COCOMO II Maintenance Model Upgrade

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COCOMO II Maintenance Model Upgrade

• Vu Nguyen, Barry Boehm
• Center for Systems and Software Engineering
  (CSSE)
• CSSE Annual Research Review 2010
• Mar 8th, 2010

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Outline

• Motivation
• Research Problem and A Solution
• Major Software Maintenance Estimation Models
• COCOMO II for Maintenance
• Research Validation and Preliminary Results
• Next Steps
• Expected Main Contributions
• References

3
Magnitude of Software Maintenance

• Majority of software costs incur after the first
  operational release Boehm 1981

Fig. 1. Software maintenance cost versus total
software cost
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Importance of Software Estimation in Managing
Software Projects

• Estimation is a key success factor of software
  projects
• Two out of three most-cited project failures are
  related to resource estimation, according to a
  CompTIA survey in 2007 Rosencrance 2007
• Cost estimate is the key information for
  investment, project planning and control, etc.
• Many software estimation approaches have been
  proposed and used in industry
• E.g., COCOMO, SEER-SEM, SLIM, PRICE-S, Function
  Point Analysis

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The Problem

• These models are built on the assumptions of new
  development projects
• Problem is that these assumptions do not always
  hold in software maintenance due to differences
  between new development and maintenance
• Low estimation accuracies achieved

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A Solution
Improved COCOMO models that allow estimators to
better determine the equivalent size of
maintained software and estimate maintenance
effort can improve the accuracy of effort
estimation of software maintenance.
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Major Software Maintenance Estimation Models

• Estimate cost for the maintenance phase after the
  software is delivered
• All types of regular maintenance tasks are
  included
• Most of the models use SLOC as a size input
• Lack of empirical evaluation of these models

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Outline

• Motivation
• Research Problem and A Solution
• Major Software Maintenance Estimation Models
• COCOMO II for Maintenance
• Modeling Process
• A Software Maintenance Sizing Method
• Effort Model
• Calibration Techniques
• Research Validation and Preliminary Results
• Next Steps
• Expected Main Contributions
• References

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The Modeling Process
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Analyze existing literature
2
Perform maintenance experiment to validate some
size measures
3A
3B
Perform Behavioral Analysis Identify relative
significance of factors
Determine sizing method for maintenance
4
Determine form of effort model
5A
5B
Perform Expert-judgment and Delphi assessment
Gather project data
6
Test hypotheses about impact of parameters
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Calibrate model parameters Determine parameter
variability
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Evaluate model performance
Fig. 2. The Modeling Process, Adapted from the
COCOMO Modeling Process Boehm 2000
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COCOMO II for Maintenance A Glance

• An extension of COCOMO II
• COCOMO is the non-proprietary most popular model
• COCOMO has attracted many independent validations
  and extensions
• Two Components
• A Unified Reuse and Maintenance Model
• Determining equivalent SLOC for reuse and
  maintenance
• COCOMO Effort Model for Maintenance
• Using a different set of parameters and constants

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Software Maintenance Sizing

• Size is a key determinant of effort
• Sizing method has to take into account different
  types of code

Fig.3. Types of Code
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A Unified Reuse and Maintenance Model 1/3

• Objectives
• Provide a consistent size measure for both reuse
  and maintenance
• Better account for different types of code
• Changes
• Use a single model for both reuse and maintenance
• Redefine the reuse model equation and parameters
  to
• account for code expansion
• allow equivalent SLOC to be determined from
  completed code
• include deleted SLOC in modified modules, but
  excluded SLOC in deleted modules
• smooth the curve representing nonlinear effects

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A Unified Reuse and Maintenance Model 2/3

• New AAF and AAM equations

AAF Adaptation Adjustment Factor ( of
modification) AAM Adaptation Adjustment
Multiplier DM Percentage of Design Modified,
accounting for only design changes made to the
preexisting modules IM Percentage of
Integration changed, relative to the integration
of the preexisting modules CM Percentage of
Code Modified, including added, modified, deleted
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A Unified Reuse and Maintenance Model 3/3

• Compute Equivalent SLOC (ESLOC)
• New Modules
• Adapted Modules
• Reused Modules
• Total Equivalent KSLOC

KSLOC of the adapted modules before changes
KSLOC of the reused modules
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COCOMO Effort Model for Maintenance

