1. Personal Software Process Software Estimation
CIS 376
Bruce R. Maxim
UM-Dearborn
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2. Product Size Measures Must be:
Precise – gives an exact value for product size and is defined by the product content
Specific – based on defined product properties.
Countable – automated counting is far less tedious than manual counting and more accurate
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3. Establishing Satandards
Database Projects
fields, tables
GUI elements, queries, code lines
GUI Creation
buttons, boxes, labels
Programming
LOC, FP
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4. Expert Guessing A = The most pessimistic estimate.
B = The most likely estimate.
C = The most optimistic estimate.
Ê = (A + 4B + C) 6
(Weighted average; where Ê = estimate).
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5. Function Points 11/29/2018 Parameter Simple + Average Complex = Fi
Distinct input items
3( )
4( )
6( ) ?
Output screens/reports
5( )
7( )
Types of user queries
Number of files
10( )
15( )
External interface
Total
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6. Function Point Equation
F.P.’s = T * ( * Q)
T = unadjusted table total
Q = score from questionnaire
(14 items with values 0 to 5)
Cost of producing one function point? May be organization specific.
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7. Function Point Questionnaire
Backup.
Data communication.
Distributed processes.
Optimal performance.
Heavily used operating system.
On-line data security.
Multiple screens.
On-line master file update.
Complex inputs, queries, outputs.
Complex internal processing.
Reusable code.
Conversion or installation.
Multiple user sites.
Ease of use.
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8. COCOMO - I Model E = a Sb * m(x) 11/29/2018 BASIC INTERMEDIATE MODE a
BASIC
INTERMEDIATE
MODE a b
Organic
2.4
1.05
3.2
Semidetached
3.0
1.12
Embedded
3.6
1.20
2.8
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9. Basic COCOMO
Computes software development effort (and cost) as a function of program size, expressed in estimated lines of code.
m(x) = 1
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10. Intermediate COCOMO
Computes software development effort as a function of program size and a set of "cost drivers" that include subjective assessments of product, hardware, personnel, and project attributes.
m(x) =  m(xi)
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11. 11/29/2018

12. Detailed COCOMO
Includes all characteristics of the intermediate version with an assessment of the cost driver’s impact on each step (analysis, design, ect.) of the software engineering process
m(x) based on similar questionnaire
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13. Size Accounting Base – initial unmodified original program
Added – new lines not in base always increases LOC in base count
Modified – lines changed in base, does not increase LOC (includes adapted reused code)
Deleted – lines removed from base always decreases LOC
Reused – unmodified existing code increases LOC
Total – net sum of changes in base LOC
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14. Example Base code 500 LOC Added 60 LOC Deleted 40 LOC Modified 30 LOC
Reused 50 LOC
Total = – = 570
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15. Using Size Data Planning and tracking Assessing program quality
historical size and time data are necessary to estimate future project
Assessing program quality
useful in computing defect density and service costs
Assessing personal work
reused, added, deleted, modified code
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16. Productivity
Computed in terms of units produced divided by the time units required to create it
Total/time = 570 LOC / 50 hours
= 11.2 LOC/hours
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17. Size Counters
Manual counting works for small programs not large programs
Physical LOC Counters
count all text lines except counters
Logical LOC Counters
only count logical program lines defined by program control structures
Counting Program Elements
measure sizes of procedures or classes in large programs
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18. Proxy-Based Estimation
Estimates made by comparing new project to previously completed project
Requires that you have saved historical data from previous projects
May have to do estimation based on similar pieces from multiple projects
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19. Proxy Selection Criteria
Proxy size measure closely related to required development effort
Proxy content or product should be automatically countable
Proxy should be easy to visualize at project beginning
Proxy customizable to needs of each project and developer
Proxy sensitive to implementation variations affecting development cost or effort
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20. Possible Proxies Function points Classes Database elements Forms
Scripts
Document chapters
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21. Estimation Considerations - 1
Improving Estimation Accuracy
break up product in to several parts and compute average of several independent estimates for each
Estimating Bias
use historical data techniques to minimize impact of subjective judgment
Incomplete Knowledge Bias
reestimate product size several times during development
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22. Estimation Considerations - 2
Overcompensation
caused by making too small an estimate after being too large or the reverse
Selecting Abstraction Level
get the right grain size
Large Systems
divide in to separately estimated subsystems
Unprecedented Systems
build prototypes and extrapolate
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23. PROBE Estimation Method - 1
Step 1: Conceptual Design
identify parts of product after conceptual design is finished
Step 2: Estimate Parts Size
use relative size table to get estimate for each part based on expected size (VS, S, M, L, VL)
Step 3: Estimate Sizes of Reused Parts and Base Additions
estimate sizes of any additional parts needed
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24. PROBE Estimation Method - 2
Step 4: Size Estimating Procedure
select which PROBE method to use (A, B, C, D, E) the default is A
Step 5: Time Estimating Procedure
check your data to see whether or not you can use method A
Step 6: Prediction Interval
determine prediction interval surrounding the size or time estimate
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25. PROBE Use - 1 Estimating practice Estimating judgment Outliers
once regression parameters stabilize they do not need to be recomputed for every new program
Estimating judgment
poor proxy estimates will ruin PROBE estimate
Outliers
discard obvious outliers from regression data
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26. PROBE Use - 2 Using limited data
you need at least 3 historical data points for regression to work (30 is better)
Overcompensation and Undercompensation
only make changes when you change processes or discover ineffective practices (normal statistical fluctuations are common)
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