Software cost estimation

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Software Cost Estimation in Software Engineering

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  • Theres an app to estimate software project costs: http://prognostr.com
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  • 1. Appropriate to talk about s/w cost after s/w size estimation because: Both are types of estimation Methods of cost estimation often require size estimates (e.g., COCOMO)
  • No recent values but in most course taken there is the general consensus that values haven’t changed much Additional cost is significant
  • There are finite amount of resources and so may not have enough for entire desired functionality What features can be included Reduce risk by scheduling costly tasks first Give more resources to costly projects (e.g., faster computers) 2. Similar to resource allocation - Assigning the more experienced personnel to costly projects/tasks - Sometimes called manpower loading – number of engineering and management personnel allocated to a project in a given amount of time
  • 0. When is cost estimation done? - Do estimates at the beginning of a project after reqs have been clarified  could be done several times - E.g., bidding on a job, initial estimate on preliminary reqs  more detailed estimate afterwards The estimates should get progressively more accurate during the software life cycle - Helpful to have an automated method for re-estimating and collecting 2. - Appropriate level of detail is dependent upon situation - Monitoring shouldn’t be difficult otherwise it may not be done (automated, tools) 3. Of course, a company that does a lot of contract work must win bids and so the initial estimate method is very important
  • The person responsible could be a developer or manager. Called analogy-based estimation Historical information is often from memory  experienced people The analog approach is fairly common. 2. Some people believe that it is better to have the estimates done by outsiders so that there is less chance of biased estimates. - Fewer politics involved, an estimator who is a developer may have more reason to give a very optimistic estimate to please the manager (short-term) - Empirical methods will include equations - Use equations because don’t know organization and previous projects as well as someone inside the organization - No estimation model is appropriate for all classes of software, and in all development environments. Often, the estimation models need to be calibrated for local needs
  • 0. Didn’t put numbers because there is some overlap in the sequence of the main points (not completely sequential) 3. Important to use more than one method
  • The Intermediate Model estimates the software development effort by using fifteen cost driver variables besides the size variable used in Basic COCOMO. Product Attributes RELY:  Required Software Reliability The extent to which the software product must perform its intended functions satisfactorily over a period of time. DATA:  Data Base Size The degree of the total amount of data to be assembled for the data base. CPLX:  Software Product Complexity The level of complexity of the product to be developed. Computer Attributes TIME --- Execution Time Constraint The degree of the execution constraint imposed upon a software product. STOR --- Main Storage Constraint The degree of main storage constraint imposed upon a software product. VIRT --- Virtual Machine Volatility The level of the virtual machine underlying the product to be developed. TURN --- Computer Turnaround Time The level of computer response time experienced by the project team developing the product. Personnel Attributes ACAP:  Analyst Capability The level of capability of the analysts working on a software product. AEXP:  Applications Experience The level of applications experience of the project team developing the software product. PCAP:  Programmer Capability The level of capability of the programmers working on the software product. VEXP:  Virtual Machine Experience The level of virtual machine experience of the project team developing the product. LEXP:  Programming Language Experience The level of programming language experience of the project team developing the product. Project Attributes MODP:  Use of Modern Programming Practices The degree to which modern programming practices (MPPs) are used in developing software product. TOOL:  Use of Software Tools The degree to which software tools are used in developing the software product. SCED:  Schedule Constraint The level of schedule constraint imposed upon the project team developing the software product.
  • The Application Composition Model Suitable for projects built with modern GUI-builder tools. Based on new Object Points. The Early Design Model You can use this model to get rough estimates of a project's cost and duration before you've determined it's entire architecture. It uses a small set of new Cost Drivers, and new estimating equations. Based on Unadjusted Function Points or KSLOC. The Post-Architecture Model This is the most detailed COCOMO II model. You'll use it after you've developed your project's overall architecture. It has new cost drivers, new line counting rules, and new equations.
