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Training on Cost Estimation & Analysis

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1 Training on Cost Estimation & Analysis
Karen Richey Jennifer Echard Madhav Panwar

2 Outline Introduction to Cost Estimating Life Cycle Costs
Data Collection Data Analysis Cost Estimating Methodologies Software Cost Modeling Cross-checks and Validation Risk and Sensitivity Analysis Documentation Requirements Cost Estimating Challenges Cost Estimating Auditor Checklists

3 Introduction to Cost Estimating
The National Estimating Society has defined Cost Estimating1as: The art of approximating the probable cost of something based on information available at the time. Cost estimating cannot: Be applied with cookbook precision, but must be tailored to a particular system, Substitute for sound judgment, management, or control, Produce results that are better than input data, or Make the final decisions. Despite these limitations, cost estimating is a powerful tool because it: Leads to a better understanding of the problem, Improves management insight into resource allocation problems, and Provides an objective baseline to measure progress. 1 The NES Dictionary, National Estimating Society, July 1982

4 Introduction to Cost Estimating
The reliability of cost estimates varies over time. The closer you get to the actual completion of a project, the estimate becomes more accurate. Four types of cost estimates represent various levels of reliability . Conceptual Estimate: Rough order of magnitude or back of the envelope. Often inaccurate because there are too many unknowns. Preliminary Estimate: Used to develop initial budget, more precise. Detailed Estimate: Serves as a basis for daily project control. Definitive Estimate: Accuracy should be within 10% of final cost. Important to repeat estimating process (i.e., re-estimate) on a regular basis as more information becomes available This will keep estimate current as well as increase the accuracy

5 Introduction to Cost Estimating (cont’d)
All cost estimates are constructed by the following tasks: Identifying the purpose and scope of the new system. New software development, software reuse, COTS integration, etc. Choosing an estimate type. Conceptual, preliminary, detailed, or definitive type estimate Identifying system performance and/or technical goals. Laying out a program schedule. Selecting a cost element structure (CES). Collecting, evaluating, and verifying data. Choosing, applying, cross-checking estimating methods to develop the cost estimate. Performing risk and sensitivity analysis Time-phasing the cost estimate by fiscal year for cash flow purposes. Example: 4 years to develop and 10 years operations and support beginning in FY2003 Providing full documentation.

6 Life Cycle Costs Most cost estimates in the federal government represent total Life Cycle Costs (LCC). LCC estimates include all costs to develop, produce, operate, support, and dispose of a new system. Important to look beyond the immediate cost of developing and producing a system and consider all costs of a system’s life cycle. What may appear to be an expensive alternative among competing systems may be the least expensive to operate and support Life Cycle Cost Estimates can be used to: Compare various alternatives before committing funds to a project, Support “Estimate-to-Budget” transition after time-phasing to account for when funds will be spent.

7 Life Cycle Costs (cont’d)
A LCC estimate is summarized using a detailed cost element structure (CES) that Identifies the activities required to complete project development and the effort, loading, and duration of each task, Provides a framework against which the cost estimate is organized. Enhances cost data collection and estimate reporting. Facilitates comparing estimates when a standard CES is used.

8 Life Cycle Costs Cost Element Structure (CES)
A CES provides a standard vocabulary for the identification and classification of cost elements to be used in cost estimating. Helps to identify costs that may be initially overlooked. Should be tailored for each project. The CES should be reviewed to ensure that there is no ‘double counting’ of costs that could be allocated to more than one element. For example, logistics support costs could be included in the investment or operations and support phase. The CES is hierarchical in nature to accommodate early development (when relatively little data is available) through deployment, when more detailed data is available.

9 Life Cycle Costs Example of CES Elements (DOD)
1.0 Investment Phase System Operations & Support Phase 1.1 Program Management System Management 1.2 Concept Exploration Annual Operations (supplies/spares) 1.3 System Development Hardware Maintenance 1.3.1 System Design & Specification 2.4 Software Maintenance 1.3.2 Development Prototype and Test Site Investment 2.5 Outsource Provider Support Software Development Data Maintenance 1.3.4Training Site Operations (personnel, training,etc.) 1.3.6 Facilities 1.4 System Procurement 1.4.1 Deployment Hardware 1.4.2 Deployment Software 1.4.3 Initial Documentation 1.4.4Logistics Support Equipment 1.4.5 Initial Spares 1.4.6 Warranties 1.5 Outsource Provider Investment 1.6 System Initiation, Implementation & Fielding 1.7 Upgrade (Pre-Planned Product Improvement (P3I)) 1.8 Disposal Costs

10 Data Collection Data Required
All CES elements will need data to support the estimate Historical cost and non-cost data need to be collected to support various estimating techniques Technical non-cost data describes the physical, performance, and engineering characteristics of a system Examples: weight, # of design drawings, SLOC, function points, # of integrated circuit boards, square footage, etc. Important to pick data that is a predictor of future cost Important to have technical and schedule data because they act as cost drivers

11 Data Collection Data Required(cont’d)
Identify both direct and indirect costs Direct costs are called “touch labor” and include direct manufacturing, engineering, quality assurance, material, etc. costs which have a direct bearing on the production of a good. Also included are direct non-wage costs such as training, supplies, and travel. Indirect costs are considered “overhead” and include such things as general & administrative support, rent, utilities, insurance, network charges, and fringe benefits. These expenses are typically charged to a company as a whole. Example: sick/annual leave, retirement pay, health insurance, etc.

