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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 Vivian_xia@hsbc.ca. Department of Electrical and Computer Engineering University of Western Ontario London, Ontario, Canada

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Calibrating Function Points Using Neuro-Fuzzy Technique

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  1. Calibrating Function Points Using Neuro-Fuzzy Technique Luiz F Capretz Danny Ho Vivian Xia IT Department HSBC Bank Vancouver, BC Canada Vivian_xia@hsbc.ca Department of Electrical and Computer Engineering University of Western Ontario London, Ontario, Canada lcapretz@eng.uwo.ca NFA Estimation Inc. London, Ontario, Canada danny@nfa-estimation.com

  2. Roadmap • Concepts of Calibration • Neuro-Fuzzy Function Points Calibration Model • Validation Result • Conclusions

  3. 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

  4. 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

  5. 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

  6. Neural Networks Basics Learning from Data Source • Adapting capability • Modeling any complex nonlinear relationships • Lack of explanation: “black box” • Cannot take linguistic information directly

  7. 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

  8. Calibrating by Fuzzy Logic Fuzzy Logic System Fuzzy Set Fuzzy Rule Input Output Fuzzy Inference

  9. 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

  10. 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

  11. 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

  12. Validation Results (MMRE) • MMRE: • Mean Magnitude of Relative Error • Criteria to assess estimation error • The lower the better

  13. Validation Results (PRED) • PRED: • Prediction at level p • Criteria to assess estimation ability • The higher the better

  14. 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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