COCOMO II

1
COCOMO II
Dr. Ray Madachy madachy_at_usc.edu CSCI 510
September 30, 2002October 2, 2002
2
Agenda

• COCOMO introduction
• Basic estimation formulas
• Cost factors
• Reuse model
• Sizing
• USC COCOMO tool demo
• Data collection

3
Supplemental Handouts

• Software Cost Estimation with COCOMO II
• Estimation Form

4
Software Cost Estimation Methods

• Cost estimation prediction of both the
  person-effort and elapsed time of a project
• Methods
• Algorithmic
• Expert judgement
• Estimation by analogy
• Parkinsonian
• Best approach is a combination of methods
• compare and iterate estimates, reconcile
  differences
• COCOMO is the most widely used, thoroughly
  documented and calibrated cost model

• Price-to-win
• Top-down
• Bottom-up

5
COCOMO Background

• COCOMO - the COnstructive COst MOdel
• COCOMO II is the update to COCOMO 1981
• ongoing research with annual calibrations made
  available
• Originally developed by Dr. Barry Boehm and
  published in 1981 book Software Engineering
  Economics
• COCOMO II described in new book Software Cost
  Estimation with COCOMO II
• COCOMO can be used as a framework for cost
  estimation and related activities

6
Software Estimation Accuracy
4x

• Effect of uncertaintiesover time

2x
Relative Size Range
x
0.5x
Initial Operating Capability
OperationalConcept
Life Cycle Objectives
Life Cycle Architecture
0.25x
Feasibility
Plans/Rqts.
Design
Develop and Test
Phases and Milestones
7
COCOMO Black Box Model
product size estimate
development, maintenance cost and schedule
estimates
product, process, platform, and personnel
attributes
COCOMO II
reuse, maintenance, and increment parameters
cost, schedule distribution by phase, activity,
increment
organizational project data
recalibration to organizational data
8
Major COCOMO II Features

• Multi-model coverage of different development
  sectors
• Variable-granularity cost model inputs
• Flexibility in size inputs
• SLOCS
• function points
• application points
• other (use cases ...)
• Range vs. point estimates per funnel chart

9
COCOMO Uses for Software Decision Making

• Making investment decisions and business-case
  analyses
• Setting project budgets and schedules
• Performing tradeoff analyses
• Cost risk management
• Development vs. reuse decisions
• Legacy software phaseout decisions
• Software reuse and product line decisions
• Process improvement decisions

10
Productivity Ranges

• COCOMO provides natural framework to identify
  high leverage productivity improvement factors
  and estimate their payoffs.

11
COCOMO Submodels

• Applications Composition involves rapid
  development or prototyping efforts to resolve
  potential high-risk issues such as user
  interfaces, software/system interaction,
  performance, or technology maturity. Its sized
  with application points (weighted screen
  elements, reports and 3GL modules).
• The Early Design model involves exploration of
  alternative software/system architectures and
  concepts of operation using function points and a
  course-grained set of 7 cost drivers.
• The Post-Architecture model involves the actual
  development and maintenance of a software product
  using source instructions and / or function
  points for sizing, with modifiers for reuse and
  software breakage a set of 17 multiplicative
  cost drivers and a set of 5 factors determining
  the project's scaling exponent.

12
Agenda

• COCOMO introduction
• Basic estimation formulas
• Cost factors
• Reuse model
• Sizing
• USC COCOMO tool demo
• Data collection

13
COCOMO Effort Formulation

• of cost drivers
• Effort (person-months) A (Size)B P EMi
• i1
• Where
• A is a constant derived from historical project
  data (currently A 2.94 in COCOMOII.2000)
• Size is in KSLOC (thousand source lines of code),
  or converted from function points or object
  points
• B is an exponent for the diseconomy of scale
  dependent on five additive scale drivers
  according to b .91 .01SSFi,where SFi is a
  weighting factor for ith scale driver
• EMi is the effort multiplier for the ith cost
  driver. The geometric product results in an
  overall effort adjustment factor to the nominal
  effort.
• Automated translation effects are not included

14
Diseconomy of Scale

• Nonlinear relationship when exponent gt 1

15
COCOMO Schedule Formulation

• Where
• Schedule is the calendar time in months from the
  requirements baseline to acceptance
• C is a constant derived from historical project
  data (currently C 3.67 in COCOMOII.2000)
• Effort is the estimated person-months excluding
  the SCED effort multiplier
• B is the sum of project scale factors
• SCED is the compression / expansion percentage
  in the SCED cost driver
• This is the COCOMOII.2000 calibration
• Formula can vary to reflect process models for
  reusable and COTS software, and the effects of
  application composition capabilities.

