1. CIS 376 Bruce R. Maxim UM-Dearborn
Process Improvement
CIS 376
Bruce R. Maxim
UM-Dearborn

2. 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

3. 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

4. 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

5. 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

6. Process Improvement Activities

7. 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

8. 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

9. Process Analysis and Modeling
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

10. 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

11. 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

12. Process Model Example

13. Process Exceptions
Process models can’t represent how to handle exceptions
key people are lost prior to a critical review
failure of 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. Capability Assessment Process

20. 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

21. Process Tool Support

22. Defect Removal Effectiveness
Defect removal is central to software development
One of the top expense items
Affects project scheduling
Improves product quality

23. 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

24. Defect Density Example
For a program with 96 new or changed lines of code and 14 defects
Dd = 1000 * (14/96) = defects/KLOC

25. 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)

26. 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

27. 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?

28. 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

29. 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

30. Example - part 2 Phase 1 effectiveness E1 = MP/TD MP = E1 * TD
E2 = PTR/(TD - MP)
PTR = E2 * (TD - MP)

31. 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

32. PSP – Phase Yield Phase yield = 100 * (defects removed during phase)/
(defects in product at phase entry)
Note: cannot be computed until project is completed

33. 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 / ( ) = 42.9 %
Phase yield for code review =
100 * 5 /( ) = 41.7 %

34. 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

36. 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

38. Seven Basic Software Quality Tools
Histogram
frequency bar graph
vertical axis is # defects
horizontal axis has ordinal or interval type labels

40. 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

42. 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)

44. 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)

47. 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

48. Control Chart (C)

51. 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

52. Fishbone Diagram