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version 3. Changeability in System Design. University of Cambridge Engineering Design Center (EDC). Olivier L. de Weck, Ph.D. [email protected] Associate Professor of Aeronautics & Astronautics and Engineering Systems. April 30, 2007. Outline. Some Experiences with Change Propagation

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changeability in system design

version 3

Changeability in System Design

University of Cambridge

Engineering Design Center (EDC)

Olivier L. de Weck, Ph.D.

[email protected]

Associate Professor of Aeronautics & Astronautics and Engineering Systems

April 30, 2007

outline
Outline
  • Some Experiences with Change Propagation
      • Swiss F/A-18 Aircraft Program (1991-1997)
  • Design for Changeability
      • Literature Review
  • Change Propagation Analysis – Recent Results
      • Analysis of a 9-year sensor system design project
      • Motifs and change patterns
  • Time Permitting:
    • Time-Expanded Decision Networks
      • Time-expanded network theory
      • Application to evolvable NASA launch vehicle design
  • Discussion
swiss f a 18 experience

(1993)

Swiss Version

interceptor

5000 flight hours

40 min average sortie

max 9.0g positive

“Redesign”

(Switch)

Swiss F/A-18 Experience

(1987)

U.S. Navy Version C/D

  • Approach
    • Apply new operational usage spectrum to existing configuration
    • Find those locations in the system which do not comply
    • Apply selective, prioritized local redesign one-element-at-a-time
    • Example: Center barrel, wing-carry-though bulkheads Al  Ti

fighter and attack

3000 flight hours

90 min average sortie

max 7.5g positive

f a 18 center barrel section
F/A-18 Center Barrel Section

Y488

Y470.5

Wing

Attachment

Y453

74A324001

f a 18 complex system change

Manufacturing Processes Changed

Original

Change

Fuselage

Stiffened

Flight Control

Software Changed

Center of Gravity Shifted

Gross Takeoff Weight Increased

F/A-18 Complex System Change

F/A-18 System Level Drawing

f a 18 lessons learned
F/A-18 Lessons Learned
  • Changes increased cost per aircraft by O(~$10M)
    • encountered “surprises” along the way
  • Changing a system (or product) after its initial design is
    • often required to accommodate new requirements
    • expensive, and time-consuming if change was not anticipated in the original design
  • Change propagation
    • some changes are local and remain local
    • other changes start local, but propagate through the system in complex, unanticipated ways
    • “Switching costs” include: engineering redesign cost, change in materials, manufacturing changes, change in operational costs, recertification, others …
examples
Examples
  • Communications Satellite Constellations [Chaize et al. 2003]
    • Iridium Bankruptcy [1999, $5B], Globalstar similar fate
    • Oversized System based on optimistic market forecasts, could not easily adapt capacity, service, footprint …
    • We’ve studied previously how to deploy them in stages
  • Automotive Platforms [Suh et al. 2005]
    • General Motors, e.g. Epsilon Platform [2003-2013+]
    • Challenge in adapting platform to changing requirements over 10-15 year life: stretching chassis, incorporate new powertrain technologies, rigid tooling, too many constraints
    • O($10B) commitments for design, factory layout, tooling,…
  • NASA Launch Vehicles [Silver et al. 2005]
    • NASA Space Shuttle Program [1972-present]
    • Original traffic model called for ~ 50 flights/year
    • Actual flight rate much smaller, technical issues, long turnaround times, high fixed cost, ~$4 billion/year