Schedule (months) C (Effort)(.330.2(B-1.01))
x SCED/100
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Coverage of Different Processes

• COCOMO II provides a framework for tailoring the
  model to any desired process
• Original COCOMO was predicated on the waterfall
  process
• single-pass, sequential progression of
  requirements, design, code, test
• Modern processes are concurrent, iterative,
  incremental, and cyclic
• e.g. Rational Unified Process (RUP), the USC
  Model-Based Architecting and Software Engineering
  (MBASE) process
• Effort and schedule are distributed among
  different phases and activities per work
  breakdown structure of chosen process

17
Common Process Anchor Points

• Anchor points are common process milestones
  around which cost and schedule budgets are
  organized
• COCOMO II submodels address different development
  stages anchored by these generic milestones
• Life Cycle Objectives (LCO)
• inception establishing a sound business case
• Life Cycle Architecture (LCA)
• elaboration commit to a single architecture and
  elaborate it to cover all major risk sources
• Initial Operational Capability (IOC)
• construction commit to transition and support
  operations

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MBASE Phase Distributions

• see COCOMO II book for complete phase/activity
  distributions

Schedule
Effort
Phase
12.5
6
Inception
37.5
24
Elaboration
62.5
76
Construction
12.5
12
Transition
100
100
COCOMO Total
125
118
Project Total
19
Waterfall Phase Distributions
Schedule
Effort
Phase
20
7
Plans rqts
26
17
Product Design
48
58
Programming
Integration Test
25
26
12.5
12
Transition
100
100
COCOMO Total
125
119
Project Total
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COCOMO II Output Ranges

• COCOMO II provides one standard deviation
  optimistic and pessimistic estimates.
• Reflect sources of input uncertainties per funnel
  chart.
• Apply to effort or schedule for all of the stage
  models.
• Represent 80 confidence limits below optimistic
  or pessimistic estimates 10 of the time.

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COCOMO Tailoring and Enhancements

• Calibrate effort equations to organizational
  experience
• USC COCOMO has a calibration capability
• Consolidate or eliminate redundant cost driver
  attributes
• Add cost drivers applicable to your organization
• Account for systems engineering, hardware and
  software integration

22
Agenda

• COCOMO introduction
• Basic estimation formulas
• Cost factors
• Reuse model
• Sizing
• USC COCOMO tool demo
• Data collection

23
Cost Factors

• Significant factors of development cost
• scale drivers are sources of exponential effort
  variation
• cost drivers are sources of linear effort
  variation
• product, platform, personnel and project
  attributes
• effort multipliers associated with cost driver
  ratings
• Defined to be as objective as possible
• Each factor is rated between very low and very
  high per rating guidelines
• relevant effort multipliers adjust the cost up or
  down

24
Scale Drivers

• Precedentedness (PREC)
• Degree to which system is new and past experience
  applies
• Development Flexibility (FLEX)
• Need to conform with specified requirements
• Architecture/Risk Resolution (RESL)
• Degree of design thoroughness and risk
  elimination
• Team Cohesion (TEAM)
• Need to synchronize stakeholders and minimize
  conflict
• Process Maturity (PMAT)
• SEI CMM process maturity rating

25
Cost Drivers

• Product Factors
• Reliability (RELY)
• Data (DATA)
• Complexity (CPLX)
• Reusability (RUSE)
• Documentation (DOCU)
• Platform Factors
• Time constraint (TIME)
• Storage constraint (STOR)
• Platform volatility (PVOL)

• Personnel factors
• Analyst capability (ACAP)
• Program capability (PCAP)
• Applications experience (APEX)
• Platform experience (PLEX)
• Language and tool experience (LTEX)
• Personnel continuity (PCON)
• Project Factors
• Software tools (TOOL)
• Multisite development (SITE)
• Required schedule (SCED)

26
Example Cost Driver - Required Software
Reliability (RELY)

• Measures the extent to which the software must
  perform its intended function over a period of
  time.
• Ask what is the effect of a software failure?