12 Data Collection Data Required (cont’d)
Some direct costs may be burdened with indirect costs and some may not Need to know to avoid double-counting or, worse yet, underestimating Important to ask when collecting data whether costs are burdened with indirect costs

13 Data Collection Data Required (cont’d)
Data can be collected in a variety of ways Contractor site visits Data requests for all relevant CES elements Documented cost estimates, if available for earlier versions of the current system Can save valuable research time for statistical analysis Published cost studies Data collection is a critical and time consuming step in the cost estimating process!

14 Data Collection Typical data sources
Two types of data sources: Primary & Secondary Primary data is found at the original source (e.g., contractor reports, actual program data, etc.) Preferred source of data Secondary data is derived from primary data of another similar system such as documented cost estimates, cost studies/research, proposal data, etc. Second choice to primary data due to data gaps May be best alternative when time constraints or data availability limit primary data collection

15 Data Collection Typical data sources
Current Program Milestone schedule (for phasing program costs over time) Current Program System Description Important to have a technical understanding of how the system will work Approved program funding (Compare to proposal estimate) Contractor actual cost reports (from internal Management Information System) Current program estimate documentation (if available) Contractor proposals (Compare to program funding profile) Commercial Off-the-Shelf (COTS) catalogs Manufacturer websites (e.g., Dell, Microsoft, etc.)

16 Data Collection Typical Data Sources (cont’d)
Forward Pricing Rate Agreements (FPRA) Similar program historical actual costs and estimate documentation Engineering drawings/specifications, Interviews with technical and program management personnel Surveys Professional journals and publications Industry guides and standards Technical Manuals

17 Data Analysis Data Validity/Integrity
Important to ensure that the cost data collected is applicable to the estimate. Identifying limitations in the historical data is imperative for capturing uncertainty When using historical cost data for a similar system, appropriate adjustments need to be made to account for differences in the new system being estimated. Data may need to be “mapped” from the contractor’s accounting system to the Cost Element Structure (CES)

18 Data Analysis Data Validity/Integrity (cont’d)
Proposal data should be validated to ensure that contractor motivations to “buy-in” or low-bid their estimates are not occurring. Compare previous contractor proposal bids and actual costs for similar programs. Look for trends in underbidding. Participate in a fact-finding trip to discuss contractor proposal estimates and gather supporting data/evidence.

19 Data Analysis Normalization
Involves making adjustments to the data so that it can account for differences in Inflation rates, Direct/indirect costs, Recurring and non-recurring costs, Production rate changes or breaks in production, Anomalies such as strikes, major test failures, or natural disasters causing data to fluctuate, and Learning curve (cost improvement) effects due to efficiencies gained from continually repeating a process Analysis of the data may indicate the need for more suitable data to add credibility to the estimate.

20 Data Analysis Normalization (cont’d)
Accounting for Economic Changes (e.g., inflation) Lack of cost data uniformity due to upward movement in prices and services over time. Index numbers are used to deflate or inflate prices to facilitate comparison analysis. Wrong to compare costs from 1980’s to today without accounting for inflation over the past 20 years. Cost estimators use inflation indices to convert costs to a constant year dollar basis to eliminate distortion that would otherwise be caused by price-level changes. Constant dollar estimates represent the cost of the resources required to meet each year’s workload using resource prices from one reference year (e.g., constant 2003 dollars). Constant dollars reflect the reference year prices for all time periods allowing analysts to determine the true cost of changes for an item

21 Data Analysis Normalization (cont’d)
For the United States, the Office of Management and Budget (OMB) is responsible for developing inflation guidance by appropriation for government estimates. Most common indices used by United States Cost Estimators are published by the U.S. Department of Labor, Bureau of Labor Statistics. Producers Price Index ( for goods Employment and Earnings Index ( for services Important to pick the appropriate “market basket” of goods index that most closely matches the costs to be estimated.

22 Data Analysis Normalization (cont’d)
For budgetary purposes, however, data expressed in constant dollars should be inflated to represent current year (or “Then-year”) dollars. Current year estimates calculate the cost of the resources using the estimated prices for the year in which the resources will be purchased. Current year estimates reflect inflation Necessary to express estimates in current year dollars when requesting funding to avoid budget shortfalls.

23 Data Analysis Normalization (cont’d)
Example: Assume 5% escalation rate compounded annually Base-year cost Then-year cost Current year $273,100 * multiplier = $ ,100 Current year $1,911,700 * multiplier = $ 2,007,285 Current year $4,096,500 * multiplier = $ 4,516,391 Current year $8,193,000 * multiplier = $ 9,484,217 Current year $6,144,750 * multiplier = $ 7,468,944 Current year $1,092,400 * multiplier = $ 1,394,230 Total Estimated Labor cost (budget quality) $25,144,167

24 Cost Estimating Methodologies
Once data has been collected and normalized to constant dollars, there are five methodologies available for estimating costs: Expert Opinion, Analogy, Parametric, Engineering, and Actual.

25 Estimating Methodology Considerations
Choice of methodology is dependent upon Type of system Software, hardware, etc Phase of program Development, Production, Support Available data Historical data points from earlier system versions or similar system Technical parameters of system

26 Methodology Expert Opinion
Often called Delphi method, proposed by Dr. Barry Boehm in his book, Software Engineering Economics. Useful in assessing differences between past projects and new ones for which no historical precedent exists.

