Steve Mattingley

1. Steve Mattingley October 22, 2002 Paper Presentation

2. A Vector-Based Approach to Software Size Measurement and Effort Estimation • T.E Hastings and A.S.M Sajeev • IEEE Transactions on Software Engineering • Vol 27, NO. 4, April 2001 • IEEECS Log Number 107066

3. Paper Overview • Introduction • Software Size Measurement • Software Specification • Vector Size Measure • Vector Prediction Models • Formal Validation • Empirical Validation • Discussion

4. Introduction • Software size is difficult to define and measure • SLOC and Function Points are two common methods • Vector Size Measure (VSM) and Vector Prediction Model (VPM) using Algebraic Specification Language (ASL)

5. Software Size Measurement • Measure Processes, Products, and Resources • We are interested in internal attributes of software (independent from environment). • Size is a function of length, functionality, and complexity (problem complexity).

6. Software Size Specification • To accurately predict software size one must have an accurate software specification. • Algebraic approach to software specification is based on Abstract Data Types (ADT) • ADT ::= F • R • An ADT is a set of functions and Rules

7. Software Size Specification • ASL used to describe software. Example: • deposit (Money, Account): Account` | balance(Account`) = balance(Account) add Money; {post condition} • deposit an amount of Money into an Account such that the new account equals the sum of the original account and the new money.

8. Vector Size Measure • Rule set for determining functionality from number of Functional Ops in ALS: Fa = OPF • Rule set for determining complexity from number of Complex Ops in ALS: Ca = OPC • Length is the sum of functionality and complexity. La = Fa + Ca

9. Vector Size Measure • Vector representation <F, C> (Functionality and Complexity) • Normal Vector mathematics apply to solve for magnitude and gradient of vector. • Magnitude provides a single measure which balances functionality and complexity. • Gradient provides a ration of problem complexity vs. functionality • Vectors may be scaled with real world units and scale factors.

10. Vector Prediction Models • Used instead of Productivity Based Models, Linear Regression, or COCOMO. • VPM is a cost model which takes the magnitude and gradient from the VSM and uses a multivariate regression model. • VPM is based on having a defined and repeatable software process in place.

11. Vector Prediction Models • Estimated Effort: E` = ambgz • m = vector magnitude, g = vector gradient • a,b,z are coefficients which are determined empirically for the organization and software type.

12. Formal Validation • Authors propose 4 rules which must be followed for ADT, ASL and VSM. • The rules ensure Formal Validation of the Vector approach.

13. Empirical Validation • The Vector based approach was validated empirically using a pilot study of 8 industry projects. • For the various projects: Requirements were gathered, product attributes were gathered, process and resource attributes were gathered, and ASLs were developed for each project.

14. Empirical Validation • A measurement tool then produced measurements from the project ASLs. • A function point count, MKII FP count, Linear Regression, VPM , and COCOMO 2 analysis were all performed. • Results were then compared and tested against defined test criteria.

15. Empirical Validation

16. Empirical Validation

17. Author’s Discussion • VPM provides a means of classifying software systems (based on functionality, complexity vectors) • Less inputs then COCOMO • 20 percent within actualize (Better then alternatives) • Limited by having to specify using ASL • Limited by having a bottoms up approach

18. My discussion • Interesting approach • Takes much of the guess work out of estimating a well specified system. • Seems like an enormous amount of work to specify large systems at that detail. • Would require a lot of maintenance to ASLs as system requirements change

19. Questions • ???