27
Example Effort Multiplier Values for RELY
1.39
1.15
Very High
High
Very Low
Nominal
Low
1.0
Slight Inconvenience
Low, Easily Recoverable Losses
High Financial Loss
Moderate, Easily Recoverable Losses
Risk to Human Life
0.88
0.75

• E.g. a highly reliable system costs 39 more than
  a nominally reliable system 1.39/1.01.39)
• or a highly reliable system costs 85 more than a
  very low reliability system (1.39/.751.85)

28
Scale Factors

• Sum scale factors Wi across all of the factors to
  determine a scale exponent, B, using B .91
  .01 S Wi

29
Precedentedness (PREC) and Development
Flexibility (FLEX)

• Elaboration of the PREC and FLEX rating scales

30
Architecture / Risk Resolution (RESL)

• Use a subjective weighted average of the
  characteristics

31
Team Cohesion (TEAM)

• Use a subjective weighted average of the
  characteristics to account for project turbulence
  and entropy due to difficulties in synchronizing
  the project's stakeholders.
• Stakeholders include users, customers,
  developers, maintainers, interfacers, and others

32
Process Maturity (PMAT)

• Two methods based on the Software Engineering
  Institute's Capability Maturity Model (CMM)
• Method 1 Overall Maturity Level (CMM Level 1
  through 5)
• Method 2 Key Process Areas(see next slide)

33
Key Process Areas

• Decide the percentage of compliance for each of
  the KPAs as determined by a judgement-based
  averaging across the goals for all 18 Key Process
  Areas.

34
Cost Drivers

• Product Factors
• Platform Factors
• Personnel Factors
• Project Factors

35
Product Factors

• Required Software Reliability (RELY)
• Measures the extent to which the software must
  perform its intended function over a period of
  time. Ask what is the effect of a software
  failure

36
Product Factors contd

• Data Base Size (DATA)
• Captures the effect large data requirements have
  on development to generate test data that will be
  used to exercise the program.
• Calculate the data/program size ratio (D/P)

37
Product Factors contd

• Product Complexity (CPLX)
• Complexity is divided into five areas
• control operations,
• computational operations,
• device-dependent operations,
• data management operations, and
• user interface management operations.
• Select the area or combination of areas that
  characterize the product or a sub-system of the
  product.
• See the module complexity table, next several
  slides

38
Product Factors contd

• Module Complexity Ratings vs. Type of Module
• Use a subjective weighted average of the
  attributes, weighted by their relative product
  importance.

39
Product Factors contd

• Module Complexity Ratings vs. Type of Module
• Use a subjective weighted average of the
  attributes, weighted by their relative product
  importance.

40
Product Factors contd

• Required Reusability (RUSE)
• Accounts for the additional effort needed to
  construct components intended for reuse.
• Documentation match to life-cycle needs (DOCU)
• What is the suitability of the project's
  documentation to its life-cycle needs.

41
Platform Factors

• Platform
• Refers to the target-machine complex of hardware
  and infrastructure software (previously called
  the virtual machine).
• Execution Time Constraint (TIME)
• Measures the constraint imposed upon a system in
  terms of the percentage of available execution
  time expected to be used by the system.

42
Platform Factors contd

• Main Storage Constraint (STOR)
• Measures the degree of main storage constraint
  imposed on a software system or subsystem.
• Platform Volatility (PVOL)
• Assesses the volatility of the platform (the
  complex of hardware and software the software
  product calls on to perform its tasks).

43
Personnel Factors

• Analyst Capability (ACAP)
• Analysts work on requirements, high level design
  and detailed design. Consider analysis and design
  ability, efficiency and thoroughness, and the
  ability to communicate and cooperate.
• Programmer Capability (PCAP)
• Evaluate the capability of the programmers as a
  team rather than as individuals. Consider
  ability, efficiency and thoroughness, and the
  ability to communicate and cooperate.

44
Personnel Factors contd

• Applications Experience (AEXP)
• Assess the project team's equivalent level of
  experience with this type of application.
• Platform Experience (PEXP)
• Assess the project team's equivalent level of
  experience with this platform including the OS,
  graphical user interface, database, networking,
  and distributed middleware.

45
Personnel Factors contd

• Language and Tool Experience (LTEX)
• Measures the level of programming language and
  software tool experience of the project team.
• Personnel Continuity (PCON)
• The scale for PCON is in terms of the project's
  annual personnel turnover.

46
Project Factors

• Use of Software Tools (TOOL)
• Assess the usage of software tools used to
  develop the product in terms of their
  capabilities and maturity.

47
Project Factors contd

• Multisite Development (SITE)
• Assess and average two factors site collocation
  and communication support.
• Required Development Schedule (SCED)
• Measure the imposed schedule constraint in terms
  of the percentage of schedule stretch-out or
  acceleration with respect to a nominal schedule
  for the project.

48
Cost Factor Rating

• Whenever an assessment of a cost driver is
  between the rating levels
• always round to the Nominal rating
• e.g. if a cost driver rating is between High and
  Very High, then select High.