27 Methodology Expert Opinion (cont’d)
Pros: Little or no historical data needed. Suitable for new or unique projects. Cons: Very subjective. Experts may introduce bias. Larger number of experts will help to reduce bias Qualification of experts may be questioned.

28 Methodologies Expert Opinion - Steps
Gather a group of experts together, Describe overall program in enough detail so experts can provide an estimate, Each member of the expert group then does an independent of the resources needed, Estimates are gathered anonymously and compared, If there exists significant divergence among the estimates, the estimates will be returned to the expert group, The expert group then discusses the estimates and the divergence and works to resolve differences, and The expert group once again submits anonymous, independent estimates which continues until a stable estimate results.

29 Methodology Expert Opinion - Example
Three software engineers are renown in the ERP software development. You hold interviews which each explaining the requirements, sizing level, and development process for your new system. Each member of the group submits their opinion of the final cost and the estimate converges to $50M.

30 Methodologies Analogy
Estimates costs by comparing proposed programs with similar, previously completed programs for which historical data is available. Actual costs of similar existing system are adjusted for complexity, technical, or physical differences to derive new cost estimates Analogies are used early in a program cycle when there is insufficient actual cost data to use as a detailed approach Compares similarities and differences Focus is on main cost drivers.

31 Methodologies Analogy (cont’d)
Often use single historical data point. May have several analogy estimates of sub elements to make up the total cost. Comparison process is key to success. May have to add or delete functionality from historical costs to match new program Consult technical experts for advice on complexity factors, technical, performance or physical differences. (not to be confused with expert opinion method) Impact of differences on cost is subjective.

32 Methodologies Analogy (cont’d)
Good choice for: A new system that is derived from an existing subsystem. Make sure actual cost data is available A system where technology/programmatic assumptions have advanced beyond any existing cost estimating relationships (CER). Secondary methodology/cross check Provides link between technical assumptions and cost.

33 Methodologies Analogy (cont’d)
Pros: Inexpensive Easily changed Based on actual experience (of the analogous system) Cons: Very Subjective Large amount of uncertainty Truly similar projects must exist and can be hard to find Must have detailed technical knowledge of program and analogous system

34 Methodologies Analogy - Steps
Determine estimate needs/ground rules, Define new system, Breakout new system into subcomponents for analogy estimating, Assess data availability of historical similar systems, Collect historical system component technical and cost data, Process/normalize data into constant year dollars (e.g., constant FY2003$), Develop factors based on prior system, Example: Program Management is 10% of total development cost Develop new system component costs. Obtain complexity (and other translation) factors Example: Historical database is for 4 million records and new database will need to house 24 million records => complexity factor of 6 (4*6 = 24) times the historical database cost Apply factors to historical costs to obtain new system costs * Pg 9-9 AF handbook

35 Methodologies Analogy - Example
Your company is developing a new IT payroll system serving 5,000 people and containing 100,000 lines of C++ code. Another company developed a similar 100,000 lines of code system for $20M for only 2,000 people. Your software engineers tell you that the new system is 25% more complicated than the other system. Other system development cost was $20M. Estimated cost for new system = 1.25 * $20M = $25M Plus 5,000/2,000, or 2.5 * $25M = $62.5M total cost

36 Methodologies Parametric
Utilizes statistical techniques called Cost Estimating Relationships (CER). Relates a dependent variable (cost) to one or more independent variables Based on specific factors that have a high correlation to total cost Number of software lines of code (SLOC) or function points, Square feet for office floor space, Number of floors in a high rise building for cabling estimates, Database size, etc. Can be used prior to development. Typically employed at a higher CES level as details are not known. Most cases will require in-house development of CER.

37 Methodologies Parametric (cont’d)
Pros: Can be excellent predictors when implemented correctly Once created, CERs are fast and simple to use Easily changed Useful early on in a program Objective Cons: Often lack of data on software intensive systems for statistically significant CER Does not provide access to subtle changes Top level; lower level may be not visible Need to be properly validated and relevant to system

38 Methodologies Parametric Example
You have a previously developed CER to estimate a new IT system based on SLOC. Cost = SLOC * 25 $/SLOC The CER is based on systems ranging from 1,000,000 to 3,000,000 SLOC. You have estimated 2,600,000 SLOC for new system Cost = 2,600,000 * $25 = $65M

39 Methodologies Parametric (cont’d)
Cost estimators can develop their own CERs or they can use existing commercial cost models. Various Software cost estimating models will be discussed next Learning curves – specialized type of CER. CERs can be cost to cost or cost to non-cost. Cost to Cost: e.g., Manufacturing costs are 1.5 times Quality Assurance costs Cost to Non-Cost: $/pound, or engineering hours/# of engineering drawings yields hours/drawing metric that can be applied to new program Factors and ratios are also examples of parametric estimating.

