1. Local Calibration for Maintenance Projects (with High Personnel Turnover) Anandi Hira, USC

2. Metrics and Terms SLOC (source lines of code)
Logical SLOC  Unified Code Count (UCC)
Equivalent SLOC
(Vu Nguyen’s PhD Dissertation  slight modification to COCOMO® II’s reuse model)
Normalized Effort (hours)

3. Presentation Outline Unified Code Count COCOMO® (SLOC-based)
Function Points
Estimate Effort
Estimate ESLOC
SNAP Points

4. Unified Code Count
SLOC counting metrics (such as logical SLOC, cyclomatic complexity)
C++
45 to 1425 logical SLOC
10% personnel continuity every 4 months
Project Types:
New Language Parsers
New Features (such as GUI, output options, etc.)
New Metrics (modify current language parsers)

5. Presentation Outline Unified Code Count COCOMO® (SLOC-based)
Function Points
Estimate Effort
Estimate ESLOC
SNAP Points

6. COCOMO® Model Adjust All Projects Project-Specific
Personnel Continuity (PCON)
Lowest: 52% / year
UCC: 10% / 4 mo.
Applications Experience (APEX)  Low
Documentation Match to Needs (DOCU)
Platform Experience (PLEX)  Low
Analyst Capability (ACAP)
Tool Use (TOOL)  Very Low
Programmer Capability (PCAP)
Complexity (CPLX)

7. Local Calibration Results
PCON = 1.757
Fixed Cost =
hrs.
Observations 27 R2
0.90
PRED (20)
70.37
PRED (25)
PRED (30)
88.889

8. Presentation Outline Unified Code Count COCOMO® (SLOC-based)
Function Points
Estimate Effort
Estimate ESLOC
SNAP Points

9. Function Points vs Effort

10. Add Functions: EFP vs Effort R2
0.928
PRED (20)
100
PRED (25)
PRED (30)

11. Add Functions: EFP vs ESLOC
ESLOC = × EFP +
× # modified functions
EFP vs ESLOC Regression Statistics
EFP vs Effort using ESLOC estimates
EFP vs Effort Regression Statistics R2
0.901
PRED (20)
62.5
PRED (25) 75
PRED (30) R2
N/A
PRED (20)
37.5
PRED (25) 50
PRED (30) R2
0.928
PRED (20)
100
PRED (25)
PRED (30)

12. Modifications to Functions: EFP vs Effort
Effort = – 2.53 × CHGA × CHGB – × # mod files × # New funct × # Mod Funct R2
0.727
PRED (20)
57.143
PRED (25)
71.429
PRED (30)
85.714

13. Mod Functions: EFP vs ESLOC
ESLOC = × EFP^
(-0.04 × mod files × new funct )
EFP vs ESLOC Regression Statistics
EFP vs Effort using ESLOC estimates
EFP vs Effort Regression Statistics R2
0.713
PRED (20)
38.09
PRED (25)
PRED (30)
61.91 R2
N/A
PRED (20)
19.05
PRED (25)
PRED (30)
23.81 R2
0.727
PRED (20)
57.14
PRED (25)
71.43
PRED (30)
85.71

14. Presentation Outline Unified Code Count COCOMO® (SLOC-based)
Function Points
Estimate Effort
Estimate ESLOC
SNAP Points

15. Conclusion Local Calibration leads to better estimates
Environment may require adjustments to cost drivers
Function Points lead to good effort estimates for new functions
Function Points do not account for calculation
More research needed for SNAP
Future Work: Try other size measures