49
Cost Driver Rating Level Summary
50
Cost Driver Rating Level Summary contd
51
Agenda

• COCOMO introduction
• Basic estimation formulas
• Cost factors
• Reuse model
• Sizing
• USC COCOMO tool demo
• Data collection

52
Reused and Modified Software

• Effort for adapted software (reused or modified)
  is not the same as for new software.
• Approach convert adapted software into
  equivalent size of new software.

53
Nonlinear Reuse Effects

• The reuse cost function does not go through the
  origin due to a cost of about 5 for assessing,
  selecting, and assimilating the reusable
  component.
• Small modifications generate disproportionately
  large costs primarily due the cost of
  understanding the software to be modified, and
  the relative cost of interface checking.

54
COCOMO Reuse Model

• A nonlinear estimation model to convert adapted
  (reused or modified) software into equivalent
  size of new software

55
COCOMO Reuse Model contd

• ASLOC - Adapted Source Lines of Code
• ESLOC - Equivalent Source Lines of Code
• AAF - Adaptation Adjustment Factor
• DM - Percent Design Modified. The percentage of
  the adapted software's design which is modified
  in order to adapt it to the new objectives and
  environment.
• CM - Percent Code Modified. The percentage of the
  adapted software's code which is modified in
  order to adapt it to the new objectives and
  environment.
• IM - Percent of Integration Required for Modified
  Software. The percentage of effort required to
  integrate the adapted software into an overall
  product and to test the resulting product as
  compared to the normal amount of integration and
  test effort for software of comparable size.
• AA - Assessment and Assimilation effort needed to
  determine whether a fully-reused software module
  is appropriate to the application, and to
  integrate its description into the overall
  product description. See table.
• SU - Software Understanding. Effort increment as
  a percentage. Only used when code is modified
  (zero when DM0 and CM0). See table.
• UNFM - Unfamiliarity. The programmer's relative
  unfamiliarity with the software which is applied
  multiplicatively to the software understanding
  effort increment (0-1).

56
Assessment and Assimilation Increment (AA)
57
Software Understanding Increment (SU)

• Take the subjective average of the three
  categories.
• Do not use SU if the component is being used
  unmodified (DM0 and CM 0).

58
Programmer Unfamiliarity (UNFM)

• Only applies to modified software

59
Commercial Off-the-Shelf (COTS) Software

• Current best approach is to treat as reuse
• A COTS cost model is under development
• Calculate effective size from external interface
  files and breakage
• Have identified candidate COTS cost drivers

60
Reuse Parameter Guidelines
61
Agenda

• COCOMO introduction
• Basic estimation formulas
• Cost factors
• Reuse model
• Sizing
• USC COCOMO tool demo
• Data collection

62
Lines of Code

• Source Lines of Code (SLOCs) logical source
  statements
• Logical source statements data declarations
  executable statements
• Executable statements cause runtime actions
• Declaration statements are nonexecutable
  statements that affect an assembler's or
  compiler's interpretation of other program
  elements
• Codecount tool available on USC web site

63
Lines of Code Counting Rules

• Standard definition for counting lines
• Based on SEI definition checklist from
  CMU/SEI-92-TR-20
• Modified for COCOMO II
• When a line or statement contains more than one
  type, classify it as the type with the highest
  precedence. Order of precedence is in ascending
  order

64
Lines of Code Counting Rules contd

• (See COCOMO II book for remaining details)

65
Counting with Function Points

• Used in both the Early Design and the
  Post-Architecture models.
• Based on the amount of functionality in a
  software product and project factors using
  information available early in the project life
  cycle.
• Quantify the information processing functionality
  with the following user function types

66
Counting with Function Points contd

• External Input (Inputs)
• Count each unique user data or user control input
  type that both
• Enters the external boundary of the software
  system being measured
• Adds or changes data in a logical internal file.
• External Output (Outputs)
• Count each unique user data or control output
  type that leaves the external boundary of the
  software system being measured.

67
Counting with Function Points contd

• Internal Logical File (Files)
• Count each major logical group of user data or
  control information in the software system as a
  logical internal file type. Include each logical
  file (e.g., each logical group of data) that is
  generated, used, or maintained by the software
  system.
• External Interface Files (Interfaces)
• Files passed or shared between software systems
  should be counted as external interface file
  types within each system.

68
Counting with Function Points contd

• External Inquiry (Queries)
• Count each unique input-output combination, where
  an input causes and generates an immediate
  output, as an external inquiry type.
• Each instance of the user function types is then
  classified by complexity level. The complexity
  levels determine a set of weights, which are
  applied to their corresponding function counts to
  determine the Unadjusted Function Points
  quantity.

