1.3 Estimation Techniques

• Expert judgement
• Estimation by analogy
• Parkinson's Law
• Pricing to win
• Algorithmic cost modelling

1.3.1 Expert judgement

• One or more experts in both software development and the application domain use their experience to predict software costs. Process iterates until some consensus is reached.
• Advantages: Relatively cheap estimation method. Can be accurate if experts have direct experience of similar systems
• Disadvantages: Very inaccurate if there are no experts!

1.3.2 Estimation by analogy

• The cost of a project is computed by comparing the project to a similar project in the same application domain
• Advantages: Accurate if project data available
• Disadvantages: Impossible if no comparable project has been tackled. Needs systematically maintained cost database

1.3.3 Parkinson's Law

• The project costs whatever resources are available
• Advantages: No overspend
• Disadvantages: System is usually unfinished

1.3.4 Pricing to win

• The project costs whatever the customer has to spend on it
• Advantages: You get the contract
• Disadvantages: The probability that the customer gets the system he or she wants is small. Costs do not accurately reflect the work required

1.3.4.0 Estimation methods  

• Each method has strengths and weaknesses
• Estimation should be based on several methods
• If these do not return approximately the same result, there is insufficient information available
• Some action should be taken to find out more in order to make more accurate estimates
• Pricing to win is sometimes the only applicable method

1.3.5 Algorithmic cost modelling

• Cost is estimated as a mathematical function of product, project and process attributes whose values are estimated by project managers
• The function is derived from a study of historical costing data
• Most commonly used product attribute for cost estimation is LOC (code size)
• Most models are basically similar but with different attribute values

1.3.5.0 The COCOMO model  

• Developed at TRW, a US defense contractor
• Based on a cost database of more than 60 different projects
• Exists in three stages
  • Basic - Gives a 'ball-park' estimate based on product attributes
  • Intermediate - modifies basic estimate using project and process attributes
  • Advanced - Estimates project phases and parts separately

1.3.5.0 Project classes  

• Organic mode small teams, familiar environment, well-understood applications, no difficult non-functional requirements (EASY)
• Semi-detached mode Project team may have experience mixture, system may have more significant non-functional constraints, organization may have less familiarity with application (MODERATE)
• Embedded Hardware/software systems, tight constraints, unusual for team to have deep application experience (HARD)

1.3.5.0 Basic COCOMO Formula  

• Organic mode: PM = 2.4(KDSI )^1.05
• Semi-detached mode: PM = 3(KDSI )^1.12
• Embedded mode: PM = 3.6(KDSI )^1.2

KDSI: thousands of delivered source instructions
M: project complexity multipler -- initially 1.0
PM: person-months

1.3.5.0 Effort estimates  

1.3.5.0 COCOMO -- Time to Develop  

• Organic mode: TDEV = 2.5(PM )^0.38
• Semi-detached mode: TDEV = 2.5(PM )^0.35
• Embedded mode: TDEV = 2.5(PM )^0.32

TDEV: time (months) to develop

Note that TDEV does not depend on number of people assigned.

1.3.5.0 COCOMO examples  

• Organic mode project, 32KLOC
  • PM = 2.4(32)^1.05 = 91 person months
  • TDEV = 2.5(91)^0.38 = 14 months
  • N = 91/14 = 6.5 people

• Embedded mode project, 128KLOC
  • PM = 3.6(128)1.2 = 1216 person-months
  • TDEV = 2.5(1216)0.32 = 24 months
  • N = 1216/24 = 51 peoples

1.3.5.0 COCOMO assumptions  

• Implicit productivity estimate
  • Organic mode = 16 LOC/day
  • Embedded mode = 4 LOC/day

• Time required is a function of total effort NOT team size
• Not clear how to adapt model to personnel availability

1.3.5.0 Intermediate COCOMO  

• Takes basic COCOMO as starting point
• Identifies personnel, product, computer and project attributes which affect cost
• Multiplies basic cost by attribute multipliers which may increase or decrease costs

1.3.5.0 Personnel attributes  

• Personnel attributes
  • Analyst capability
  • Virtual machine experience
  • Programmer capability
  • Programming language experience
  • Application experience

• Product attributes
  • Reliability requirement
  • Database size
  • Product complexity

1.3.5.0 Computer attributes  

• Computer attributes
  • Execution time constraints
  • Storage constraints
  • Virtual machine volatility
  • Computer turnaround time

• Project attributes
  • Modern programming practices
  • Software tools
  • Required development schedule

1.3.5.0 Attribute choice  

• These are attributes which were found to be significant in one organization with a limited size of project history database
• Other attributes may be more significant for other projects
• Each organization must identify its own attributes and associated multiplier values

1.3.5.0 Model tuning  

• All numbers in cost model are organization specific. The parameters of the model must be modified to adapt it to local needs
• A statistically significant database of detailed cost information is necessary
  • Fitting to database used to set attribute multipliers
    • [SJZ: In practice, fitting to such a large number of inter-dependent variables is unreliable.]

  • Different attributes may be combined into aggregate multipliers.

1.3.5.0 Example  

• Embedded software system on microcomputer hardware.
• Basic COCOMO predicts a 45 person-month effort requirement
• Attributes = RELY (1.15), STOR (1.21), TIME (1.10), TOOL (1.10)
• Intermediate COCOMO predicts
  • 45 × 1.15 × 1.21 × 1.10 × 1.10 = 76 person-months.

• Total cost = 76 × $7000 = $532, 000

1.3.5.0 Project planning example  

• Algorithmic cost models provide a basis for project planning as they allow alternative strategies to be compared.
• Alternative 1: Use more powerful hardware to reduce TIME and STOR attribute multipliers
• Alternative 2: Invest in support environment

1.3.5.0 Alternative 1: Hardware investment  

• Processor capacity and store doubled
  • TIME and STOR multipliers = 1

• Extra investment of $30, 000 required
• Fewer tools available
  • TOOL = 1.15

• Total cost = 45 × 1.24 × 1.15 × $7000 = $449, 190
• Cost saving = $83, 000

1.3.5.0 Alternative 2: Environment investment  

• In addition to hardware costs
• Reduces turnaround, tool multipliers. Increases experience multiplier
• C = 45 × 0.91 × 0.87 × 1.1 × 1.15 × 7000 = $315, 472
• Saving from investment = $133, 718

1.3.5.0 Cost Estimation -- Summary  

• Estimate the project cost to the supplier then decide on the price to the customer
• Factors affecting productivity include individual aptitude, domain experience, the development project, the project size, tool support and the working environment
• Prepare cost estimates using different techniques. Estimates should be comparable

1.3.5.0 Summary (cont.)  

• Algorithmic cost estimation is difficult because of the need to estimate attributes of the finished product
• Algorithmic cost models are a useful aid to project managers as a means of comparing different development options
• The time required to complete a project is not simply proportional to the number of people working on the project