1. CS 510, Fall 2017 Edited & presented by Jim Alstad
Software Decision Analysis Techniques Chapters in Software Engineering Economics (SEE) Updated to MedFRS
Barry Boehm, USC-CSSE
CS 510, Fall 2017 Edited & presented by Jim Alstad

2. Topics in Software Decision Analysis Techniques
Economics – The study of how people make decisions in resource-limited situations
Macroeconomics (not covered in this course)
Inflation, taxation, balance of payments
Microeconomics
Make-or-buy, pricing, how much to build
Software economics decisions
Make-or-buy: Software Product
How many options to build?
Which DP architecture to use?
How much testing (prototyping, specifying) is enough?
How much software to re-use?
Which new features to add first?
Fall 2017
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3. Outline of The Four Lectures (Chapters in SEE)
Context; MedFRS TPS Example; Cost-Effectiveness Analysis
Models, optimization, production functions, economies of scale decision criteria
Multiple-Goal Decision Analysis I
Net value, marginal analysis, present value, figures of merit.
Multiple-Goal Decision Analysis II
Goals as constraints, constrained optimization, system analysis, unquantifiable goals
Dealing with Uncertainties
Expected value, utility functions, statistical decision theory, value of information
Fall 2017
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4. Master Key to Software Engineering Economics Decision Analysis Techniques
All decision criteria (DCs) convertible to present $?
Yes
Use standard optimization, net value techniques (chapters 10, 13)
outcome Is
of decision highly
sensitive to assumptions?
(chapter 17) No
Use standard constrained- optimization techniques (chapter 16)
Yes
Yes
All Non-$ DCs expressible as constraints?
Find, use less sensitive solution No
Use cost-benefit (CB) decision making techniques (chapters 11, 12)
All Non- $ DCs expressed as single “benefit” criterion?
Yes
End No
Use figure of-merit techniques, CB techniques (chapter 15)
Use present value techniques to convert future $ to present $ (chapter 14)
Yes
When $ are a mix of present and future cash flows
All Non-$ DCs quantifiable?
Use statistical decision theory techniques (Chapters 19,20)
When some DCs involve uncertainties No
Use techniques for reconciling non-quantifiable DCs (chapter 18)
Fall 2017
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5. Chapters 10-12 Cost-Effectiveness Analysis
Introduction
Example: MedFRS transaction processing system (TPS)
Performance models
Cost-performance models
Production functions: economies of scale
Cost-effectiveness decision criteria
Summary
Fall 2017
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6. MedFRS TPS Context
Current Ensayo IT processing capacity less than needed in crises
Top performance less than 1000 transactions/second (tr/sec)
Need 2000 tr/sec soon
Need growth to 4000 tr/sec
COTS server capability can provide over 2000 tr/sec, but mostly for business applications
And can’t achieve 4000 tr/sec
MedFRS considering developing or outsourcing its own server software
Fall 2017
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7. MedFRS TPS Architecture variant on SEE architecture (Fig 10-1)… … … … … … …
FRS Devices … … …
MedFRS communications hub vehicles (CHVs)
Ensayo Regional Server …
Area Concentrators 1 N
Medical DB
DB Server
Patient DB
Fall 2017
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8. MedFRS TPS Concept of Operation
MedFRS devices send patient trauma information to CHVs
CHVs validate information and forward it to area concentrators at regional MedFRS server
Usually about 10 local concentrators per area
N area concentrators use MedFRS server to determine best near-term treatment
Send back to device operators via CHVs
Multiprocessor overhead due to resource contention, coordination
Fall 2017
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9. COTS vs. New Development Cost Tradeoff: MedFRS TPS
Build special version of MedFRS regional server systems functions
To reduce COTS server software overhead, improve transaction throughput
Server systems software size: 20,700 SLOC
Transaction prioritization, status monitoring
COTS license tradeoffs vs. number of area concentrators N
Need 10 N licenses for CHVs
$1K each for acquisition, $1K each for 5-year maintenance
Fall 2017
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10. COTS/New Development Cost Tradeoff Analysis
COTS $K New Development $K
Software
Cost to acquire
Integrate & test Included
Run-time licenses N Not applicable
5-year maintenance N 151
N 757
Server N N
Total N N
Fall 2017
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11. COTS/New Development Cost Tradeoff
1200
1000
Life
Cycle
Cost, $K
800
New
600
400
200 10 20 30 40 50
Number of Area Concentrators, N
Now, we need to address the benefit tradeoffs
Fall 2017
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12. MedFRS TPS Decision Problem 1: How Many Regional Concentrators in Server?
Performance Parameters (for COTS OS)
N, number of processors N = ?