40 Methodologies Parametric CERs
CERs are defined as a technique used to estimate a particular cost by using an established relationship with an independent variable. Can be as simple as a one variable ratio to a multi-variable equation. CERs use quantitative techniques to develop a mathematical relationship between an independent variable and a specific cost. * Parametric Estimating Handbook pg 1-1

41 Methodologies Parametric CERs (cont’d)
Reliable, normalized data is most important for CER development. Must determine range of data for which the CER is valid. Useful at any stage in a program. Typically CERs are the main cost estimating methodology in early stages of a program. In later stages of a program, CERs serve as a cross check to other methods Must be logically sound as well as statistically sound. High correlation (r2 = 0.75 or higher) for goodness of fit test Different statistical techniques may be used to judge the quality of the CER. Least squares best fit (regression analysis, or the ability to predict one variable on the basis of the knowledge of another variable) Multiple regression (a change in the dependent variable can be explained by more than one independent variable) Curvilinear regression (relationship between dependent and independent variable is not liner, but based on a curve) Learning curve (describe how costs decrease as the quantity of an item increases)

42 Methodologies Parametric CERs (cont’d)
Statistics may be used to evaluate how well the CER will produce a reliable estimate. Coefficient of determination, R2: Percent of the variation in the Y-data explained by the X-data, (ie. How close the points are to the line) Standard Error, SE: Average estimating error when using the equation as the estimating rule Coefficient of Variation, CV: SE divided by mean of the Y-data, relative measure of estimating error t-stat Tests whether the individual X-variable(s) is/are valid F-stat Tests whether the entire equation, as a whole, is valid No single statistic may validate or invalidate a CER, quality of the input data is just as important. Best to use a statistical software package like SAS or SPSS to quickly evaluate alternative CERs.

43 Methodologies Parametric CERs - Steps
Define the dependent variable (e.g., cost dollars, hours, etc.) and what the CER will estimate, Select independent variables to be tested for developing estimates of the dependent variable, Variables should be quantitatively measurable and available If there is a choice between developing a CER based on performance or physical characteristics, performance characteristics are generally the better choice because they are known early on Collect data concerning the relationship between the dependent and independent variables, Most difficult and time consuming step, but essential that all data is checked to ensure that all observations are relevant, comparable, and free of unusual costs Explore the relationship between the dependent and independent variables, Use statistical analysis to judge strength of relationship and validity of equation Select the relationship that best predicts the dependent variable, and A high correlation often indicates that the independent variable will be a good predictive tool Document your findings. Identify independent variables tested, data gathered along with sources, time period (normalization for inflation effects), and any adjustments made to the data

44 Methodologies Parametric Learning Curves
Basic premise: Repetitive tasks should result in productivity for subsequent, similar tasks This improvement is usually quantified at a rate Y = AXb In simplest terms, the cost of manufacturing or installing a unit should decrease as the number of units involved increases. As the number of units produced doubles, the cost per unit decreases by a fixed percentage The concept of learning curves is not new, originated in the mid-1930’s with T.P. Wright in the Journal of Aeronautical Sciences.

45 Methodologies Parametric Learning Curve - Example
Say that the first 100 tasks of an installation took 10 hours per task and the next 100 averaged 8 hours per task. Thus, the learning curve would be calculated as follows: Learning curve = 8 hours per task/10 hours per task = 0.8 Implies an 80% learning curve meaning an improvement of 20% occurred between the first 100 tasks and next 200 tasks

46 Methodologies Rate Impact
Basic premise: The number of units produced in a single lot effects the overall cost of producing that lot. Costco theory that buying in bulk makes the unit cost less Mathematical equation showing rate impact along with learning curve Y = AXbQr Both rate and learning curves can be impacted by the following: Operator turnover rate (new employees do not meet expected productivity standards immediately) Production reworks Material handling and downtime (learning curves assume material is ready when needed) Engineering rework Rework of vendor supplied parts

47 Methodologies Engineering
Also referred to as bottoms up or detailed method. Underlying assumption is that future costs for a system can be predicted with a great deal of accuracy from historical costs of that system. Involves examining separate work segments in detail and then synthesizing these detailed estimates along with any integration costs into a total program estimate. Estimate is built up from the lowest level of system costs. Uses detailed cost element structure (CES). Must include all components and functions. Can be used during development and production.

48 Methodologies Engineering (cont’d)
Most useful for systems in production. Design configuration has stabilized Test results are available Development cost actuals are available Typically broken into functional labor categories e.g. engineering, manufacturing, quality control, etc.

49 Methodologies Engineering
Pros: Objective Reduced uncertainty Cons: Expensive Time Consuming Not useful early on May leave out software integration efforts

50 Methodologies Engineering Steps
Understand program requirements, Prepare program baseline definition, Define ground rules and assumptions, Develop detailed cost element structure, Develop functional estimates, and Use other program history Compile estimate data Develop rates and factors Incorporate supplier/subcontractor prices Include integration costs to “glue” the separate components into an integrated system (may need to use a CER for this estimate) Summarize estimate.