  • 9. Does COCOMO II replace traditional COCOMO? You should use COCOMO II for most of your new projects. COCOMO 81 is still the best approach for some software projects. If you're using a fairly traditional approach, and using a 3GL (third generation language), such as C, Fortran, or Cobol, the original COCOMO will give you good results.  If your development tools and processes haven't changed much in recent years, COCOMO 81 might be the right model for you. If you're using more modern tools, a new development process model, a 4GL language, or tools like Visual Basic, Delphi, or Power Builder, you might find that COCOMO II makes more sense. When in doubt, try to model your project using both the traditional COCOMO and COCOMO II.
  • How many of you see cost estimation done in your organizations? What types of experiences have you had with COCOMO?
  • Software cost estimation

    1. 1. Software Cost Estimation
    2. 2. Outline <ul><li>Introduction </li></ul><ul><li>Inputs and Outputs </li></ul><ul><li>Methods of Estimation </li></ul><ul><li>COCOMO </li></ul><ul><li>Conclusion </li></ul>
    3. 3. Cost Estimation Is Needed <ul><li>55% of projects over budget </li></ul><ul><ul><li>24 companies that developed large distributed systems (1994) </li></ul></ul><ul><li>53% of projects cost 189% more than initial estimates </li></ul><ul><ul><li>Standish Group of 8,380 projects (1994) </li></ul></ul>
    4. 4. Cost Estimation Affects <ul><li>Planning and budgeting </li></ul><ul><ul><li>Requirements prioritization </li></ul></ul><ul><ul><li>Schedule </li></ul></ul><ul><ul><li>Resource allocation </li></ul></ul><ul><li>Project management </li></ul><ul><ul><li>Personnel </li></ul></ul><ul><ul><li>Tasks </li></ul></ul>
    5. 5. Cost Estimation During the Software Life Cycle <ul><li>Cost estimation should be done throughout the software life cycle to allow for refinement </li></ul><ul><li>Need effective monitoring and control of the software costs to verify and improve accuracy of estimates </li></ul><ul><ul><li>At appropriate level of detail </li></ul></ul><ul><ul><li>Gathering data should not be difficult </li></ul></ul><ul><li>Success of a cost estimate method is not necessarily the accuracy of the initial estimates, but rather the rate at which estimates converge to the actual cost </li></ul>
    6. 6. Who is the Estimator? <ul><li>Someone responsible for the implementation </li></ul><ul><ul><li>Can compare previous projects in organization to current project </li></ul></ul><ul><ul><li>Usually experienced </li></ul></ul><ul><li>Someone from outside the organization </li></ul><ul><ul><li>Can provide unbiased estimate </li></ul></ul><ul><ul><li>Tend to use empirical methods of estimation </li></ul></ul><ul><ul><ul><li>Difficulties: </li></ul></ul></ul><ul><ul><ul><ul><li>Lack of confidence that a model will outperform an expert </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Lack of historical data to calibrate the model </li></ul></ul></ul></ul>
    7. 7. General Steps and Remarks <ul><li>Establish Plan </li></ul><ul><ul><li>What data should we gather </li></ul></ul><ul><ul><li>Why are we gathering this data </li></ul></ul><ul><ul><li>What do we hope to accomplish </li></ul></ul><ul><li>Do cost estimation for initial requirements </li></ul><ul><ul><li>Decomposition </li></ul></ul><ul><li>Use several methods </li></ul><ul><ul><li>There is no perfect technique </li></ul></ul><ul><ul><li>If get wide variances in methods, then should re-evaluate the information used to make estimates </li></ul></ul>
    8. 8. General Steps and Remarks (cont.) <ul><li>Do re-estimates during life cycle </li></ul><ul><li>Make any required changes to development </li></ul><ul><li>Do a final assessment of cost estimation at the end of the project </li></ul>