Iridium

780 km

gasoline

hybrid

STS

design for changeability flexibility
Design for Changeability/Flexibility
  • Traditional Systems Engineering Methods (QFD, DSM,…)
    • Hauser, LR.; Clausing, D. The House of Quality. Harvard Business Review,. Vol. 66, No. 3, May-June 1988,. pp, 63-73
    • say very little about system change over time
  • Changeability
    • E. Fricke and A.P. Schulz, Design for changeability (DfC): Principles to enable changes in systems throughout their entire lifecycle, Systems Engineering, 8(4) (2005)
    • Fundamental & Supporting Principles for Changeability
  • Engineering Changes
    • C. Eckert, P. Clarkson, and W. Zanker, Change and customisation in complex engineering domains, Research in Engineering Design, 15(1) (2004), 1–21
    • External vs. Internal Changes, Classification in Multipliers, Absorbers, etc…
  • Flexibility
    • J. H. Saleh, D. Newman, D. Hastings. “Flexibility in System Design and Implications for Aerospace Systems” Acta Astronautica, Vol. 53, No. 12, (2003), pp. 927 – 944
    • Importance of Design Lifetime
  • Product/System Platforms
    • Seepersad, C.C., Mistree, F., and Allen, J.K.. (2002) "A Quantitative Approach for Designing Multiple Product Platforms for an Evolving Portfolio of Products." in Proceedings of the ASME Design Engineering Technical Conferences, Advances in Design Automation, Montreal, Canada, (2002)

90% of research and teaching on system and product design is about “clean sheet” design, but 90% of industrial practice is design-by-redesign

research questions
Research Questions
  • What are sources of changes in system design (during design and/or operations)?
      • exogenous uncertainties, internal sources of change
  • How can the amount of change and change propagation paths be
      • analyzed for existing or past projects?
      • predicted for future projects?
  • What causes lock-in and how can it be mitigated?
  • What are elements of switching costs?
  • What can be done to affect switching costs?
      • Where should flexibility be embedded?
      • How much is it worth?
      • Is there an optimal degree of flexibility in system design?
change propagation analysis
Change Propagation Analysis

work with Monica Giffin and Gergana Bounova

inspired by Clarkson, Eckert and Keller

system description
Complex Sensor System

Radar System for BMD, complex hardware, software, human operators

Derivative of earlier system

9 Year development

46 Areas

Hardware

Software

Program Documentation

System Map (graph)

Interconnections between areas

System Description
data set
Change Request Database

technical, managerial, procedural

track parent, child, siblings by areas with unique ID number

chronologically numbered IDs

Data Mining Procedure

Export from DBMS to text file

Written into MySQL database with Perl scripts

Equivalent to a MS Word document with 120,000 pages

Sorting, Filtering, Anonymizing

Write simplified change request format (see right side)

Data Set

Typical Change Request

initial analysis
There is an inverse relationship between expected change magnitude and its frequency of occurrence.

Very large changes are rather infrequent while small changes on the other hand are commonplace.

Nearly half the small (magnitude 0) changes were either withdrawn, superseded or disapproved (46.6%), whereas nearly all the large changes (magnitude 5) were approved and carried out to completion (96.7%).

Initial Analysis
change networks
Apply Graph Theory to extract networks of connected changes

parent-child changes

sibling changes

Most changes are only loosely connected

2-10 related changes

Some large networks emerged

Question: do these networks emerge from a single initial change?

Change Networks

Legend

Change proposed

parent

child

Change rejected

sibling

sibling

Change implemented

change propagation network
Change Propagation Network

Network plot of largest change network in the dataset, with 2579 associated change requests.

Created by Gergana Bounova

Data from Monica Giffin (SDM)