51 Methodologies Engineering Example
New IT system can be broken down into Labor and Material costs. Labor: Estimated number of hours 250,000 hrs * $200/hr = $50M Material: From vendor quotes Server = $10M COTS = $3M Desktops $100k) = $5M Cost = $50M+$10M+$3M+$5M = $68M

52 Methodologies Actual Bases future costs on recent historical costs of same system. Used later in development or production. Preferred method. Costs are calibrated to actual development or production productivity for your organization

53 Methodologies Actual Pros: Cons: Most accurate
Most objective of the five methodologies Cons: Data not available early on Time consuming Labor intensive to collect all the data necessary

54 Methodologies Actual - Example
The development process is nearing completion. The material has all been procured at a cost of $20M. The labor cost to date is $30M. According to earned value cost performance reports (CPRs) the estimate at complete for the remainder of the labor is another $20M. Cost = $20M + $30M + $20M = $70M

55 Methodologies Summary of the Five Cost Estimate Results
Expert - $50M Analogy - $62M Parametric – $65M Engineering – $68M Actual - $70M

56 Software Cost Models Software costs as a percentage of total system costs continues to increase while associated hardware costs decrease. Accurately capturing software costs can be difficult and cost overruns often occur as a result of software requirements being difficult to estimate and track. Software estimating problems occur most often because of the: Inability to accurately size a software project, Inability to accurately specify a software development and support environment, Improper assessment of staffing levels and skills, and Lack of well-defined requirements for the specific software activity being estimated

57 Software Cost Models (cont’d)
The requirement to develop cost estimates for systems at a time when few details are known has led to the development of cost models. The need to generate estimates quickly and for many alternatives can make the analogy and engineering methods impractical due to lack of detailed requirements. In the early stages of software development, few details other than performance requirements and some physical constraints are available. Software lines of code (SLOC) are often used to estimate software size which is the most significant driver in determining software costs. Programming languages also have a significant effect on overall cost.

58 Software Cost Models (cont’d)
Software cost models are based on statistically derived cost estimating relationships (CERs) and various estimating methodologies used to predict the cost of a system. Models approximate the real world through a combination of Statistical analysis of historical data, Informal/Intuitive analysis of rules of thumb based on experience, Standard procedures such as learning curves, inflation computation, and labor rate/overhead applications. Models may contain the following features: Support costs calculations, time-phasing to spread development costs by fiscal year, & inflation calculations to convert base year to then-year dollars.

59 Software Cost Models (cont’d)
Regardless of how software is programmed, it must proceed through certain steps or phases and must be supported after development. The combination of software development and support is known as the software life cycle. The Cost Element Structure (CES) discussed earlier accounts for the software life cycle using the following sub-elements for CES 1.3 Software Development: Requirements Definition Analysis, Design, and Integration Quality Assurance Program Configuration Management Software Design and Development HW/SW Integration, Assembly, Test and Checkout System Operational Test and Evaluation System Independent Software Verification and Validation Site Acceptance Testing Independent Operational Test and Evaluation Program Management Installation and Checkout Corrective Maintenance

60 Software Cost Models (cont’d) Examples of Commercially Available Models

61 Software Cost Models (cont’d) COCOMO
Constructive Cost Model (COCOMO) is one of the earliest cost models widely used by the cost estimating community. COCOMO was originally published in Software Engineering Economics by Dr. Barry Boehm in 1981. COCOMO is a regression-based model that considers various historical programs software size and multipliers.

62 Software Cost Models (cont’d) COCOMO
COCOMO’s most fundamental calculation is the use of the Effort Equation to estimate the number of Person-Months required to develop a project. Example: # of person months * loaded labor rate = Estimated Cost Most of the other estimates (requirements, maintenance, etc) are derived from this quantity.

63 Software Cost Models (cont’d) COCOMO
COCOMO requires as input the project’s estimated size in Source Lines of Code (SLOC). In addition to SLOC, COCOMO has ‘scale factors’ which allow you to tailor conditions to your project. Scale factors are used to calibrate or adjust the model that is based on data which is not necessarily representative of the system being estimated Intent of calibrating is to allow for general application of a model developed from a specific database Based on the assumption that any differences between the predicted cost and the historical cost not explained by the model are due to: Differences in the type of systems and acquisition environment represented in the two data sets

64 Software Cost Models (cont’d) COCOMO
Scale factors include: Development flexibility, architecture, risk, team cohesion, and process maturity among others. Additionally COCOMO uses ‘Cost Drivers’ grouped in broad categories such as personnel, product, platform, project etc. Product Cost drivers include: required reliability and product complexity. Personnel cost drivers include: language, platform, and tool experience

65 Software Cost Models (cont’d) COCOMO
COCOMO defaults to a nominal value for all factors when model is first used. Cost estimators must calibrate the model to the program being estimated to their specific development environment and productivity level of staff. COCOMO uses analogy and parametric methods based pm a historical, proprietary database of data submitted by consortium members. Allows COCOMO to be compatible with current design methods and evolving higher order languages.

66 Software Cost Models (cont’d) COCOMO
The model makes its estimates of required effort (measured in Person-Months) based primarily on the project’s estimate of the software size (as measured in thousands of SLOC, KSLOC)): Effort = 2.94 * EAF * (KSLOC)**E Where EAF is the Effort Adjustment Factor derived from the Cost Drivers and E is an exponent derived from the five Scale Drivers.

67 Software Cost Models (cont’d) COCOMO
For example assuming a project with all nominal cost and scale drivers, EAF would have a value of and exponent, E, of Assuming that the project consists of 15,000 source lines of code, COCOMO estimates that Person-Months of effort is required to complete it. Effort = 2.94 * (1.0) * (15)** = Person-Months. Person-months multiplied by a loaded labor rate (includes direct and overhead costs) would yield the cost estimate: 57.77 person-months * $15K per person-month = $866,000

68 Software Cost Models (cont’d) COCOMO Model output example

69 Software Cost Models (cont’d) COCOMO
Example of how model defaults to nominal scale factors. These should be adjusted for every estimate to approximate the new, unique development effort.

