Early Effort Estimation of Business Data-processing Enhancements

1. Early Effort Estimation of Business Data-processing Enhancements CS 689 November 30, 2000 By Kurt Detamore

2. Background • Software effort estimation has been researched for several years • Most research has involved medium to large development projects • Concentration on small projects for external customers

3. Importance of Accurate Estimates • Increased customer satisfaction • Increased productivity of development staff • Competitive advantage in a bidding war for services contract

4. Problem The estimation of small development projects, specifically, the development of customizations or enhancements to existing products to meet customer needs has not been studied. Estimation of small projects in many cases is a “best guess” estimate.

5. Existing Tools • COCOMO (Boehm) • COCOMO II (Boehm) • SLIM (Putnam) • Function Point Analysis (Albrecht) • Feature-based model (Mukhopadhyay)

6. Limitations of COCOMO • Must be calibrated to provide accurate results • Not accurate for incrementally developed projects • Uses lines of code to calculate estimates

7. Limitations of SLIM • Proprietary calculations • Requires significant technical knowledge to implement • Requires two of the following • Size greater than 5000 lines • Effort greater than 1.5 man.years • Development time greater than 6 months • Uses lines of code to calculate estimates

8. Function Point Analysis • Must be calibrated to be accurate • Fourteen general characteristics factors rated on a scale from 0 (no influence) to 5 (essential) • Developed using business data processing systems • Non-technical staff can be trained to execute this method • Guidelines exist for counting function points in enhancement projects • Function Points can be counted after requirements analysis

9. Feature-based Model • Designed to use as an early estimation tool • Requires only limited information • Developed for Process Control Applications

10. Calculations for Feature-based Model • Model for calculating function counts from user-defined features • Model for calculating lines of code from user-defined features

11. Research Objectives • Develop a calibrated Function Point Analysis (FPA) estimation model with a margin of error less than 40% • Develop an early estimation model using the application features as the key to estimation with a margin of error less than 25%

12. Research Design • Use the FPA model • Evaluate approximately 40 projects completed over the past two years • Individual project size between 5 days and 150 days of total effort • Calculate the language level of the proprietary language in use • Calibrate the 14 “general characteristics factors”

13. Research Design (cont’d) • Develop feature-based model • Identify groups of features to determine function counts and/or lines of code • Addition of fields to screens • Creation of new screens • Creation of new functionality within a screen • Evaluate existing projects to determine constant values for the formulas

14. Data Analysis • Calculate magnitude of relative error for FPA and feature-based models • Calculate correlation of the estimates for each model

15. Facilities and Resources • No special resources are needed • Tools used • Microsoft Excel • WinStat

16. Schedule • January 2001 – Proposal accepted and training on FPA completed • February to June 2001 – FPA research • Possible publication of FPA research results • July to November 2001 – Feature-based model analysis • December 2001 – Article submitted for publication to IEEE Transactions on Software Engineering

17. Deliverables • Calibrated FPA model • Feature-based model adapted for business data-processing applications • Research report which details accuracy and consistency of models

18. Conclusion • Research on effort estimation of small, incremental development projects has not been adequately provided up to this point • The results of this research will provide tools to improve the accuracy and consistency of estimates for these development projects

19. Any Questions?