slide18

1: Y5 new CRs

ID 1-22000

Analysis of 87CR Network

8000

12156

13320

slide19

2: Y6 new CRs

ID 22000-26000

23942

23945

23992

24980

23729

23922

23024

23821

24781

23925

23831

25481

8000

22850

24659

25053

25476

24927

25515

12156

24926

22946

25463

13320

slide20

3: Y7 new CRs

ID 26000-29000

27585

28213

28187

28007

28166

28122

28153

27027

28695

28790

28567

28788

23942

28846

27627

28878

23945

28531

26333

28528

27656

28428

28009

26331

23992

28186

27169

24980

23729

28067

27023

23922

23024

28529

28821

23821

24781

23925

28601

28162

28696

23831

25481

8000

22850

24659

25053

25476

27952

24927

25515

12156

24926

22946

25463

27592

26117

13320

slide21

4: Y7 new CRs

ID 29,000-31,000

27585

30143

28213

28187

28007

30344

28166

28122

28153

27027

28695

28790

28567

28788

29538

30614

29547

23942

29399

28846

27627

30465

28878

23945

28531

26333

30148

28528

27656

28428

28009

26331

29711

23992

28186

27169

24980

23729

28067

27023

23922

30771

23024

28529

28821

30126

23821

30548

29353

29731

29826

29226

30466

30501

24781

29227

23925

30503

28601

28162

29744

28696

23831

25481

8000

22850

24659

25053

25476

27952

24927

25515

12156

24926

22946

25463

27592

26117

13320

slide22

5: Y7 new CRs

ID 31000-32645

27585

30143

28213

28187

28007

30344

28166

28122

28153

27027

28695

28790

28567

28788

29538

30614

29547

23942

29399

28846

27627

30465

28878

23945

28531

26333

30148

28528

27656

28428

28009

26331

29711

23992

28186

27169

24980

23729

28067

27023

23922

30771

23024

28529

28821

32289

30126

23821

30548

29353

29731

29826

29226

30466

30501

31471

24781

29227

23925

30503

28601

28162

29744

28696

23831

25481

8000

22850

31973

24659

25053

31972

25476

27952

24927

25515

32645

12156

31966

24926

22946

31235

25463

27592

26117

13320

31967

slide23

6: Y8/9 update

final status of all CRs

27585

30143

28213

28187

28007

30344

28166

28122

28153

27027

28695

28790

28567

28788

29538

30614

29547

23942

29399

28846

27627

30465

28878

23945

28531

26333

30148

28528

27656

28428

28009

26331

29711

23992

28186

27169

24980

23729

28067

27023

23922

30771

23024

28529

28821

32289

30126

23821

30548

29353

29731

29826

29226

30466

30501

31471

24781

29227

23925

30503

28601

28162

29744

28696

23831

25481

8000

22850

31973

24659

25053

31972

25476

27952

24927

25515

32645

12156

31966

24926

22946

31235

25463

27592

26117

13320

31967

observations
Observations
  • The 87CR network did not initiate with a single CR and then grow gradually by change propagation
  • Several initially unrelated changes grew together to form a larger network over time
  • A few changes are highly connected
    • Examples: 24781(7), 29226 (7), 28009 (7)
    • highly connected changes are not necessarily parent changes
  • Most changes only connect to one or two other changes
  • Clustering
    • Changes that are disapproved or whose status is open tend to cluster with respect to each other (more analysis required)
  • The network is loosely connected
    • Example: Remove 26333   29226 and the network separates

Quick Netdraw Demo

change propagation motifs
Change Propagation Motifs

Step j

Step i

1-motifs

Implemented

Change

C11

Single

Change

proposed

C00

Single

Change

proposed

C10

Rejected

Change

1 motifs

Legend

Change proposed

Change rejected

Change implemented

100

Total

87

parent

child

sibling

sibling

change propagation motifs cont
Change Propagation Motifs (cont.)

parent-child-2-motifs

Analysis of 87CR

Step k

Step j

P01

P11

Both Changes Rejected

Step i

P12

P10

Parent Rejected

Child Implemented

P21

P20

P00

Parent Implemented

Child Rejected

P22

P02

Parent-Child

Changes

Proposed

Parent and Child Implemented

9 unique 2P-motifs

8 possible paths

observations from 2 motifs
Observations from 2-motifs
  • Parent-Child Pairs
    • The most frequent pattern is that both a parent and child change are implemented as planned (58%)
    • If a change in a change-pair is disapproved it is more likely to be the child (24%)
  • Sibling Pairs
    • In sibling pairs it is most likely that both will be implemented (41%) or at least one of them is implemented, while the other one is rejected (28%)
    • If one change in a pair is disapproved there is a 25% chance that the sibling will be disapproved as well
3 motifs
3-motifs

nodal CR connectivity motifs

PPS

PSP

ok

ok

illegal

illegal

illegal

3 parent-child links

2 parent-child links, 1 sibling link

PSS

SSS

Illegal links are

those where CRs

have more than

one parent or

where there is a cycle

ok

ok

2 sibling links

3 sibling links

For each legal triplet we can define a set of 3-motifs

3 motifs cont

PSP220=PSP202

step l

3-motifs (cont.)

