trends in productivity and cocomo cost drivers over the years

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



© 2010, USC-CSSE 2

Outline

Objectives and Background

Productivity Trend

Discussions and Conclusions

Cost Driver Trends



© 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



© 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



© 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



© 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



© 2010, USC-CSSE 7

Outline


Productivity Trend


Cost Driver Trends



© 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



© 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)



© 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



© 2010, USC-CSSE 11

Outline


Productivity Trend


Cost Driver Trends



© 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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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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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



© 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

trends in productivity and cocomo cost drivers over the years

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