process Analysis and Modeling
• Process analysis
– study of existing processes to understand relationships among
process components
– allows comparisons with other processes
• Process modeling
– documentation of process in which the tasks, roles, and entities
used are recorded
– best to represent models graphically
– several different perspectives may be used (e.g. activities,
deliverables, etc.)
– model should be examined for weaknesses, this involves
discussion with stakeholders
Process Model Elements - part 1
• Activity - (round edged rectangle)
– has clearly defined objective, entry, and exit conditions
• Process - (round edged rectangle with shadow)
– set of coherent activities with agreed upon objective
• Deliverable - (rectangle with shadow)
– tangible output of an activity predicted by project plan
• Condition - (parallelogram)
– process or activity pre- or post-conditions
Process Model Elements - part 2
• Role - (circle with shadow)
– defined and bounded area of responsibility
• Exception - (double edged box))
– description of how to modify the process if anticipated
or unanticipated events occur
• Communication - (arrow)
– exchange of information between people and/or
machines
Process Model Example
Rôle
Post-cond ition
Pre-condition
Module compiles
without syntax
errors
Input
Module
specification
Test
engineer
Responsible
for
Test
module
Process
All defined tests
run on module
Outputs
Signed-of f test
record
Module test
data
Process Exceptions
• Process models can’t represent how to handle
exceptions
–
–
–
–
key people are lost prior to a critical review
failure of e-mail server for several days
organizational reorganization
request to respond to change requests
• General procedure is to suspend the process model
and follow RMMM plans augmented with the
managers own initiatives
Process Measurement
• Wherever possible quantitative process data
should be collected
• Organizations without process standards may have
to be define processes before measurements can
be made (since they won’t know what to measure)
• Process measurements should be used to assess
process improvements
• Organization objectives drive process
improvement, not measurements
Process Measurement Classes
• Time taken to complete process activities
– e.g. calendar time to complete a milestone
• Resources required to complete processes or
activities
– e.g. person months
• Number of event occurrences
– e.g. number of defects found
Goal Question Metric Paradigm
• Goals
– What is the organization trying to achieve?
– Process improvement deals with goal satisfaction.
• Questions
– Concerned with areas of uncertainty related to goals.
– You need process knowledge to derive questions.
• Metrics
– Measurements collected to answer questions
SEI Process Maturity Model
• Level 1 - Initial
– essentially uncontrolled
• Level 2 - Repeatable
– project management procedures defined and used
• Level 3 - Defined
– process management strategies defined and used
• Level 4 - Managed
– quality management strategies defined and used
• Level 5 - Optimizing
– process improvement strategies defined and used
SEI Process Model Problems
• Focuses on project management rather than project
development
• Ignores the use of strategies like rapid prototyping
• Model is intended to represent organizational
capability and not practices used on particular
projects
• There may be wide variation in the practices used
in a single organization
• Capability assessment is questionnaire-based
Capability Assessment Process
Select projects
for assessment
Distribute
questionnair es
Identify issues
for discussion
Interview
project managers
Brief managers
and engineers
Present
assessment
Analyse
responses
Interview
engineers
Write report
Clarify
responses
Interview
mana gers
Process Classification
• Informal
– No detailed process model, developers created their
own way of doing things
• Managed
– defined model drive development process
• Methodical
– processes supported by standard development method
• Supported
– processes supported by automated CASE tools
Process Tool Support
Informal
process
Generic
tools
Managed
process
Configuration
management
tools
Project
management
tools
Methodical
process
Analysis and
design
workbenches
Improving
process
Specialized
tools
Defect Removal Effectiveness
• Defect removal is central to software
development
• One of the top expense items
• Affects project scheduling
• Improves product quality
PSP - Defect Density
• This is the primary defect measure used in
PSP
• Dd = 1000 * D/N
• D = total number of defects found in all
phases of the process
• N = number of new and changed lines of
code in the program
Defect Density Example
• For a program with 96 new or changed lines
of code and 14 defects
• Dd = 1000 * (14/96) = 145.83 defects/KLOC
Defect Metrics - part 1
• Error Detection Efficiency
100%*(#errors found in 1 inspection)/(#errors in product before inspection)
• Defect Removal Efficiency
100%*(#defects found now)/(#defects found now + #defects found later)
• Error Detection Percentage
100%*(#inspection errors)/(#inspection errors + #valid discrepancy reports)
Defect Metrics - part 2
• Total Defect Containment Effectiveness (TDCE)
(#prerelease defects)/(#prerelease defects + #post-release defects)
• Phase Containment Effectiveness (PCE)
(#phase(i) defects)/(#phase(i) defects + #phase(i+x) defects)
• Effectiveness (E)
100%*N/(N + S)
N = #defects found by an activity
S = #defects found in subsequent activities
Phase-based Defect Removal Model
• Defects present at exit of each development phase
are estimated
• This allows us to set realistic targets and assess the
costs of reducing error injection rates
• This is a quality management tool and not a device
for estimation of software reliability
• How would this work in practice?