step k

PSP222

step j

PSP

family

PSP210=PSP201

PSP200

PSP221=PSP212

PSP022

step i

PSP020=PSP002

PSP000

PSP211

PSP021=PSP012

PSP010=PSP001

PSP120=PSP102

PSP121=PSP112

PSP011

PSP100

PSP111

PSP110=PSP101

3 motif psp statistics
Only found two 3-motifs from PSP family to ever occur in 87CR data set 3-motif PSP statistics

PSP family

“change family pattern”

PSP222

parent and two related

child changes were proposed;

all were implemented

7 instances

found

“change substitution pattern”

PSP221=PSP212

parent and two related

child changes were proposed;

only one child was implemented

4 instances

found

one interpretation: one child change substitutes for another

bi partite graph analysis
Bi-partite graph analysis:

Analyze relationship of change request network to underlying system area network

Questions:

Which areas are most affected by changes?

Which areas are unaffected? [constants]

Which areas act as change multipliers, carriers, absorbers?

Which areas act as reflectors of change?

Bi-Partite Graph Analysis
slide32

1-motif-area-impact analysis

30143

27585

28213

28187

28007

30344

28166

28122

27027

28153

28695

28567

28788

28790

29538

30614

29547

23942

29399

28846

27627

30465

28878

23945

28531

26333

30148

28528

27656

28428

26331

23992

29711

28009

28186

27169

24980

23729

28067

23922

27023

30771

23024

32289

28821

28529

30126

29826

23821

29226

29353

30548

29731

System Network Map

30466

30501

31471

23925

24781

30503

29227

28601

28162

29744

28696

23831

8000

22850

25481

31973

25053

24659

31972

27952

24927

25476

25515

32645

31966

12156

22946

24926

31235

27592

25463

31967

26117

13320

Change Propagation Network

1 motif area impact analysis results by area
1-motif-area-impact analysis: Results by Area
  • Change Activity by area
    • Areas 3, 19 and 1 are most active in terms of number of proposed CR
  • Change Acceptance Index (CAI)
    • fraction of proposed changes that are actually implemented
  • Change Reflection Index (CRI)
    • fraction of proposed changes that are rejected
87cr area classification
87CR-area classification

perfect reflectors

Area 5

Reflectors

Area 19

Area 3

Area 10

Acceptors

Areas 4, 6, 14, 20

Area 11, 23, 35

Area 1

no CRs issued

or all CR’s unresolved

perfect acceptors

change propagation index cpi
Change Propagation Index (CPI)

change propagation probability

  • Classify each area
    • Absorber, Carrier, Multiplier

total

completed

changes

in Area j

instigating area

DDSM Change

Propagation Frequency

receiving

area

A change in Area 1 caused

changes in Area 6 with a

frequency of 4.17%.

-1 <= CPI <= +1

system area classification
System Area Classification

CPI Spectrum

  • Areas found to be strong multipliers
    • 16: hardware performance evaluation
    • 25: hardware functional evaluation
    • 5: core data processing logic
    • 32: system evaluation tools
    • 19: common software services
    • 3: graphical user interface (GUI)
  • Areas found to be perfect reflectors
    • 27, 41: look like perfect absorbers
    • but actually zero changes implemented
    • despite numerous changes proposed
    • = perfect reflectors

Note: results differ when doing 2-motif analysis

cpm tool
CPM Tool
  • Developed at EDC, University of Cambridge, UK

Likelihood of changes propagating from area 1 to other areas

change request generation

Discovered new change

pattern: “inverted ripple”

system

integration

and test

bug

fixes

[Eckert, Clarkson 2004]

subsystem

design

major milestones

or management

changes

component

design

Change Request Generation

Change Requests Written per Month

1500

Source:

Monica Giffin (SDM)

1200

900

Number Written

600

300

0

1

5

9

77

81

85

89

93

73

13

17

21

25

29

33

37

41

45

49

53

57

61

65

69

Month

time expanded decision networks1

Step 1

Step 2

Time-Expanded Decision Networks
  • We developed concept of time expanded decision networks, to formalize effect of lock-in and identify opportunities for flexibility