    9. 9. Software Cost Estimation Process <ul><li>Definition </li></ul><ul><ul><li>A set of techniques and procedures that is used to derive the software cost estimate </li></ul></ul><ul><li>Set of inputs to the process and then the process will use these inputs to generate the output </li></ul>
    10. 10. Inputs and Outputs to the Estimation Process <ul><li>Classical view of software estimation process [Vigder/Kark] </li></ul>
    11. 11. Inputs and Outputs to the Estimation Process (Cont.) <ul><li>Actual cost estimation process [Vigder/Kark] </li></ul>
    12. 12. Cost Estimation Accuracy <ul><li>To determine how well a cost estimation model predicts </li></ul><ul><li>Assessing model performance </li></ul><ul><ul><li>Absolute Error = ( E pred – E act ) </li></ul></ul><ul><ul><li>Percentage Error = ( E pred – E act ) / E act </li></ul></ul><ul><ul><li>Mean Magnitude of Relative Error </li></ul></ul>
    13. 13. Cost Estimation Methods <ul><li>Algorithmic (Parametric) model </li></ul><ul><li>Expert Judgment (Expertise Based) </li></ul><ul><li>Top – Down </li></ul><ul><li>Bottom – Up </li></ul><ul><li>Estimation by Analogy </li></ul><ul><li>Price to Win Estimation </li></ul>
    14. 14. Algorithmic (Parametric) Model <ul><li>Use of mathematical equations to perform software estimation </li></ul><ul><li>Equations are based on theory or historical data </li></ul><ul><li>Use input such as SLOC, number of functions to perform and other cost drivers </li></ul><ul><li>Accuracy of model can be improved by calibrating the model to the specific environment </li></ul>
    15. 15. Algorithmic (Parametric) Model (Cont.) <ul><li>Examples: </li></ul><ul><ul><li>COCOMO (COnstructive COst MOdel) </li></ul></ul><ul><ul><ul><li>Developed by Boehm in 1981 </li></ul></ul></ul><ul><ul><ul><li>Became one of the most popular and most transparent cost model </li></ul></ul></ul><ul><ul><ul><li>Mathematical model based on the data from 63 historical software project </li></ul></ul></ul><ul><ul><li>COCOMO II </li></ul></ul><ul><ul><ul><li>Published in 1995 </li></ul></ul></ul><ul><ul><ul><li>To address issue on non-sequential and rapid development process models, reengineering, reuse driven approaches, object oriented approach etc </li></ul></ul></ul><ul><ul><ul><li>Has three submodels – application composition, early design and post-architecture </li></ul></ul></ul>
    16. 16. Algorithmic (Parametric) Model (Cont.) <ul><ul><li>Putnam’s software life-cycle model (SLIM) </li></ul></ul><ul><ul><ul><li>Developed in the late 1970s </li></ul></ul></ul><ul><ul><ul><li>Based on the Putnam’s analysis of the life-cycle in terms of a so-called Rayleigh distribution of project personnel level versus time. </li></ul></ul></ul><ul><ul><ul><li>Quantitative software management developed three tools : SLIM-Estimate, SLIM-Control and SLIM-Metrics. </li></ul></ul></ul>
    17. 17. Algorithmic (Parametric) Model (Cont.) <ul><li>Advantages </li></ul><ul><ul><li>Generate repeatable estimations </li></ul></ul><ul><ul><li>Easy to modify input data </li></ul></ul><ul><ul><li>Easy to refine and customize formulas </li></ul></ul><ul><ul><li>Objectively calibrated to experience </li></ul></ul><ul><li>Disadvantages </li></ul><ul><ul><li>Unable to deal with exceptional conditions </li></ul></ul><ul><ul><li>Some experience and factors can not be quantified </li></ul></ul><ul><ul><li>Sometimes algorithms may be proprietary </li></ul></ul>
    18. 18. Expert Judgment <ul><li>Capture the knowledge and experience of the practitioners and providing estimates based upon all the projects to which the expert participated. </li></ul><ul><li>Examples </li></ul><ul><ul><li>Delphi </li></ul></ul><ul><ul><ul><li>Developed by Rand Corporation in 1940 where participants are involved in two assessment rounds. </li></ul></ul></ul><ul><ul><li>Work Breakdown Structure (WBS) </li></ul></ul><ul><ul><ul><li>A way of organizing project element into a hierarchy that simplifies the task of budget estimation and control </li></ul></ul></ul>
