T. E. Potok - University of Tennessee CS 594 Software Engineering Lecture 3 Dr. Thomas E. Potok

2 Software Engineering CS 594T. E. Potok - University of Tennessee Agenda  Review  COCOMO  PERT

3 Software Engineering CS 594T. 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 Software Engineering CS 594T. E. Potok - University of Tennessee Linear Regression Where is an estimate of the mean of Y, and are numerical estimated of the parameters

5 Software Engineering CS 594T. 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

6 Software Engineering CS 594T. E. Potok - University of Tennessee Derivation

7 Software Engineering CS 594T. E. Potok - University of Tennessee Typical Effort Vs Project Size Curve

8 Software Engineering CS 594T. 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: – LOC/PM

9 Software Engineering CS 594T. 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 Software Engineering CS 594T. E. Potok - University of Tennessee COCOMO Models

11 Software Engineering CS 594T. 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 Software Engineering CS 594T. 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 Software Engineering CS 594T. 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 Software Engineering CS 594T. E. Potok - University of Tennessee Example  We also know that the design experience is low – Analyst, – application, – programmer experience is low  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 Software Engineering CS 594T. 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 Software Engineering CS 594T. 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 Software Engineering CS 594T. 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 Software Engineering CS 594T. 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 Software Engineering CS 594T. 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 Software Engineering CS 594T. E. Potok - University of Tennessee PERT Example StartReqDesignReviewCodeTestDemoFinish Doc Req Doc Design Doc Code Test Case

21 Software Engineering CS 594T. E. Potok - University of Tennessee Duration Estimates

22 Software Engineering CS 594T. E. Potok - University of Tennessee Critical Path Estimate

23 Software Engineering CS 594T. E. Potok - University of Tennessee Completion Probability

24 Software Engineering CS 594T. E. Potok - University of Tennessee Cumulative Completion Probability 80% Probability of Completion in 46 days