1.) Design Set of feasible System Configurations

2.) Quantify Switching Costs and Create a Static Network

3.) Create a Time Expanded Network based on the Static Network

4.) Create Operational Scenarios, Evaluate Optimal Paths

5) Modify System Configurations to exploit easier Switching

exit with optimal initial

configuration and switching embedded

Silver M.R., de Weck O.R., “Time Expanded Decision Networks: A Framework for Designing Evolvable Complex Systems , Systems Engineering, (10) 2, 2007

time expanded decision networks tdn

Design and Ops Costs

Switching Costs

Time-expanded Decision Networks (TDN)
  • Expand static network in time
  • Model operations arcs and switching arcs
  • Chance Nodes and Decision Nodes
  • Cost Elements identified

Step 3

scenarios and path optimization

Step 5

  • Evaluate optimal paths through TDN:
  • acyclic network
  • topological ordering
  • reaching algorithm (find shortest path)

Method Scaling

# of nodes

# of paths

optimal path solvable in

# of arcs

Scenarios and Path Optimization

Step 4

Major advantage over traditional decision trees !

application of tdn to launch vehicles

Model Development

Application of TDN to Launch Vehicles
  • How to choose launch vehicle configurations for space exploration when future traffic model is uncertain
  • Demand at the campaign level  vehicle demand
  • Effort started under NASA funded Draper/MIT CER effort 2004-2005, continued development after that

4 initial configurations considered

tdn launch case study results

SDV-A (80 mt)

EELV+ (62 mt)

TDN Launch Case Study - Results
  • Initially: only two launch vehicles are ever used
    • 1:SDV-A(80mt) = large, 2:EELV+(62 mt)=small
  • No switching during lifecycle observed (= “lock-in”)
  • Idea: gradually reduce switching cost to:
    • see if/when switching will occur
    • what switches are selected most often?
    • how valuable is it?

Switching cost matrix

tdn launch case study results cont
As switching cost is reduced switching becomes more frequent

Scenario 8 most interesting

Max savings $0.6B/$2.8B

Guideline for embedding flexibility in 3:EELV+ and how much it is worth

TDN Launch Case Study – Results (cont.)

reduce switching cost

system landscape

“Lock-in” Quadrant

volatile

inflexible

volatile

flexible

facilitate

switching

embed

flexibility

avoid

switching

design

robustly

stable

inflexible

System Landscape

Degree of Outcome

Uncertainty

sNPV

communication

satellites

E[NPV]

commercial

aircraft

wireless

sensor

networks

automotive

platforms

stable

flexible

water supply

systems

(some)

consumer

products

highway

infrastructure

Relative

Switching

Costs

DC/LCC

emerging principles
Emerging Principles
  • Flexibility is a relative, not absolute system property.
  • There are two types of flexibility: operational (inner loop), configurational (outer loop).
  • Flexibility only makes sense in the context of specified uncertainty, which might lead to specific classes of changes in the future. General flexibility does not exist.
  • Fully reconfigurable systems are those systems that exhibit the highest degree of flexibility where the switching costs between a finite set of configurations (states) has been reduced to nearly zero.
  • Given an uncertain future operating environment, there exists an optimal degree of flexibility that will balance reduction in switching costs with upfront system design/build complexity and ops cost.
  • Real options are switching cost reducers.
future work
Future Work
  • Uncertainty Characterization (jump-diffusion = continuous change + discrete events superimposed)
  • Change propagation in complex systems
    • refine CPI
      • sibling changes: count them or not?
      • change propagation frequency versus motif-counts
    • how to best use change magnitude
    • compare predicted versus actual change propagation and effort
  • Empirical/Field work on system evolution over time
    • Example Whitney subway study
    • Real Options Equivalence of TDN
more information
More Information
  • Thinking strategically about engineering and design of systems is important when we face:
      • long lifecycles
      • exogenous uncertainty
      • large, (partially) irreversible investments
  • http://strategic.mit.edu
meta controls view

configurational

flexibility

operational

flexibility

Meta-Controls View

System Change

Modeling

Uncertainty

Modeling

Decision

Modeling

uncertainty modeling
Domains:

Operational Environment

terrain, weather,…

Market (Demand)

customers, competition

Economic

interest rates, prices

Technology

disruptive

Regulatory

e.g. CAFÉ,

Political/Policy

Funding

Techniques

Diffusion Models

GBM

Lattice Models

binomial, trinomial…

Scenario Planning

Delphi Method [RAND 1959]

Times Series Forecasting

5

x 10

Geometric Brownian Motion Model

1.6

GBM model, Dt = 1 month,

Do = 50,000, m = 8% p.a., s = 40% p.a.

