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Calibrating use case points

Published: 31 May 2014 Publication History

Abstract

An approach to calibrate the complexity weights of the use cases in the Use Case Points (UCP) model is put forward. The size metric used is the Use Case Points (UCP) which can be calculated from the use case diagram along with its use case scenario as described in the UCP model. The approach uses a neural network with fuzzy logic to tune the complexity weights.

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  • (2023)Random forests for predicting software effort estimation based on use-case points analysisPROCEEDINGS OF THE 4TH INTERNATIONAL COMPUTER SCIENCES AND INFORMATICS CONFERENCE (ICSIC 2022)10.1063/5.0174699(040001)Online publication date: 2023
  • (2021)Empirical analysis on productivity prediction and locality for use case points methodSoftware Quality Journal10.1007/s11219-021-09547-0Online publication date: 6-Apr-2021
  • (2020)Software Effort Estimation from Use Case Diagrams Using Nonlinear Regression Analysis2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)10.1109/CCECE47787.2020.9255712(1-4)Online publication date: 30-Aug-2020
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cover image ACM Conferences
ICSE Companion 2014: Companion Proceedings of the 36th International Conference on Software Engineering
May 2014
741 pages
ISBN:9781450327688
DOI:10.1145/2591062
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • TCSE: IEEE Computer Society's Tech. Council on Software Engin.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2014

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Author Tags

  1. Fuzzy Logic
  2. Neural Networks
  3. Software Effort Estimation
  4. Use Case Points

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  • (2023)Random forests for predicting software effort estimation based on use-case points analysisPROCEEDINGS OF THE 4TH INTERNATIONAL COMPUTER SCIENCES AND INFORMATICS CONFERENCE (ICSIC 2022)10.1063/5.0174699(040001)Online publication date: 2023
  • (2021)Empirical analysis on productivity prediction and locality for use case points methodSoftware Quality Journal10.1007/s11219-021-09547-0Online publication date: 6-Apr-2021
  • (2020)Software Effort Estimation from Use Case Diagrams Using Nonlinear Regression Analysis2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)10.1109/CCECE47787.2020.9255712(1-4)Online publication date: 30-Aug-2020
  • (2020)Heart Arrhythmia Abnormality Classification Using Machine Learning2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)10.1109/CCCI49893.2020.9256763(1-5)Online publication date: 3-Nov-2020
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  • (2019)Estimating Change Effort Using a Combination of Change Impact Analysis Technique with Function Point AnalysisProceedings of the 8th International Conference on Software and Information Engineering10.1145/3328833.3328836(9-14)Online publication date: 9-Apr-2019
  • (2018)Calibrating use case points using bayesian analysisProceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3239235.3239236(1-10)Online publication date: 11-Oct-2018
  • (2018)Estimated software measurement base on use case for online admission systemIOP Conference Series: Materials Science and Engineering10.1088/1757-899X/434/1/012062434(012062)Online publication date: 4-Dec-2018
  • (2018)Comparative analysis of soft computing techniques for predicting software effort based use case pointsIET Software10.1049/iet-sen.2016.032212:1(19-29)Online publication date: Feb-2018
  • (2018)Project productivity evaluation in early software effort estimationJournal of Software: Evolution and Process10.1002/smr.211030:12Online publication date: 21-Sep-2018
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