    19. 19. Expert Judgment (Cont.) <ul><li>Advantages </li></ul><ul><ul><li>Useful in the absence of quantified, empirical data. </li></ul></ul><ul><ul><li>Can factor in differences between past project experiences and requirements of the proposed project </li></ul></ul><ul><ul><li>Can factor in impacts caused by new technologies, applications and languages. </li></ul></ul><ul><li>Disadvantages </li></ul><ul><ul><li>Estimate is only as good expert’s opinion </li></ul></ul><ul><ul><li>Hard to document the factors used by the experts </li></ul></ul>
    20. 20. Top - Down <ul><li>Also called Macro Model </li></ul><ul><li>Derived from the global properties of the product and then partitioned into various low level components </li></ul><ul><li>Example – Putnam model </li></ul>
    21. 21. Top – Down (Cont.) <ul><li>Advantages </li></ul><ul><ul><li>Requires minimal project detail </li></ul></ul><ul><ul><li>Usually faster and easier to implement </li></ul></ul><ul><ul><li>Focus on system level activities </li></ul></ul><ul><li>Disadvantages </li></ul><ul><ul><li>Tend to overlook low level components </li></ul></ul><ul><ul><li>No detailed basis </li></ul></ul>
    22. 22. Bottom - Up <ul><li>Cost of each software components is estimated and then combine the results to arrive the total cost for the project </li></ul><ul><li>The goal is to construct the estimate of the system from the knowledge accumulated about the small software components and their interactions </li></ul><ul><li>An example – COCOMO’s detailed model </li></ul>
    23. 23. Bottom – Up (Cont.) <ul><li>Advantages </li></ul><ul><ul><li>More stable </li></ul></ul><ul><ul><li>More detailed </li></ul></ul><ul><ul><li>Allow each software group to hand an estimate </li></ul></ul><ul><li>Disadvantages </li></ul><ul><ul><li>May overlook system level costs </li></ul></ul><ul><ul><li>More time consuming </li></ul></ul>
    24. 24. Estimation by Analogy <ul><li>Comparing the proposed project to previously completed similar project in the same application domain </li></ul><ul><li>Actual data from the completed projects are extrapolated </li></ul><ul><li>Can be used either at system or component level </li></ul>
    25. 25. Estimation by Analogy (Cont.) <ul><li>Advantages </li></ul><ul><ul><li>Based on actual project data </li></ul></ul><ul><li>Disadvantages </li></ul><ul><ul><li>Impossible if no comparable project had been tackled in the past. </li></ul></ul><ul><ul><li>How well does the previous project represent this one </li></ul></ul>
    26. 26. Price to Win Estimation <ul><li>Price believed necessary to win the contract </li></ul><ul><li>Advantages </li></ul><ul><ul><li>Often rewarded with the contract </li></ul></ul><ul><li>Disadvantages </li></ul><ul><ul><li>Time and money run out before the job is done </li></ul></ul>
    27. 27. COCOMO 81 <ul><li>COCOMO stands for COnstructive COst MOdel </li></ul><ul><li>It is an open system First published by Dr Barry Bohem in 1981 </li></ul><ul><li>Worked quite well for projects in the 80’s and early 90’s </li></ul><ul><li>Could estimate results within ~20% of the actual values 68% of the time </li></ul>