Assumptions
• Suppose we decide to create two broad
defect removal classes
– activities that handle defects before code is
integrated into the system library (design
reviews, inspections, unit testing)
– formal machine tests after code integration
• Also assume the same defect removal
effectiveness for each phase
Example - part 1
• MP = major problems found in before integration
• PTR = errors found during formal machine tests
• mu = MP/PTR
– the higher the value of mu the better
• Q = defects found after release to customer
• TD = (MP + PTR + Q)
– total defects for life of software
Example - part 2
• Phase 1 effectiveness
E1 = MP/TD
MP = E1 * TD
• Phase 2 effectiveness
E2 = PTR/(TD - MP)
PTR = E2 * (TD - MP)
Example - part 3
• Some equations that can be useful in quality
planning (assuming that E1 = E2)
Q = PTR /(mu - 1)
Q = MP / [mu * (mu - 1)]
Q = TD / (mu * mu)
• These equations work with either raw or
normalized defect values
PSP – Phase Yield
Phase yield =
100 * (defects removed during phase)/
(defects in product at phase entry)
Note: cannot be computed until project is
completed
Phase Yield - Example
•
•
•
•
5 defects found during code review
3 defects found during compile
2 defects found during unit testing
2 defects found during integration testing
• Phase yield for compile =
100 * 3 / (3 + 2 + 2) = 42.9 %
• Phase yield for code review =
100 * 5 /(5 + 3 + 2 + 2) = 41.7 %
Seven Basic Software Quality Tools
• Checklists (paper forms)
– used to gather data for later analysis
– used to confirm that process tasks are complete
– both simple yes/no and branching questions
Seven Basic Software Quality Tools
• Pareto Diagram
– bar chart sorted in descending height order
– vertical axis labeled with # defects
– horizontal axis (nominal) labeled with defect
cause types
– software defects tends cluster near related
causes
Seven Basic Software Quality Tools
• Histogram
– frequency bar graph
– vertical axis is # defects
– horizontal axis has ordinal or interval type
labels
Seven Basic Software Quality Tools
• Flowchart
– pictorial representation of a process
– breaks down process into its constituent steps
– can be useful in identifying were errors are
likely to be found in the system
Seven Basic Software Quality Tools
• Scatter diagram (point plots)
– used with correlation, regression, or statistical
modeling
– vertical axis is # defects
– horizontal axis some metric (e.g. McCabe’s
index)
Seven Basic Software Quality Tools
• Run chart
– line graph showing performance of dependent
variable (y) over time (x)
– best used for trend analysis (e.g. arrival of
defects during formal machine testing)
– can plot cumulative dependent variables (S
curves)
Seven Basic Software Quality Tools
• Control chart
– advanced form of run chart where capability is defined
– upper and lower control limits (dashed lines) are drawn
to alert the user when dependent measure is out of
control
– can plot cumulative dependent variables (S curves)
– C chart based on # conforming or not
– R chart based on subgroup ranges (max – min)
– X bar chart based on subgroup means
Control Chart (C)
Seven Basic Software Quality Tools
• Cause and effect (fish bone) diagram
– not widely used in software development, but can be
useful
– shows effect between quality variable and the factors
affecting it
Fishbone Diagram