University of Southern California
Center for Systems and Software Engineering
Reuse and Maintenance Estimation
Vu Nguyen
March 17, 2009
Center for Systems and Software Engineering
University of Southern California
© 2009, USC-CSSE
1
University of Southern California
Center for Systems and Software Engineering
Reuse and Maintenance
– A Source Code Perspective
Reused Pieces
(Unmodified)
Acquired Pieces
Project Execution
Adapted Pieces
(modified, deleted,
added)
Exiting System’s
Pieces
New Pieces
© 2009, USC-CSSE
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University of Southern California
Center for Systems and Software Engineering
COCOMO II Reuse and Maintenance
Models
• Reuse Model
Equivalent KSLOC  Adapted KLOC * (1 
AT
) * AAM
100
AAF  0.4 * DM  0.3 * CM  0.3 * IM
 AA  AAF (1  0.02 * SU *UNFM )
, for AAF  50

100
AAM  
AA  AAF  SU *UNFM

, for AAF  50
100

• Maintenance Model
Size = (Size Added + Size Modified) * (1 + SU*UNFM/100)
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University of Southern California
Center for Systems and Software Engineering
Limitations
• The two models do not converge although reuse
and maintenance are indistinguishable
• Unclear how to determine the “actual” equivalent
SLOC from a completed system
• Determining the models’ parameters DM, CM, IM,
Adapted SLOC, etc. is difficult
• Difficult to calibrate the models
• Exploratory results
– Smoothed AAM factor
– Revised equivalent SLOC
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University of Southern California
Center for Systems and Software Engineering
Adaptation Adjustment Modifier (AAM)
1.60
AAM worse case:
AA = 8
UNFM = 1
SU = 50
1.40
Relative Cost
1.20
New AAM formula
1.00
Old AAM formula
0.80
0.60
Relative # of interfaces checked
0.40
0.20
0.00
0
20
40
60
80
100
120
Relative Amount of Modification (AAF)
New AAM formula:
AAF 2

 AA  AAF  [1  (1  100 ) ] * SU *UNFM

if AAF  100
AAM  
100

AA  AAF  SU *UNFM
if AAF  100

100

© 2009, USC-CSSE
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University of Southern California
Center for Systems and Software Engineering
Calculating Actual Equivalent SLOC
Based on CodeCount Tool
• Equivalent Adapted KSLOC (EKSLOCadapted)
– Determining “actual” DMadapted, CMadapted, IMadapted
• DMadapted = CMadapted = IMadapted = KSLOCmodified/KSLOCadapted
• AAFadapted = KSLOCmodified/KSLOCadapted
– EKSLOCadapted = KSLOCadapted * AAMadapted
• Equivalent Reused KSLOC (EKSLOCreused)
• DMreused = CMreused = 0 (no modification needed)
• AAMreused = AAFreused = 0.3 * IMreused
– EKSLOCreused = 0.3 KSLOCreused * IMreused
• Total Equivalent KSLOC
EKSLOC = KSLOCadded + EKSLOCadapted + EKSLOCreused
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University of Southern California
Center for Systems and Software Engineering
Product Factor
• A product often has multiple releases
– Each release is delivered by a project
• Each release is based on a baseline release
– Unmodified modules are reused
– Modified modules are adapted
– New modules are added
– Some modules are deleted
– Inherit overall software complexity
• Leverage some environments, processes,
personnel, and hence productivity pattern (e.g.,
decrease or increase)
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University of Southern California
Center for Systems and Software Engineering
Product Factor (cont’d)
• Calibration with product factor
– Each product has multiple releases
– Each project is corresponding to a release
• Regression equation (2)
log(PMi) – [SFi * log(Sizei) + log(EAFi)]
= [0 + 1*log(Sizei)] *  fi(j)
j
Where
productN

fi(j) = 1 if product(i) = j
j=product1
= 0 otherwise
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University of Southern California
Center for Systems and Software Engineering
Validation
• Models
– R1: Use existing COCOMO II Reuse Model, A = 2.94,
and B = 0.91
– R2: Use the modified formulas above and calibrate A
and B values
– R3: Same as R2 plus applying the “product factor” using
the regression equation (2)
• Calculate MMRE, PRED(.25), and PRED(.30) from
the three models
• Use dataset of 29 releases
© 2009, USC-CSSE
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University of Southern California
Center for Systems and Software Engineering
Dataset
• A dataset from CSSE’s Affiliate
– 6 products
– 29 releases
– Application types
• data processing
• Military – Ground
• Test
Product
No
1
2
3
4
5
6
© 2009, USC-CSSE
# of Releases
8
4
4
6
5
2
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University of Southern California
Center for Systems and Software Engineering
Results
• Models
– R1: Use existing COCOMO II Reuse Model, A = 2.94, and B = 0.91
– R2: Use the modified formulas above and calibrate A and B values
– R3: Same as R2 plus applying the “product factor” using the
regression equation (2)
Model
R1
R2
R3
MMRE
0.45
0.29
0.15
PRED25
0.31
0.48
0.90
© 2009, USC-CSSE
PRED30
0.34
0.55
0.93
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University of Southern California
Center for Systems and Software Engineering
Results (cont’d)
• The results suggest that the calibrated model
generates better estimates
• Calibrated model using data from projects for the
same product (Model R3) potentially has best
performance
• Limitations of Model R3
– Calibrated A and B values are unstable, high variant
– Calibrated A and B values are sometimes counterintuitive (e.g., A < 1, B < 0)
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University of Southern California
Center for Systems and Software Engineering
Next steps
• Modify the AAM formula
– Apply regression to determine the coefficients for
KSLOCadded, EKSLOCadapted, EKSLOCreused
• Perform Delphi survey to determine the constant
values of SU, UNFM, DM, CM, IM
• Determine the weights of Added, Modified, and
Deleted SLOC in adapted modules
• Collect more data to calibrate COCOMO cost
driver values for maintenance and reuse model
– Explore causes of variation in “product factor”
– May discover an explanatory driver
© 2009, USC-CSSE
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