1. Fundamentals of Software Engineering Economics (I)
Cost Effective Analysis
(Chapters 10-20)
LiGuo Huang
Computer Science and Engineering
Southern Methodist University

2. Software Decision Analysis Techniques
Economics – The study of how people make decisions in resource-limited situations
Macroeconomics
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?

3. Outline of Chapters
Context; TPS II 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

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)

5. Chapters 10-12 Cost-Effectiveness Analysis
Introduction
Example: transaction processing system (TPS II)
Performance models
Cost-performance models
Production functions: economies of scale
Cost-effectiveness decision criteria
Summary

6. TPS II Business Context
Current TPS inadequate for growing workload
Travel reservations: air, rail, car, hotel
Top performance: 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 can’t achieve 4000 tr/sec
Consider developing your own server software

7. TPS II System Architecture - variant on COCOMO II book architecture… … … … … … …
Clients … … …
Companies
Travel agents
About 10/region
Local Concentrators
Server …
Regional Concentrators 1 N
Financial DB
DB Server
Services DB

8. TPS II Concept of Operation
Clients prepare and send travel-itinerary requests to local concentrator
Package of air, rail, car, hotel reservation requests
Local concentrators validate requests and forward them to regional concentrators at server
Usually about 10 local concentrators per region
N Regional concentrators use DB server to develop best-match travel itinerary package
Send back to clients via local concentrators
Multiprocessor overhead due to resource contention, coordination

9. COTS vs. New Development Cost Tradeoff: TPS II
Build special version of server systems functions
To reduce COTS server software overhead, improve transaction throughput
Server systems software size: 20,700 SLOC
Server systems, library integration, status monitoring
COTS license tradeoffs vs. number of regional concentrators N
Need 10 N licenses for local concentrators
$1K each for acquisition, $1K each for 5-year maintenance

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

11. COTS/New Development Cost Tradeoff
1200
1000
Life
Cycle
Cost, $K
800
New
600
400
200 10 20 30 40 50
Number of Regional Concentrators, N
Now, we need to address the benefit tradeoffs

12. TPS Decision 1 How Many Regional Concentrators in Server?
Performance Parameters
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 time (MOPS/TR) T = 1.0
Performance (TR/sec)
MOPS/sec available for processing
MOPS/TR required per transaction
N[S-P-M(N-1)] T
E(N) =
E(N) =

13. TPS Performance, E(N) KOPS/sec available for processing
KOPS/sec required per transaction
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
= N* =0

14. TPS throughout: E(N) versus number of processors, N1
800 2
1440 3
1920 4
2240 5
2400 6 7 8
1960
E(N) (tr/sec)
Number of processors N

15. Performance Model
A sequence of formulas that determine the estimated system performance in terms of a set of variables (system parameters)
Uses of Performance Models in Software Engineering
Optimal performance information
Use derivatives to find optimum points
Sensitivity analysis information

16. Multiple Maxima or Minima
E(x) x0 x

17. Points of Inflection
E(x) x

18. Saddle Points

19. Inflection and Saddle Points
2nd and 3rd derivatives
Infection Points
2nd derivative = 0; 3rd derivative ≠ 0
Saddle Points
2nd derivative positive in some directions and negative in others
Maxima
2nd derivative positive in all directions
Minima
2nd derivative negative in all directions

20. Sensitivity Analysis I

21. Sensitivity Analysis II
Partial derivative
TPS example: the partial derivative of E with respect to M
Software Engineering sensitivity analysis
Feasibility studies during the exploratory and conceptual phases
Risk analyses during plans, requirements and product design phases
Difficult to compute partial derivatives for complicated function E
Pick neighboring values of the parameter, compare the resulting values of E

22. TPS Decision 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
E(N) =
Option B:
N( (N-1))
E(N) =
= 840N-40N2
1.0
= 40N(21-N)
For N = 10, E(N) = 40(10)(11) = 4400

23. Cost-Effectiveness Comparison: TPS II Options A, B
5000
Option B
E(N) = 40N(21 – N)
C(N) = N
4000
3000
2000
1000
450
650
1207
500
1000
1500
Cost C, $K

24. 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

25. Segments of Production Function
Output
Input
High payoff
Diminishing returns
Investment

26. 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

27. 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

28. Combat Diseconomies of Scale
Reduce the Scale !
Prototyping
Incremental development
Wish-list pruning
Avoid gold-plating
Hardware-software products
System partition

29. Software Gold-Plating
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
Agents with attitudes
Animated displays
“everything for everybody”
Modularity, info. hiding
Measurement, diagnostics

30. 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
Module 2

31. Cost-Effectiveness Decision Criteria
Maximum available budget
Minimum performance requirement
Maximum effectiveness/cost ratio
Maximum effectiveness – cost difference
Return on investment (ROI)
Composite alternatives

32. Production Functions for TPS II Options A, B
1207
2400
4400 X Y Q Z P
E (tr/sec)
1007
650
450

33. Minimum Performance Requirement
Impose an overly rigorous performance requirement can lead to an unsatisfactory commitment
Suppose our application requires a performance of at least 2500 tr/sec

34. 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)

35. 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)

36. Return on Investment ROI
E-C C
Cost C ($K) 1 2 3 4 5
500
1000
1500
2.67
2.60 A B

37. Production Function for TPS Composite Alternative
2400
4400 E
C, $K
E (r/sec)
450
1007
1207
650

38. 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