70 Software Cost Models (cont’d) COCOMO outputs by phase

71 Software Cost Models (cont’d) COCOMO outputs by phase

72 Software Cost Models (cont’d) COCOMO overall estimate by phase

73 Software Cost Models (cont’d) COCOMO Summary
COCOMO provides the following information: Software development, maintenance cost and schedule estimates. Cost, schedule distribution by phase, activity, and increment. A free updated version of COCOMO is available at:

74 Software Cost Models (cont’d)

75 Software Cost Models (cont’d) CostXpert
CostXpert builds on COCOMO by having a database of actual projects that is constantly being updated for various types of systems (commercial, military, etc.). When you enter project specific information, CostXpert maps it to the closest projects in its database and gives you results tailored to your project. CostXpert uses both parametric and analogy estimating methods. CostXpert can accommodate a variety of software sizing elements such as SLOC, function points, object metrics, GUI metrics, and feature points.

76 Software Cost Models (cont’d) CostXpert
CostXpert also allows for customization specifically to your work environment. In addition, CostXpert creates a detailed cost element structure (CES), conducts ‘what if’ scenarios, risk analysis, and provides a wide variety of reports to include: Cost allocations by task list, Labor loading by phase and functional categories, Maintenance and life-cycle costs, Software defects estimates, and Documentation deliverables GAO uses CostXpert during various cost analysis assessments to check the reasonableness of both the software cost estimate and accompanying schedule for delivery. Evaluation copies can be downloaded at

77 Software Cost Models (cont’d) CostXpert example of input screen

78 Software Cost Models (cont’d)

79 Software Cost Models (cont’d) SLIM
The Software Life Cycle Model (SLIM) is marketed by Quantitative Software (QSM). Set of tools include SLIM-Estimator, SLIM-Data Manager, SLIM-Control, SLIM-Metrics and SLIM-Master Plan. Input metrics include SLOC, Function Points, Objects, Windows, Screens, or Diagrams Historical productivity and complexity are also inputs to the model Quick start wizard enable rapid generation of estimates and details which can then be tailored for the specific project.

80 Software Cost Models (cont’d) SLIM
Results are presented at the 50% probability and can be adjusted by using ‘sliders’ for various parameters. Estimates can readily be compared to historical data and model allows for analysis of ‘what if’ scenarios. A Productivity Index (PI) is calculated for your project and updates as new data is available.

81 Software Cost Models (cont’d) SLIM
As described in the user’s manual, the PI is a macro measure of the total development environment. It incorporates many factors in software development, including: Management influence, development methods, tool, techniques, skill and experience. Values for PI range from .1 to 40. Low values generally are associated with poor environments, process, tools and complex systems. High values are associated with mature processes, and capable management and well-understood, straightforward projects. Outputs of the model include: project description, estimation analysis views, schedule, risk analysis, staffing and skill breakout, effort and cost, reliability estimates, and documentation sections. Demo copies of SLIM can be downloaded at .

82 Software Cost Models (cont’d)

83 Software Cost Models (cont’d) SEER
This set of tools provides both software and hardware estimation capabilities. Software Project Control Software Estimation, Planning and Project Control Hardware Project Control Hardware Estimation, Planning, Project Control and Life-Cycle Cost Analysis, Including Operations and Support Design for Manufacturability Cost Designer for Parts, Process and Assembly

84 Software Cost Models (cont’d) SEER
The toolset includes: SEER-SEM for evaluating software development. SEER-H for estimating hardware and hardware Operations and Support costs. SEER-DFM for manufacturability analysis. SEER-IC for estimating foundry costs. SEER-SSM for software sizing. Outputs of the model include Labor estimates by function, A “quick estimate” providing a snapshot of size, effort, and schedule, and Staffing by month, cost by activity, person-months by activity, delivered defects, and SEI maturity rating. Demo copies of SER-SEM and other tools can be downloaded at

85 Software Cost Models (cont’d)

86 Software Cost Models (cont’d) REVIC
The Revised Enhanced Version of Intermediate COCOMO (REVIC) model was developed by Mr. Raymond L. Kile formerly of Hughes Aerospace. The main difference between REVIC and COCOMO is the coefficients used in the effort equations. REVIC changed the coefficients based on a database of recently completed DOD projects. REVIC's schedule estimates are often considered lengthy because it assumes that a project's documentation and reviews comply with the full requirements of DOD-STD-2167A/498.

87 Software Cost Models (cont’d)

88 Software Cost Models (cont’d) Costar, Price S, etc.
There are many other models available. Most are based on COCOMO. Almost all need to be calibrated with project specific data. Using multiple models and comparing results provides better confidence. Crosschecking estimates leads to better cost estimate validation The International Council on Systems Engineering (INCOSE) has a list of software tools that may be of use.

89 Cross-checks and Validation
After an estimate has been created, the next step involves validating the estimate by cross-checking. Cross-checking means using a different approach to create the estimate. If both estimates are close, the target estimate has some validity. If both estimates are very different. This increases the level of uncertainty which must be reflected in a risk analysis. This may lead to another estimating method to increase cost estimate confidence. It is a good practice to cross-check major cost drivers. If time is available, cross-checking other cost elements can further validate the entire estimate. Factors tend to be the most common approach for top level checks. Software cost models play an important role during later stages of development phase. Can be used to cross-check the reasonableness of costs forecasted using either the engineering or actual cost methods.