S, processor speed (MOPS/sec) S = 1000
P, processor overhead (MOPS/sec) P = 200
M, multiprocessor overhead factor M = 80
[overhead=M(N-1) MOPS/sec]
T, transaction processing load (MOPS/TR) T = 1.0
Performance (TR/sec)
MOPS/sec available for processing
MOPS/TR
N[S-P-M(N-1)] T
E(N) =
E(N) =
Fall 2017
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13. TPS Performance, E(N) MOPS/sec available for processing
MOPS/sec required
E(N) =
N [ (N-1)]
1.0
= N [ )-80N2
= 880N – 80N2
= 80N(11-N)
dE(N) dN
160N* = 880
N* = 5.5
E(N*) = 2440 = N
E(N) 1
800 2
1440 3
1920 4
2240 5
2400 6 7 11
0 =
= N*
Fall 2017
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14. TPS throughout: E(N) versus # of processors, N1
800 2
1440 3
1920 4
2240 5
2400 6 7 8
1960
E(N) (tr/sec)
Number of processors N
Fall 2017
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15. TPS Decision Problem 2: Which Operating System?
Option A
(Accept Available OS) B
(Build New OS)
Cost ($k) N N
Multiprocessor overhead factor M
O/H = M(N-1) 80 40
Option B:
N( (N-1))
E(N) =
= 840N-40N2
1.0
= 40N(21-N)
For N = 10, E(N) = 40(10)(11) = 4400
Fall 2017
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16. Cost-Effectiveness Comparison: TPS Options A, B
Option B
E(N) = 40N(21 – N)
C(N) = N
5000
4000
3000
2000
1000
450
650
1207
500
1000
1500
Cost C, $K
Fall 2017
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17. Production Function Achievable output = F (input consumed)
-Assuming only technologically efficient pairs:
No higher level of output achievable, using given input X
Output
Input
PF is nonnegative
PF is nondecreasing
Fall 2017
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18. Segments of Typical Production Function
Output
Input
High payoff
Diminishing returns
Investment
Fall 2017
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19. Value of software product to organization
Natural speech input
Tertairy application functions
Animated displays
Secondary application functions
User amenities
Value of software product to organization
Main application functions
Basic application functions
Operating System
Data management system
Investment
High-payoff
Diminishing returns
Cost of software product
Fall 2017
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20. Software “Gold-Plating” (Adding More Than Is Necessary)
Agents with attitudes
Animated displays
“everything for everybody”
Modularity, info. hiding
Measurement, diagnostics
Frequently Gold-Plating
Instant response
Pinpoint accuracy
Unbalanced systems
Usually Not Gold-Plating
Humanized input
Humanized output
Sometimes Gold-Plating
Highly generalized control, data structures
Sophisticated command languages
General-purpose utilities
Automatic trend analysis
Fall 2017
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21. Modular Transaction Processing System
Module 1
E(2 x 3) =
2 x E(3) =
3840 vs. 2400
P11
P12
P13 1
Trans. in
Processed
Transaction out
P21
P22
P23 2
Fall 2017
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Module 2

22. Software Project Diseconomies of Scale
SLOC
Output
1 + x
PM = C (KSLOC)
Person – Months Input
The best way to combat diseconomies of scale is to Reduce the Scale
Fall 2017
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23. Cost-Effectiveness Decision Criteria
Maximum available budget
Minimum performance requirement
Maximum effectiveness/cost ratio
Maximum effectiveness – cost difference
Return on investment (ROI)
Composite alternatives
Fall 2017
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24. Production Functions for TPS Options A, B
1207
2400
4400 X Y Q Z P
E (tr/sec)
1007
650
450
Fall 2017
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25. Maximum Effectiveness/Cost Ratio
1200
2000
1600
800
400
2400
100
200
300
500
600
700 R L
Eff/Cost = 8 K
Eff/Cost = 3.69
C, $K
E (tr/sec)
Fall 2017
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26. Maximum Effectiveness-Cost Difference
1000
2000
3000
4000
5000
500
1500
2400
1750
650
1207
3193
4400
B-Build new OS
A-Accept available OS
Cost C, $K
Throughput
E(TR/sec)
Fall 2017
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27. Return on Investment ROI
E-C C
Cost C ($K) 1 2 3 4 5
500
1000
1500
2.67
2.60 A B
Fall 2017
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28. Production Function for TPS Composite Alternative
2400
4400 E
C, $K
E (r/sec)
450
1007
1207
650
Fall 2017
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29. Summary – Cost-Effectiveness Analysis
Microeconomic concepts help structure, resolve software decision problems
Cost-effectiveness
Production functions
Economies of scale
C-E decision criteria
No single decision criterion dominates others
Each is best for some situations
Need to perform sensitivity analysis:
Slightly altered situation
doesn’t yield bad decision
Fall 2017
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