– 3 scenarios are shown

1.4

1.2

Demand [Nusers]

1

0.8

0.6

0.4

0

5

10

15

Time [years]

Uncertainty Modeling

de Weck O.L., Eckert C., “A Classification of Uncertainty for Early Product and System Design”, ICED-2007-1999, 16th International Conference on Engineering Design, Paris, France, August, 28-31, 2007

decision modeling
Decision Modeling
  • Choices among initial design configurations
    • S. Pugh, Total Design, Addison-Wesley, New York, 1991
    • D. L. Thurston, Real and Misconceived Limitations to Decision Based Design With Utility Analysis, J. Mech. Des. 123, 176 (2001)
  • How to build-in and value system flexibility?
    • Real Options “in” Projects [de Neufville et al. 2004]
  • When to switch to a new system/configuration?
    • Non-homogenous Markov-Chains [W. Feller, An Introduction to Probability Theory and Its Applications, Volume I. John Wiley & Sons, 3rd, ed. 1968
    • Staged Development and Deployment [de Weck et al. 2004]
    • Spiral Approaches/Rapid Prototyping [Boehm 1986]
  • Optimization: Stochastic Programming, Dynamic Programming, Multiobjective, …
    • too numerous to cite
  • Many large-scale complex systems suffer from “lock-in”, the inability to change/switch to a better configuration (or technology) despite superior solutions being known. Choice between continuing to operate an inefficient current system and redesigning or replacing it with a new system
    • Nuclear reactor technology [Cowan 1990]
    • Economic aspects of lock-in [Arthur 1989, Arrow 2000]
    • Causes are still being debated [Liebowitz 1985]
    • Network externalities are important [Katz 1997, Witt 1997]
    • Recent AA/TPP S.M. Thesis on lock-in [Silver 2005]
change propagation analysis1
Change Propagation Analysis
  • Analyze Change Propagation Processes on an Actual Program
    • Complex technical project at Raytheon
    • Radar System for Ballistic Missile defense
    • Change and Configuration Management critical
    • What happens when one change unexpectedly causes another?
  • Database of 41,500 Requested Changes:
    • ~500 individuals named in requests
    • ~9 years project life
    • Detailed design  implementation  test  acceptance
    • Changes requested involved hardware, software, documentation
  • Approach
    • Build underlying system map
    • Mine database in an automated fashion
    • Create change propagation networks and analyze them using motifs
    • Find change propagation patterns, multipliers, opportunities for flexibility, better architecture…?
slide57