90 Cross-checks and Validation (cont’d)
Validation is the process of demonstrating that either a CER or a cost model accurately predicts past history. Validation also includes a demonstration that: The data relationships are logical, The data used are credible, Strong data relationships exist, and The CER or cost model is a good predictor of costs. This can be done using independent test data (not included in model’s calibration). The model’s cost output would then be compared to the test point’s “known value” to provide an accuracy benchmark.

91 Cross-checks and Validation (cont’d)
Validation also includes a demonstration that: Model users have sufficient experience and training, Calibration processes are thoroughly documented, Formal estimating policies and procedures are established, and When applicable, information system controls are maintained to ensure the integrity of the models being used.

92 Risk and Sensitivity Analysis Risk Analysis Definition
Risk analysis is a process that uses qualitative and quantitative techniques for analyzing, quantifying and reducing uncertainty associated with cost goals. By nature, all cost estimates have some uncertainty. Earlier in development that uncertainty is higher. As the project matures these uncertainties decrease due to greater design definition, actual experience, and less opportunity for change. Errors can also occur from historical data inconsistencies. Cost risk analysis aims to achieve the following objectives: Identify program level confidence for development schedule, Provide credibility to the target estimate, and Identify technical, schedule, and cost estimating risk drivers for use in risk management. SEI Definition of Risk

93 Risk and Sensitivity Analysis Risk Analysis Definition (cont’d)
Risk is defined as a situation in which the outcome is subject to an uncontrollable random event stemming from a known probability distribution. Roll of two dice is an example since the roll can result in one of 11 possible outcomes. Uncertainty is defined as a situation in which the outcome is subject to an uncontrollable random event stemming from an unknown probability distribution. Example would be will it rain two weeks from today? Cost estimating falls more into the range of uncertainty than risk, but most managers use the term risk analysis. Business Case Essentials, pg 21

94 Risk and Sensitivity Analysis Sensitivity Analysis Definition (cont’d)
Sensitivity analysis answers the question: What happens if the assumptions change? Changes to some assumptions can have profound impact, while huge changes to other assumptions have little effect on results. Sensitivity examines the effect of primary cost estimating outputs to changes on individual CES inputs. Sensitivity analysis highlights the factors that have the strongest impact on the overall cost estimate. Points to management which factors deserve the most attention. Narrows down the number of lower level cost elements that should be examined using risk analysis techniques.

95 Risk and Sensitivity Analysis Sources of Cost Risks
Schedule and Technical risks Unexpected design changes Project team experience Number of business units impacted Requirements changes Integration considerations Technical difficulties or maturity issues Revised project or acquisition plans Quantity changes New labor rates Higher inflation Cost estimating risks Imprecision associated with the estimating techniques used, errors, or oversights FEA Guidebook, pg 4-10

96 Risk and Sensitivity Analysis Risk Analysis Techniques
Risk factor approach Adds cost to the overall point estimate (most likely estimate) to cover risks by way of a factor. This factor is usually a percentage derived from past data and experience. Often applied to the estimate as a whole versus lower level cost elements. Monte Carlo Simulation Automatically analyzes the effect of varying inputs on outputs of a cost model using spreadsheet risk analysis. Randomly generates values for uncertain variables over and over to simulate a model. Economic Analysis Handbook, pg 13

97 Risk and Sensitivity Analysis Risk Analysis Techniques (cont’d)
Monte Carlo Simulation Steps Analyst obtains cost distribution for each element identified as a major cost driver either through experience or sensitivity analysis. Cost distribution is usually triangular in the form of optimistic, most likely, and pessimistic cost estimates. These cost ranges are usually obtained through expert judgment (engineer or technical specialist interviews) or using a Delphi technique After all cost elements have been identified by a distribution, the simulation is run many times (1,000 – 10,000 times). Simulation calculates multiple scenarios of the cost model by repeatedly sampling values from the probability distributions assigned to the various cost elements. While the simulation runs, the forecasts stabilize towards a smooth frequency distribution called a cumulative frequency distribution or CDF After thousands of trials, statistics of the results and the certainty of any outcome can be obtained from the CDF. Business Case Essentials, pg 21. Modified example.

98 Risk and Sensitivity Analysis Risk Analysis Techniques (cont’d)
Monte Carlo Simulation Results Reveal not only the result values for each forecast, but also the probability of any value occurring. This is helpful to management because it can show the level of certainty of achieving a cost objective. For example, a simulation can show there is a 10% chance of the project finishing for $50 million, a 50% chance of it costing $ 70 million, and a 90% chance of developing the project for $100 million or less. Decision makers can use these results to decide which projects will continue funding based on quantifiable risk parameters.