69
Counting with Function Points contd

• The usual Function Point procedure involves
  assessing the degree of influence of fourteen
  application characteristics on the software
  project.
• The contributions of these characteristics are
  inconsistent with COCOMO experience, so COCOMO II
  uses Unadjusted Function Points for sizing.

70
Unadjusted Function Points Counting Procedure

• Step 1 - Determine function counts by type.
• The unadjusted function counts should be counted
  by a lead technical person based on information
  in the software requirements and design
  documents.
• The number of each of the five user function
  types should be counted
• Internal Logical File (ILF)
• Note The word file refers to a logically related
  group of data and not the physical implementation
  of those groups of data.
• External Interface File (EIF)
• External Input (EI)
• External Output (EO)
• External Inquiry (EQ))

71
Unadjusted Function Points Counting Procedure
contd

• Step 2 - Determine complexity-level function
  counts.
• Classify each function count into Low, Average
  and High complexity levels depending on the
  number of data element types contained and the
  number of file types referenced. Use the
  following scheme

72
Unadjusted Function Points Counting Procedure
contd

• Step 3 - Apply complexity weights.
• Weight the number in each cell using the
  following scheme. The weights reflect the
  relative value of the function to the user.
• Step 4 - Compute Unadjusted Function Points.
• Add all the weighted functions counts to get one
  number, the Unadjusted Function Points.

73
Agenda

• COCOMO introduction
• Basic estimation formulas
• Cost factors
• Reuse model
• Sizing
• USC COCOMO tool demo
• Data collection

74
USC COCOMO Demo
75
Agenda

• COCOMO introduction
• Basic estimation formulas
• Cost factors
• Reuse model
• Sizing
• USC COCOMO tool demo
• Data collection

76
Cost Driver Ratings Profile

• Need to rate cost drivers in a consistent and
  objective fashion within an organization.
• Cost driver ratings profile
• Graphical depiction of historical ratings to be
  used as a reference baseline to assist in rating
  new projects
• Used in conjunction with estimating tools to
  gauge new projects against past ones objectively

77
Example Cost Driver Ratings Profile
78
Techniques to Generate Cost Driver Ratings Profile

• Single person
• Time efficient, but may impose bias and person
  may be unfamiliar with all projects
• Group
• Converge ratings in a single meeting (dominant
  individual problem)
• Wideband Delphi technique (longer calendar time,
  but minimizes biases). See Software Engineering
  Economics, p. 335

79
COCOMO Dataset Cost Metrics

• Size (SLOCS, function points)
• Effort (Person-hours)
• Schedule (Months)
• Cost drivers
• Scale drivers
• Reuse parameters

80
Recommended Project Cost Data

• For each project, report the following at the end
  of each month and for each release
• SIZE
• Provide total system size developed to date, and
  report new code size and reused / modified code
  size separately. This can be at a project level
  or lower level as the data supports and is
  reasonable. For languages not supported by tools
  such as assembly code, report the number of
  physical lines separately for each language.
• EFFORT
• Provide cumulative staff-hours spent on software
  development per project at the same granularity
  as the size components.

81
Recommended Project Cost Data contd

• COST DRIVERS AND SCALE DRIVERS
• For each reported size component, supply the cost
  driver ratings for product, platform, personnel
  and project attributes. For each reported size
  component, supply scale driver ratings.
• REUSE PARAMETERS
• For each component of reused/modified code,
  supply reuse parameters AA, SU, UNFM, DM, CM and
  IM.
• See Appendix C in COCOMO II book for additional
  data items
• Post-mortem reports are highly recommended

82
Effort Staff-Hours Definition

• Standard definition
• Based on SEI definition checklist form
  CMU/SEI-92-TR-21
• Modified for COCOMO II
• Does not include unpaid overtime, production and
  deployment activities, customer training
  activities
• Includes all personnel except level 3 or higher
  software management (i.e. directors or above who
  timeshare among projects)
• Person-month is defined as 152 hours

83
Further Information

• B. Boehm, C. Abts, W. Brown, S. Chulani, B.
  Clark, E. Horowitz, R. Madachy, D. Reifer, B.
  Steece, Software Cost Estimation with COCOMO II,
  Prentice-Hall, 2000
• B. Boehm, Software Engineering Economics.
  Englewood Cliffs, NJ, Prentice-Hall, 1981
• Main COCOMO website at USC http//sunset.usc.edu/
  research/COCOMOII
• COCOMO information at USC [phone]
• COCOMO email cocomo-info_at_sunset.usc.edu