• Follows the same COCOMO II non-linear form
• Where,
• PM project effort measured in person-month
• A a multiplicative constant, calibrated using
  data sample
• B an exponent constant, calibrated using data
  sample
• Size software size measured in SLOC
• EM effort multipliers, cost drivers that have
  an multiplicative effect on effort
• SF scale factors, cost drivers that have an
  exponential effect on effort
• Linearize the model using log-transformation
• log(PM) ?0 ?1 log(Size) ?i SFi
  log(Size) ?j log(EMj)

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Calibration

• Process of fitting data to the model to adjust
  its parameters and constants

Rating scales for cost drivers
New rating scales for cost drivers and Constants
Delphi survey of experts (Expert-judgment
estimates)
Model Calibration

• Calibration Techniques
• Ordinary Least Squares Regression (OLS)
• Bayesian Analysis
• Constrained Regression Technique Nguyen 2008

Sample data
Fig.4. Calibration Process
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Outline

• Motivation
• Research Problem and A Solution
• Major Software Maintenance Estimation Models
• COCOMO II for Maintenance
• Research Validation and Preliminary Results
• Next Steps
• Expected Main Contributions
• References

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Data Collection 1/2

• Collect data of completed maintenance projects
  from industry
• Maintenance type error corrections,
  enhancements, etc. Excluding reengineering and
  language-migration projects
• CodeCount tool (UCC) is used for size collection
  Nguyen 2007

Fig.5. Release Period to be Collected
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Data Collection 2/2

• Metrics

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Current Status

• Completed the controlled experiment
• Collected 83 projects/releases
• 64 from a large organization member of CSSE
  Affiliates
• 14 from a CMMI-Level 5 company in Vietnam
• 4 from a CMMI-Level 3 company in Thailand
• Generated preliminary results using Bayesian and
  constrained regression techniques
• Use cost-driver rating scales from Delphi
  exercise for COCOMOII.2000 (Delphi COCOMOII.2000)
• Delphi survey has yet to be completed

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Preliminary Results 1/2

• Relative Impact of Cost Drivers on Effort
• Rating scales Delphi COCOMOII.2000

Sample data 161 projects
Sample data 83 projects
Values Generated by COCOMO II for
Maintenance Using the Bayesian Analysis
COCOMO II.2000 Values Boehm 2000
Fig. 6. Productivity Ranges
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Preliminary Results 2/2

• Estimation Accuracies
• Estimated 83 projects using
• COCOMO II.2000
• COCOMO II for Maintenance Bayesian analysis
• COCOMO II for Maintenance Constrained regression
• Rating scales Delphi COCOMOII.2000
• Computed MMRE and PRED(0.3) values

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Next Steps

• Perform Delphi survey to obtain expert-judgment
  rating scales for maintenance
• Continue data collection
• COCOMO data for both new development and
  maintenance projects
• Validate the research hypotheses
• Analyze and validate the models

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Expected Main Contributions

• A model for sizing maintenance and reuse
• An extended COCOMO model for maintenance
• A set of cost drivers and levels of their impact
  on maintenance cost
• Empirical validations on the impact of cost
  drivers for software maintenance

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References

• Boehm B.W. (1981), Software Engineering
  Economics, Prentice-Hall, Englewood Cliffs, NJ,
  1981.
• Erlikh L. (2000). Leveraging legacy system
  dollars for E-business. (IEEE) IT Pro, May/June,
  17-23.
• McKee J. (1984). Maintenance as a function of
  design. Proceedings of the AFIPS National
  Computer Conference, 187-193.
• Moad J. (1990). Maintaining the competitive
  edge. Datamation 61-62, 64, 66.
• Nguyen V., Deeds-Rubin S., Tan T., Boehm B.W.
  (2007), A SLOC Counting Standard, The 22nd
  International Annual Forum on COCOMO and
  Systems/Software Cost Modeling.
• Nguyen V., Steece B., Boehm B.W. (2008), A
  constrained regression technique for COCOMO
  calibration, Proceedings of the 2nd ACM-IEEE
  international symposium on Empirical software
  engineering and measurement (ESEM), pp. 213-222
• Rosencrance L. (2007), "Survey Poor
  communication causes most IT project failures,"
  Computerworld
• Zelkowitz M.V., Shaw A.C., Gannon J.D. (1979).
  Principles of Software Engineering and Design.
  Prentice-Hall

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