4. Lecture 3.ppt
- 1. T. E. Potok - University of Tennessee CS 594 Software Engineering Lecture 3 Dr. Thomas E. Potok potokte@ornl.gov 865-574-0834
- 2. 2 Software Engineering CS 594 T. E. Potok - University of Tennessee Agenda Review COCOMO PERT
- 3. 3 Software Engineering CS 594 T. E. Potok - University of Tennessee AMI Update 200 jobs per day AMI has received a quote from Acme Consulting of $40K to do the work in 2 months Ballpark price range for AMI is $20- $30K.
- 4. 4 Software Engineering CS 594 T. E. Potok - University of Tennessee Linear Regression Where is an estimate of the mean of Y, and are numerical estimated of the parameters Sample Regression Line 0 5 10 15 20 25 0 2 4 6 8 10 12 i i i E Y i Y
- 5. 5 Software Engineering CS 594 T. E. Potok - University of Tennessee Many early studies applied regression Data gathered from multiple software project Log-linear relationship found between project size and effort Where PM are person-months, KLOC is thousands of lines of code ln(PM) = ln() + ln(KLOC)+ .
- 6. 6 Software Engineering CS 594 T. E. Potok - University of Tennessee Derivation KLOC PM KLOC e PM e e PM e e PM e e KLOC PM Y E Y KLOC KLOC KLOC PM i i i ) ln( ) ln( ) ln( ) ln( ) ln( ) ln( ) ln( ) ln(
- 7. 7 Software Engineering CS 594 T. E. Potok - University of Tennessee Typical Effort Vs Project Size Curve Typical Log-linear Effort Curve 0 2000 4000 6000 8000 10000 12000 14000 0 100000 200000 300000 400000 500000 600000 Lines of code Effort
- 8. 8 Software Engineering CS 594 T. E. Potok - University of Tennessee Constructive Cost Model (COCOMO) Developed by Barry Boehm Statistical model of software development effort and time. Base on results from 63 projects completed at TRW. Basic model is a log-linear regression model that fits the 63 projects Productivity ranges: – 20 - 1250 LOC/PM
- 9. 9 Software Engineering CS 594 T. E. Potok - University of Tennessee Basic COCOMO Organic - small to medium size, familiar projects – Person-months=2.4(KLOC)1.05 – Development-time = 2.5(PM).38 Semidetached - intermediate – Person-months=3.0(KLOC)1.12 – Development-time = 2.5(PM).35 Embedded - ambitious, tightly constrained – Person-months=3.6(KLOC)1.20 – Development-time = 2.5(PM).32
- 10. 10 Software Engineering CS 594 T. E. Potok - University of Tennessee COCOMO Models COCOMO Models 0 1000 2000 3000 4000 5000 6000 7000 0 100 200 300 400 500 600 Thousands of lines of code Person-months Organic Semidetached Embedded
- 11. 11 Software Engineering CS 594 T. E. Potok - University of Tennessee Cost Drivers Product Attributes – Required Reliability – Database Size – Product Complexity Computer Attributes – Execution Time Constraints – Main storage constraints – Virtual Machine Volatility – Computer turnaround time
- 12. 12 Software Engineering CS 594 T. E. Potok - University of Tennessee More Cost Drivers Personnel Attributes – Analyst Capability – Application Experience – Programmer Capability – Virtual Machine Experience – Programming Language Experience Project Attributes – Modern Programming Practices – Use of Software Tools – Required Development Schedule
- 13. 13 Software Engineering CS 594 T. E. Potok - University of Tennessee Example Need to produce 10,000 LOC, 10 KLOC. Small project, familiar development Use organic model: – Person-months=2.4(10)1.05 =26.9 Person-months – Development-time = 2.5(26.9).38 =8.7 Months – Average People = 26.9 PM/8.7 Months = 3 People Linear model 3 people would take 16.5 months, at 50 person-months
- 14. 14 Software Engineering CS 594 T. E. Potok - University of Tennessee Example We also know that the design experience is low – Analyst, - 1.19 – application, - 1.13 – programmer experience is low. - 1.17 Yet the programming experience is high - .95 Adjustment factor 1.19*1.13*1.17*.95 = 1.49 PM = 26.9*1.49 = 40 Person-months Development time = 10.2 Months People = 3.9 People
- 15. 15 Software Engineering CS 594 T. E. Potok - University of Tennessee Drawbacks COCOMO has to be calibrated to your environment. Very sensitive to change. – Over a person-year difference in a 10 KLOC project with minor adjustments Broad brush model that can generate significant errors
- 16. 16 Software Engineering CS 594 T. E. Potok - University of Tennessee COCOMO 2.0 Includes – COTS and reusable software – Degree of understanding of requirements and architectures – Schedule constraints – Project size – Required reliability Three Types of models – Application Composition - Prototyping or RAD – Early Design - Alternative evaluation – Post-architecture - Detailed estimates
- 17. 17 Software Engineering CS 594 T. E. Potok - University of Tennessee COCOMO Summary Quick and easy to use Provides a reasonable estimate Needs to be calibrated Results must be treated as ball park values unless substantial validation has been performed.
- 18. 18 Software Engineering CS 594 T. E. Potok - University of Tennessee PERT Project Evaluation and Review Technique – Developed for the Navy Polaris Missile Program – Directed Acyclic Graphs of project activities – Used for estimation and control of a project
- 19. 19 Software Engineering CS 594 T. E. Potok - University of Tennessee Example Start project Gather requirements Document requirements Create design Document design Review design Create code Document code Define test cases Test code Demonstrate Finish project To create our 10K program we need the following activities
- 20. 20 Software Engineering CS 594 T. E. Potok - University of Tennessee PERT Example Start Req Design Review Code Test Demo Finish Doc Req Doc Design Doc Code Test Case
- 21. 21 Software Engineering CS 594 T. E. Potok - University of Tennessee Duration Estimates Tasks Minimum Average Maximum Critical Path Start project 0 0 0 Y Gather requirements 3 5 7 Y Document requirements 2 3 5 N Create design 5 9 13 Y Document design 2 3 5 N Review design 1 2 3 Y Create code 7 12 20 Y Document code 2 4 7 N Define test cases 3 5 8 N Test code 5 7 12 Y Demonstrate 1 2 3 Y Finish project 0 0 0 Y
- 22. 22 Software Engineering CS 594 T. E. Potok - University of Tennessee Critical Path Estimate Tasks Minimum Average Maximum Critical Path Start project 0 0 0 Y Gather requirements 3 5 7 Y Create design 5 9 13 Y Review design 1 2 3 Y Create code 7 12 20 Y Test code 5 7 12 Y Demonstrate 1 2 3 Y Finish project 0 0 0 Y Total 22 37 58
- 23. 23 Software Engineering CS 594 T. E. Potok - University of Tennessee Completion Probability Triangular Distribution 0.000 0.010 0.020 0.030 0.040 0.050 0.060 20 25 30 35 40 45 50 55 60 Duration Probability Probability
- 24. 24 Software Engineering CS 594 T. E. Potok - University of Tennessee Cumulative Completion Probability Triangular Distribution 0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900 1.000 20 25 30 35 40 45 50 55 60 Duration Cumulative Probability Cumulative Probability 80% Probability of Completion in 46 days