An analysis of changes in productivity and cocomo cost drivers over the years
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An Analysis of Changes in Productivity and COCOMO Cost Drivers over the Years. Vu Nguyen, Barry Boehm Center for Systems and Software Engineering (CSSE) 24 th Int’l COCOMO Forum Nov 3 rd , 2009. Outline. Objectives and Background. Productivity Changes. Cost Driver Changes.

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An Analysis of Changes in Productivity and COCOMO Cost Drivers over the Years

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An analysis of changes in productivity and cocomo cost drivers over the years

An Analysis of Changes in Productivity and COCOMO Cost Drivers over the Years

Vu Nguyen, Barry Boehm

Center for Systems and Software Engineering (CSSE)

24th Int’l COCOMO Forum

Nov 3rd, 2009


Outline

Outline

Objectives and Background

Productivity Changes

Cost Driver Changes

Discussions and Conclusions


Objectives and background

Objectives and Background


Objectives

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 periods?

    • Are there any implications from these changes?

  • Inviting further discussions


Estimation models need upgrading

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


Cocomo data projects over the five year periods

COCOMO Data Projects Over the Five-year Periods

  • Dataset has 255 projects completed between 1970 and 2009

    • 161 used in COCOMO II 2000

    • 87 collected since 2000

7 projects were collected before 2000 but not included in COCOMO II 2000


Cocomo ii formula

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


Changes in productivity and cost drivers

Changes in Productivity and Cost Drivers


Average productivity is increasing over the periods

1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999 2000-2004 2005-2009

Average productivity is increasing over the periods

  • Two productivity increasing trends exist: 1970 – 1994 and 1995 – 2009

  • 1970-1999 productivity trends largely explained by cost drivers and scale factors

  • Post-2000 productivity trends not explained by cost drivers and scale factors

SLOC per PM

Five-year Periods


Effort multipliers and scale factors

EAF

Sum of Scale Factors

1970- 1975- 1980- 1985- 1990- 1995- 2000- 2005-

1974 1979 1984 1989 1994 1999 2004 2009

1970- 1975- 1980- 1985- 1990- 1995- 2000- 2005-

1974 1979 1984 1989 1994 1999 2004 2009

Effort Multipliers and Scale Factors

  • EM’s and SF’s don’t change sharply as does the productivity over the periods

Effort Adjustment Factor (EAF) or ∏EM

Sum of Scale Factors (SF)


Changes in product factors and platform factors

1970- 1975- 1980- 1985- 1990- 1995- 2000- 2005-

1974 1979 1984 1989 1994 1999 2004 2009

Changes in Product Factors and Platform Factors

  • Testing data (DATA) increases over the years, suggesting an increase in size of the testing data

1970- 1975- 1980- 1985- 1990- 1995- 2000- 2005-

1974 1979 1984 1989 1994 1999 2004 2009

  • Storage constraint (STOR) is almost constant in the last three periods

  • Storage constraint was important during 1970 – 1979, but not during the other periods


Changes in product factors and platform factors1

1970- 1975- 1980- 1985- 1990- 1995- 2000- 2005-

1974 1979 1984 1989 1994 1999 2004 2009

Changes in Product Factors and Platform Factors

  • Programmer capability is low in the 70-74 period

  • Language and platform experience seems improving

  • Application experience seems declining

    • Programmers change projects more often?

Personnel Factors

1970- 1975- 1980- 1985- 1990- 1995- 2000- 2005-

1974 1979 1984 1989 1994 1999 2004 2009

  • Tool support gradually increases over the years

    • 20% improvement in effort over 40 years

  • Multisite development drops sharply in the last period

    • Supported by technologies?


Constant a decreases over the periods

Productivity is not fully characterized by SF’s and EM’s

What factors can explain the phenomenon?

1970- 1975- 1980- 1985- 1990- 1995- 2000- 2005-

1974 1979 1984 1989 1994 1999 2004 2009

Constant A 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

50% decrease over the post-2000 period


Analysis of productivity ranges

Analysis of Productivity Ranges

  • Productivity range is the ratio between the highest and lowest rating value for a cost driver

    • Represents the degree of impact of the cost driver on effort

  • Calibrated SF’s and EM’s by dividing the dataset into five sub-datasets

    • 161 projects used in COCOMO II 2000

      • completed and collected by 1999

    • 190 projects completed between 1990 – 2009

    • 51 maintenance projects

      • completed between 1989 – 2009

    • 149 new development projects

      • completed between 1989 – 2009


Pr of cost drivers of cocomo ii and all projects in 1990 2009

PR of Cost Drivers of COCOMO II and all projects in 1990 - 2009

COCOMO II.2000

1990 – 2009: including 97 projects used in COCOMO II.2000

  • Major changes:

  • Increase  CPLX

  • Decrease Θ DATA, ACAP, PMAT

1. Complexity is more influential in the more recent data

2. Analyst capability and process are less influential in the more recent data

3. Testing data is least influential


Pr of cost drivers of new development and maintenance

PR of Cost Drivers of New Development and Maintenance

Maintenance

New Development

  • Major changes:

  • Increase  DATA, DOCU

  • Decrease Θ PCON, ACAP, PCAP, CPLX, PREC

1. Testing data and documentation are more important in maintenance

2. Personnel capacity, complexity in maintenance is not as critical as in development


Discussions

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


Conclusions

Conclusions

  • Productivity is generally increasing over the 40-year period

    • SF’s and EM’s only partially explain this improvement

  • The impact of some cost drivers on effort has changed when calibrating more recent data

    • Increased impact of complexity (CPLX)

    • Decreased impact of testing data (DATA), Analyst capability (ACAP), and Process capability (PMAT)

  • Need for further analysis and calibration using recent data points


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