    28. 28. COCOMO 81 <ul><li>COCOMO has three different models (each one increasing with detail and accuracy): </li></ul><ul><ul><li>Basic , applied early in a project </li></ul></ul><ul><ul><li>Intermediate , applied after requirements are specified. </li></ul></ul><ul><ul><li>Advanced , applied after design is complete </li></ul></ul><ul><li>COCOMO has three different modes: </li></ul><ul><ul><li>Organic – “relatively small software teams develop software in a highly familiar, in-house environment” [Bohem] </li></ul></ul><ul><ul><li>Embedded – operate within tight constraints, product is strongly tied to “complex of hardware, software, regulations, and operational procedures” [Bohem] </li></ul></ul><ul><ul><li>Semi-detached – intermediate stage somewhere between organic and embedded. Usually up to 300 KDSI </li></ul></ul>
    29. 29. COCOMO 81 <ul><li>COCOMO uses two equations to calculate effort in man months (MM) and the number on months estimated for project (TDEV) </li></ul><ul><li>MM is based on the number of thousand lines of delivered instructions/source (KDSI) </li></ul><ul><li>MM = a(KDSI) b * EAF </li></ul><ul><li>TDEV = c(MM) d </li></ul><ul><li>EAF is the Effort Adjustment Factor derived from the Cost Drivers, EAF for the basic model is 1 </li></ul><ul><li>The values for a, b, c, and d differ depending on which mode you are using </li></ul>0.32 2.5 1.20 3.6 Embedded 0.35 2.5 1.12 3.0 Semi-detached 0.38 2.5 1.05 2.4 Organic d c b a Mode
    30. 30. COCOMO 81 <ul><li>A simple example: </li></ul><ul><li>Project is a flight control system (mission critical) with 310,000 DSI in embedded mode </li></ul><ul><li>Reliability must be very high (RELY=1.40).  So we can calculate: </li></ul><ul><li>Effort = 1.40*3.6*(319) 1.20 = 5093 MM </li></ul><ul><li>Schedule = 2.5*(5093) 0.32 = 38.4 months </li></ul><ul><li>Average Staffing = 5093 MM/38.4 months = 133 FSP </li></ul>
    31. 31. COCOMO II <ul><li>Main objectives of COCOMO II: </li></ul><ul><ul><li>To develop a software cost and schedule estimation model tuned to the life cycle practices of the 1990’s and 2000’s </li></ul></ul><ul><ul><li>To develop software cost database and tool support capabilities for continuous model improvement </li></ul></ul><ul><li>From “Cost Models for Future Software Life Cycle Processes: COCOMO 2.0,&quot; Annals of Software Engineering , (1995). </li></ul>
    32. 32. COCOMO II <ul><li>Has three different models: </li></ul><ul><li>The Application Composition Model </li></ul><ul><ul><li>Good for projects built using rapid application development tools (GUI-builders etc) </li></ul></ul><ul><li>The Early Design Model </li></ul><ul><ul><li>This model can get rough estimates before the entire architecture has been decided </li></ul></ul><ul><li>The Post-Architecture Model </li></ul><ul><ul><li>Most detailed model, used after overall architecture has been decided on </li></ul></ul>
    33. 33. COCOMO II Differences <ul><li>The exponent value b in the effort equation is replaced with a variable value based on five scale factors rather then constants </li></ul><ul><li>Size of project can be listed as object points, function points or source lines of code (SLOC). </li></ul><ul><li>EAF is calculated from seventeen cost drivers better suited for today's methods, COCOMO81 has fifteen </li></ul><ul><li>A breakage rating has been added to address volatility of system </li></ul>
    34. 34. COCOMO II Calibration <ul><li>For COCOMO II results to be accurate the model must be calibrated </li></ul><ul><li>Calibration requires that all cost driver parameters be adjusted </li></ul><ul><li>Requires lots of data, usually more then one company has </li></ul><ul><li>The plan was to release calibrations each year but so far only two calibrations have been done (II.1997, II.1998) </li></ul><ul><li>Users can submit data from their own projects to be used in future calibrations </li></ul>
    35. 35. Importance of Calibration <ul><li>Proper calibration is very important </li></ul><ul><li>The original COCOMO II.1997 could estimate within 20% of the actual values 46% of the time. This was based on 83 data points. </li></ul><ul><li>The recalibration for COCOMO II.1998 could estimate within 30% of the actual values 75% of the time. This was based on 161 data points. </li></ul>
