calibrating function points using neuro fuzzy technique
Download
Skip this Video
Download Presentation
Calibrating Function Points Using Neuro-Fuzzy Technique

Loading in 2 Seconds...

play fullscreen
1 / 14

Calibrating Function Points Using Neuro-Fuzzy Technique - PowerPoint PPT Presentation


  • 71 Views
  • Uploaded on

Calibrating Function Points Using Neuro-Fuzzy Technique. Luiz F Capretz. Danny Ho. Vivian Xia. IT Department HSBC Bank Vancouver, BC Canada Vivian\[email protected] Department of Electrical and Computer Engineering University of Western Ontario London, Ontario, Canada

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Calibrating Function Points Using Neuro-Fuzzy Technique' - kermit-wyatt


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
calibrating function points using neuro fuzzy technique

Calibrating Function Points Using Neuro-Fuzzy Technique

Luiz F Capretz

Danny Ho

Vivian Xia

IT Department

HSBC Bank

Vancouver, BC Canada

[email protected]

Department of Electrical and Computer Engineering

University of Western Ontario

London, Ontario, Canada

[email protected]

NFA Estimation Inc. London, Ontario, Canada

[email protected]

roadmap
Roadmap
  • Concepts of Calibration
  • Neuro-Fuzzy Function Points Calibration Model
  • Validation Result
  • Conclusions
calibration concept
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
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 d1
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

slide6
Neural Networks Basics

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
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 fuzzy logic
Calibrating by Fuzzy Logic

Fuzzy Logic System

Fuzzy Set

Fuzzy Rule

Input

Output

Fuzzy Inference

calibrating by neural network
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
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

    • Shrink to 184 projects
validation methodology
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
Validation Results (MMRE)
  • MMRE:
    • Mean Magnitude of Relative Error
  • Criteria to assess estimation error
  • The lower the better
validation results pred
Validation Results (PRED)
  • PRED:
    • Prediction at level p
  • Criteria to assess estimation ability
  • The higher the better
conclusions
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.
ad