1: Y5 new CRs

ID 1-22000

Analysis of 87CR Network

8000

12156

13320

slide58

2: Y6 new CRs

ID 22000-26000

23942

23945

23992

24980

23729

23922

23024

23821

24781

23925

23831

25481

8000

22850

24659

25053

25476

24927

25515

12156

24926

22946

25463

13320

slide59

3: Y7 new CRs

ID 26000-29000

27585

28213

28187

28007

28166

28122

28153

27027

28695

28790

28567

28788

23942

28846

27627

28878

23945

28531

26333

28528

27656

28428

28009

26331

23992

28186

27169

24980

23729

28067

27023

23922

23024

28529

28821

23821

24781

23925

28601

28162

28696

23831

25481

8000

22850

24659

25053

25476

27952

24927

25515

12156

24926

22946

25463

27592

26117

13320

slide60

4: Y7 new CRs

ID 29,000-31,000

27585

30143

28213

28187

28007

30344

28166

28122

28153

27027

28695

28790

28567

28788

29538

30614

29547

23942

29399

28846

27627

30465

28878

23945

28531

26333

30148

28528

27656

28428

28009

26331

29711

23992

28186

27169

24980

23729

28067

27023

23922

30771

23024

28529

28821

30126

23821

30548

29353

29731

29826

29226

30466

30501

24781

29227

23925

30503

28601

28162

29744

28696

23831

25481

8000

22850

24659

25053

25476

27952

24927

25515

12156

24926

22946

25463

27592

26117

13320

slide61

5: Y7 new CRs

ID 31000-32645

27585

30143

28213

28187

28007

30344

28166

28122

28153

27027

28695

28790

28567

28788

29538

30614

29547

23942

29399

28846

27627

30465

28878

23945

28531

26333

30148

28528

27656

28428

28009

26331

29711

23992

28186

27169

24980

23729

28067

27023

23922

30771

23024

28529

28821

32289

30126

23821

30548

29353

29731

29826

29226

30466

30501

31471

24781

29227

23925

30503

28601

28162

29744

28696

23831

25481

8000

22850

31973

24659

25053

31972

25476

27952

24927

25515

32645

12156

31966

24926

22946

31235

25463

27592

26117

13320

31967

observations from 1 motifs
Observations from 1-motifs
  • A majority of proposed changes (61%) is implemented
  • About 1 out of every 4 changes is disapproved (25%)
  • The status of about 1 in 6 proposed changes remains open (15%), even at the end of the program
    • probably not implemented
  • Results for all change requests in database

Among nodes in connected components

(with nodal degree at least 1):

Proposed:          1458:    9%  

Implemented:    10326:  67%

Rejected:           3642:  24%

For entire dataset:

Proposed:         15611:  38%  

Implemented:    20006:  48%

Rejected:           5934:   14%

Total # nodes: 41551

# isolates:      26125 – 63%

# non-isolates: 15426 – 37%

Among isolates:

Proposed:        14153:  54%  

Implemented:     9680:  37%

Rejected:           2292    9%

change propagation motifs cont1
Change Propagation Motifs (cont.)

sibling-2-motifs

Analysis of 87CR

Step k

Step j

Step i

S11

Both Changes Rejected

S01=S10

S12=S21

One Change Rejected

One Implemented

S00

S02=S20

S22

Both Changes Implemented

Sibling

Changes

Proposed

6 unique 2S-motifs

4 possible paths

3 motifs1
3-motifs

nodal CR connectivity motifs

PPS

PSP

ok

ok

illegal

illegal

illegal

3 parent-child links

2 parent-child links, 1 sibling link

PSS

SSS

Illegal links are

those where CRs

have more than

one parent or

where there is a cycle

ok

ok

2 sibling links

3 sibling links

For each legal triplet we can define a set of 3-motifs

3 motifs cont1

PSP220=PSP202

step l

3-motifs (cont.)

step k

PSP222

step j

PSP

family

PSP210=PSP201

PSP200

PSP221=PSP212

PSP022

step i

PSP020=PSP002

PSP000

PSP211

PSP021=PSP012

PSP010=PSP001

PSP120=PSP102

PSP121=PSP112

PSP011

PSP100

PSP111

PSP110=PSP101

3 motif psp statistics1
Only found two 3-motifs from PSP family to ever occur in 87CR data set 3-motif PSP statistics

PSP family

“change family pattern”

PSP222

parent and two related

child changes were proposed;

all were implemented

7 instances

found

“change substitution pattern”

PSP221=PSP212

parent and two related

child changes were proposed;

only one child was implemented

4 instances

found

one interpretation: one child change substitutes for another

observations from 3 motif analysis
Observations from 3-motif analysis
  • 3-Motifs give the most insight into the relationship between change requests
  • 3-Motifs are not uniformly present
  • The most common 3-motifs are:
    • PSP222: Parent + 2 child changes implemented as proposed
    • PSP221: Parent child implemented, one child change rejected and substituted for another
      • potential explanation: as change magnitude/invasiveness is assessed a change is rejected, and a change with equivalent outcome, but applied to another area is substituted
  • None of the 3-change motifs found contained any unresolved (open) change requests
    • Open change requests tend to be orphans

[Note: 3-motif analysis for entire dataset still ongoing]

results for cpi
Results for CPI

Monica

Gergana

requirements

documents

need to resolve differences

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