99 Risk and Sensitivity Analysis Types of Risk Simulation Tools
US GAO uses Crystal Ball to perform Monte Carlo Simulations Crystal Ball is an simulation program that helps you analyze the risks and uncertainties associated with Microsoft Excel spreadsheet based models. Other tools are available, such as @Risk ( RiskEase ( Pertmaster (

100 Documentation After cross-checking the estimate major cost drivers, the next step, and most important one of all, involves documenting the entire estimate process. Although this is a difficult and time-consuming procedure, the level of detail and attention will pay big dividends when it comes time to re-estimate May also help data collection of actual analogous costs. Important to document the estimate as it is being developed (i.e., document as you go). Hard to remember rationale and judgments for adjusting data months later when it needs to be documented. Documenting as you prepare the estimate leads to a better quality estimate and requires minimum effort at the end.

101 Documentation (cont’d)
Best to provide more vs. less information Assume reader knows nothing about the system being estimated. Include step-by-step instructions for how the estimate was developed. Aim to provide enough information for the estimate to be recreated by a cost analyst independent of the team. Most users of the documentation will either be updating the estimate at a later date or will relay on data for estimating an analogous system.

102 Documentation (cont’d)
Documentation should include at a minimum: Introduction Purpose of estimate Estimate team members, POC information, and area of responsibility Program background and system description Program schedule (necessary for phasing costs over time) Scope of estimate (including what was omitted and why) Ground rules and assumptions (all technical and program specific assumptions that bound the estimate – e.g., constant 2003 dollars using xx inflation rates) Summary of estimate by year to show phasing by CES Overview of main body that describes how each CES element was estimated

103 Documentation (cont’d)
Summary of estimate by year showing phasing by CES element (In constant 2002 millions of dollars)

104 Documentation (cont’d)
Main Body documentation should include: Enough detail for each CES element that would allow for another analyst to replicate the cost estimate. Each CES element should provide a description of the methods (with rationale for choosing it), techniques, and data used to generate the estimate as well as any cross-checks done to add rigor to the estimate. Include information such as data sources, direct/indirect labor rates, labor hours, material/subcontracts, learning curve data and methodology, factors, cost estimating relationships with corresponding statistics and data ranges, cost models used, inflation indices, time phasing (e.g., development lasts 3 years and 10 years operations and support), estimator judgments, cross-checks, risk and confidence levels. Documentation flow should follow the same CES structure shown in the estimate summary by year. If the estimate is an update to a prior estimate, then provide an explanation for any differences.

105 Documentation (cont’d)
Be sure to spell out all acronyms when first introduced. Cross-reference the reader to where data is used multiple times. Show data at the lowest levels possible. Include copies of vendor quotes, studies used, statistical analysis printouts, cost model input and output reports, etc. Include disclaimers to show the level of risk and uncertainty surrounding estimate. Try to quantify the uncertainty by using a simulation model that will express the summary estimate in terms of level of confidence: Estimate is presented at the 90% confidence level, or Point estimate of $1 million is bounded by a range of $750,000 to $1,250,000

106 Cost Estimating Challenges
Access to historical data Need to invest in database capture of historical costs and technical data for proper CER development Costly, time consuming, and usually not funded Development costs for IT systems can quickly become outdated by new programming languages Maintenance costs are even more difficult to capture because they are seen as on-going support or overhead and not as metrics System architecture change effects on cost estimates can be hard to determine Validity and uncertainty of data Garbage in = Garbage out Limited time to develop estimates Can result in rough-order magnitude costs being used as budget quality estimates Cause important steps like validation and Monte Carlo simulation to be omitted Resources Lack of trained people is a problem

107 Cost Estimating Auditor Checklists
Various auditor checklists can be found in the many source documents used to create this briefing. A few of these are shown below: The Software Engineering Institute (SEI) has created "A Manager's Checklist for Validating Software Cost and Schedule Estimates." This checklist is designed to help managers access the credibility of software cost and schedule estimates. It identifies seven issues to address and questions to ask when determining your willingness to accept and use a software estimate. Each question is associated with elements of evidence that, if present, support the credibility of the estimate. Joint Industry/Government Parametric Estimating Handbook (Second Edition, Spring 1999) Chapter 6, Auditing and Evaluating a CER & Auditing Parametric Estimates Appendix G, Parametric Estimating System Checklist

108 Cost Estimating Auditor Checklists (cont’d)
Various auditor checklists can be found in the many source documents used to create this briefing. A few of these are shown below: Department of the Army Economic Analysis Manual (U.S. Army Cost and Economic Analysis Center, Feb 2001) Appendix M, Economic Analysis Checklist Department of the Army Cost Analysis Manual (U.S. Army Cost and Economic Analysis Center, May 2002) Link to a zipped MS Word file for downloading or printing Checklist on page 42, Figure 3-4 Validation Considerations TASC (The Analytic Sciences Corp.), The AFSC Cost Estimating Handbook, Reading, MA, prepared for the U.S. AirForce Systems Command, 1986. Chapter 8, Cost Models pages 8-6 through 8-12 Appendix D, Staff Review Cost Estimate Checklist pages D-1 through D-8

109 Cost Estimating Auditor Checklists (cont’d)
Various auditor checklists can be found in the many source documents used to create this briefing. A few of these are shown below: Navy Post Graduate School Economic Analysis Handbook Chapter 6, Guide for Reviewers National Research Council Canada (NRC-CNRC) Software Cost Estimation and Control (1994) Appendix A, pages 59-67, Software Cost Estimation Questionnaire

110 Contact Information For further information please contact us: Karen Richey (202) 512 – 4784 or Madhav Panwar (202) or Jennifer Echard (202) 512 – 3875 or

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