    36. 36. Is COCOMO the Best? <ul><li>COCOMO is the most popular method however for any software cost estimation you should really use more then one method </li></ul><ul><li>Best to use another method that differs significantly from COCOMO so your project is examined from more then one angle </li></ul><ul><li>Even companies that sell COCOMO based products recommend using more then one method. Softstar (creators of Costar) will even provide you with contact information for their competitor’s products </li></ul>
    37. 37. COCOMO Conclusions <ul><li>COCOMO is the most popular software cost estimation method </li></ul><ul><li>Easy to do, small estimates can be done by hand </li></ul><ul><li>USC has a free graphical version available for download </li></ul><ul><li>Many different commercial version based on COCOMO – they supply support and more data, but at a price </li></ul>
    38. 38. Conclusions <ul><li>Project costs are being poorly estimated </li></ul><ul><li>The accuracy of cost estimation has to be improved </li></ul><ul><ul><li>Data collection </li></ul></ul><ul><ul><li>Use of tools </li></ul></ul><ul><li>Use several methods of estimation </li></ul>
    39. 39. References <ul><li>Boehm B., Clark B., Horowitz E., Madachy R., Shelby R., Westland C. (1995). Cost Models for Future Software Life Cycle Processes: COCOMO 2.0, Annals of Software Engineering. http:// sunset.usc.edu/research/COCOMOII/Docs/stc.pdf . </li></ul><ul><li>Boehm B., Clark B., Horowitz E., Madachy R., Shelby R., Westland C. (1995). An Overview of the COCOMO 2.0 Software Cost Model. http:// sunset.usc.edu/research/COCOMOII/Docs/stc.pdf . </li></ul><ul><li>Boehm B., Chulani S., Clark B. (1997). Calibration Results of COCOMO II.1997. http://sunset.usc.edu/publications/TECHRPTS/1998/usccse98-502/CalPostArch.pdf . </li></ul><ul><li>Boehm B., Chulani S., Clark B. (1997). Calibrating the COCOMO II Post Architecture Model. http://sunset.usc.edu/Research_Group/Sunita/down/calpap.pdf . </li></ul><ul><li>Boehm B., Chulani S., Reifer D., The Rosetta Stone: Making COCOMO 81 Files Work With COCOMO II. http://sunset.usc.edu/publications/TECHRPTS/1998/usccse98-516/usccse98-516.pdf . </li></ul><ul><li>Chulani, S. (1998). Software Development Cost Estimation Approaches – A Survey. IBM Research. </li></ul><ul><li>Humphrey, W.S. (1990). Managing the Software Process. Addison-Wesley Publishing Company, New York, NY. </li></ul>
    40. 40. References <ul><li>Hussein, A. (2001). Introduction to Software Process Management. University of Calgary, Calgary, Canada. http://sern.ucalgary.ca/courses/SENG/621/W01/intro.ppt . </li></ul><ul><li>Londeix, B. (1987). Cost Estimation for Software Development. Addison-Wesley Publishing Company, New York, NY. </li></ul><ul><li>Pressman, R.S. (2001). Software Engineering: A Practitioner’s Approach. McGraw-Hill Higher Education, New York, NY. </li></ul><ul><li>Vigder, M. R. and Kark, A. W. (1994). Software Cost Estimation and Control. Software Engineering Institute for Information Technology. http://wwwsel.iit.nrc.ca/seldocs/cpdocs/NRC37116.pdf . </li></ul><ul><li>Wu, L. (1997). The comparison of the Software Cost Estimating Methods. University of Calgary, Calgary, Canada. http://sern.ucalgary.ca/courses/seng/621/W97/wul/seng621_11.html . </li></ul><ul><li>Basic COCOMO Software Cost Model. http://www.jsc.nasa.gov/bu2/COCOMO.html . </li></ul><ul><li>COCOMO 2, Softstar Systems. http://www.softstarsystems.com/cocomo2.htm . </li></ul><ul><li>Answers to Frequently Asked Questions, Softstar Systems. http:// www.softstarsystems.com/faq.htm . </li></ul>
    41. 41. Questions and Discussion

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