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

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

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

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

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

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

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

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

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)

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

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

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

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

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

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

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

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

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

University of Southern California

Center for Systems and Software Engineering

© 2010, USC-CSSE 19

Thank You