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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 1
Trends in Productivity and COCOMO Cost Drivers over the Years
Vu NguyenCenter for Systems and Software Engineering (CSSE)
CSSE Annual Research Review 2010
Mar 9th, 2010
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 2
Outline
Objectives and Background
Productivity Trend
Discussions and Conclusions
Cost Driver Trends
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 3
Objectives
• Analysis of Productivity
– How the productivity of the COCOMO data projects has changed over the years
– What caused the changes in productivity
• Analysis of COCOMO cost drivers
– How cost driver ratings have changed over the years
– Are there any implications from these changes
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 4
Estimation models need upgrading
• It has been 10 years since the release of COCOMO II.2000
– Data collected during 1970 – 1999
• Software engineering practices and technologies are changing
– Process: CMM CMMI, ICM, agile methods
– Tools are more sophisticated
– Advanced communication facility
• Improved storage and processing capability
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 5
COCOMO II Formula
• Effort estimate (PM)
– COCOMO II 2000: A and B constants were calibrated using 161 data points with A = 2.94 and B = 0.91
• Productivity =
• Constant A is considered as the inverse of adjusted productivity
EMSizeAPM
SFB**
01.0
EMSize
PMA
SFB*
01.0
PM
Size
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 6
COCOMO Data Projects Over the Five-year Periods
• Dataset has 341 projects completed between 1970 and 2009
– 161 used for calibrating COCOMO II 2000
– 149 completed since 2000
12
36
0
1722
105 102
47
0
20
40
60
80
100
1970-1974
1975-1979
1980-1984
1985-1989
1990-1994
1995-1999
2000-2004
2005-2009
Five-year periods
# o
f d
ata
pro
jec
ts
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 7
Outline
Objectives and Background
Productivity Trend
Discussions and Conclusions
Cost Driver Trends
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 8
Average productivity is increasing over the periods• Two productivity increasing trends exist: 1970 – 1994 and 1995 –
2009
1970-1974 1975-1979 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009
Five-year Periods
KS
LO
C p
er P
M
• 1970-1999 productivity trends largely explained by cost drivers and scale factors
• Post-2000 productivity trends not explained by cost drivers and scale factors
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 9
Effort Multipliers and Scale Factors
• EM’s and SF’s don’t change sharply as does the productivity over the periods
EA
F
1970- 1975- 1980- 1985- 1990- 1995- 2000- 2005-1974 1979 1984 1989 1994 1999 2004 2009
Su
m o
f S
cale
Fac
tors
1970- 1975- 1980- 1985- 1990- 1995- 2000- 2005-1974 1979 1984 1989 1994 1999 2004 2009
Effort Adjustment Factor (EAF) or ∏EM Sum of Scale Factors (SF)
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 10
Constant A generally decreases over the periods
• Calibrate the constant A while stationing B = 0.91
• Constant A is the inverse of adjusted productivity
– adjusts the productivity with SF’s and EM’s
• Constant A decreases over the periods
EMSize
PMA
SFB*
01.0
50% decrease over the post-2000 period
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0 1 2 3 4 5 6 7 8 9
Con
stan
t A
1970- 1975- 1980- 1985- 1990- 1995- 2000- 2005- 1974 1979 1984 1989 1994 1999 2004 2009
EMSizeAPM
SFB**
01.0
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 11
Outline
Objectives and Background
Productivity Trend
Discussions and Conclusions
Cost Driver Trends
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 12
Correlation between cost drivers and completion years
• Trends in cost drivers
– Cost drivers unchanged
• TEAM, FLEX, RESL, RELY, CPLX, ACAP, PCAP, RUSE, DOCU, PCON, SITE, SCED
– Increasing trends: increasing effort
• DATA, APEX– Decreasing trends: decreasing effort
• PMAT, TOOL, PREC,TIME, STOR, PLEX, LTEX, PVOL
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 13
Application and Platform Experience
• Platform and language experience has increased while application experience decreased
– Programmers might have moved projects more often in more recent years
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 14
Use of Tools and Process Maturity
• Use of Tools and Process Maturity have increased significantly
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 15
Storage and Time Constraints
• Storage and Time are less constrained than they were
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 16
Outline
Objectives and Background
Productivity Trend
Discussions and Conclusions
Cost Driver Trends
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 17
Discussions
• Productivity has doubled over the last 40 years
– But scale factors and effort multipliers did not fully characterize this increase
• Hypotheses/questions for explanation
– Is standard for rating personnel factors different among the organizations?
– Were automatically translated code reported as new code?
– Were reused code reported as new code?
– Are the ranges of some cost drivers not large enough?
• Improvement in tools (TOOL) only contributes to 20% reduction in effort
– Are more lightweight projects being reported?
• Documentation relative to life-cycle needs
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 18
Conclusions
• Productivity is generally increasing over the 40-year period
– SF’s and EM’s only partially explain this improvement
• Advancements in processes and technologies affect some cost drivers
– But majority of the cost driver ratings are unchanged
• Changes in productivity and cost drivers indicate that estimation models should recalibrate regularly
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University of Southern California
Center for Systems and Software Engineering
© 2010, USC-CSSE 19
Thank You