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Calibrating Function Points Using Neuro-Fuzzy TechniquePowerPoint Presentation

Calibrating Function Points Using Neuro-Fuzzy Technique

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### Calibrating Function Points Using Neuro-Fuzzy Technique

Luiz F Capretz

Danny Ho

Vivian Xia

IT Department

HSBC Bank

Vancouver, BC Canada

Department of Electrical and Computer Engineering

University of Western Ontario

London, Ontario, Canada

NFA Estimation Inc. London, Ontario, Canada

Roadmap

- Concepts of Calibration
- Neuro-Fuzzy Function Points Calibration Model
- Validation Result
- Conclusions

Calibration Concept

Internal Logical File (ILF) Complexity Matrix

DET, RET --- Component Associated Files

Same methodology for all FP 5 components

External Input, External Output, External Inquiry

Internal Logical File, External Interface File

Calibration Concept Cont’d

- e.g. One project has 3 Internal Logical Files (ILF)

- Calibrate complexity degree by fully utilizing the number of component associated files
- Calibrate to fit specific application

Calibration Concept Cont’d

Unadjusted Function Points Weight Values

UFP weight values are determined in 1979 based on

Albrecht’s study of 22 IBM Data Processing projects

.

Calibrate UFP weight values to reflect global software industry trend

Learning from Data Source

- Adapting capability
- Modeling any complex nonlinear relationships
- Lack of explanation: “black box”
- Cannot take linguistic information directly

Neuro-Fuzzy Function Points Calibration Model Overview

Estimation

Equation

ISBSG 8

Project Data

Validated for better estimation

Calibrated

by

Neural

Network

MMRE, PRED

Calibrated

by Fuzzy Logic

Calibrating by Neural Network

- Learn UFP weight values by effort
- the values should reflect complexity
- complexity proportioned to effort

- 15 UFP inputs as neurons
- Back-propagation algorithm

Data Source --- ISBSG Release 8

- ISBSG
- International Software Benchmarking Standards Group
- Non-profit organization

- Release 8 Contains 2,027 projects
- 75% built in recent 5 years
- Filter on ISBSG 8 data set
- Filter Criteria:
- Quality, Counting method, Resource level,
Development Types, UFP breakdowns

- Quality, Counting method, Resource level,
- Shrink to 184 projects

- Filter Criteria:

Validation Methodology

- Developed a calibration tool
- Randomly split data set
- totally 184 data points
- 100 training points
- 84 testing points for validation

- Repeat 5 times
- Using estimation equation for comparison

Validation Results (MMRE)

- MMRE:
- Mean Magnitude of Relative Error

- Criteria to assess estimation error
- The lower the better

Validation Results (PRED)

- PRED:
- Prediction at level p

- Criteria to assess estimation ability
- The higher the better

Conclusions

- Neuro-Fuzzy Function Points model improves software cost estimation by an average of 22%.
- Fuzzy logic calibration part improves UFP complexity classification.
- Neural network calibration part overcomes problems with UFP weight values.

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