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research-article

ANN model for predicting software function point metric

Authors:

Pradeep Kumar Bhatia,

Omprakash SangwanAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 34, Issue 1

Pages 1 - 4

https://doi.org/10.1145/1457516.1460352

Published: 31 January 2009 Publication History

Abstract

Software Engineering measurement and analysis specially, size estimation initiatives have been in the center of attention for many firms. Function Point (FP) metric is among the most commonly used techniques to estimate the size of software system projects or software systems for measuring the functionality delivered by a system. In this paper we explore an alternative, Artificial Neural Network (ANN) approach for predicting function Point. We proposed an ANN model to explore neural network as tool for function point metric. A multilayer feed forward network is trained using backpropogation algorithm and demonstrated to be suitable. The training and validation data is randomly selected from the data repository of 365 projects [7]. The experimental results of two validation sets each of 55 projects indicate that the Mean Absolute Relative Error (MARE) was 0.198 and 0.145 of ANN model and shows that ANN model is a competitive model as Function Point Metric.

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Cited By

Mittal SSingh TSharma RDalip Gandhi P(2023)Soft Computing Methods for Predicting Component Development System Through Software Reusability2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)10.1109/ICCCIS60361.2023.10425651(166-172)Online publication date: 3-Nov-2023
https://doi.org/10.1109/ICCCIS60361.2023.10425651
Zhang KWang XRen JLiu C(2021)Efficiency Improvement of Function Point-Based Software Size Estimation With Deep Learning ModelIEEE Access10.1109/ACCESS.2020.29985819(107124-107136)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2020.2998581
Kumar SMishra RMitra ABiswas SDe SKarmakar R(2020)A Relative Comparison of Training Algorithms in Artificial Neural Network2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)10.1109/ICCE50343.2020.9290718(315-319)Online publication date: 5-Sep-2020
https://doi.org/10.1109/ICCE50343.2020.9290718
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Index Terms

ANN model for predicting software function point metric
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics
2. Software and its engineering
  1. Software creation and management
    1. Designing software
      1. Software implementation planning
        Software design techniques
    2. Software development process management
  2. Software notations and tools

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Information

Published In

cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 34, Issue 1

January 2009

119 pages

ISSN:0163-5948

DOI:10.1145/1457516

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Copyright © 2009 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 January 2009

Published in SIGSOFT Volume 34, Issue 1

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Cited By

Mittal SSingh TSharma RDalip Gandhi P(2023)Soft Computing Methods for Predicting Component Development System Through Software Reusability2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)10.1109/ICCCIS60361.2023.10425651(166-172)Online publication date: 3-Nov-2023
https://doi.org/10.1109/ICCCIS60361.2023.10425651
Zhang KWang XRen JLiu C(2021)Efficiency Improvement of Function Point-Based Software Size Estimation With Deep Learning ModelIEEE Access10.1109/ACCESS.2020.29985819(107124-107136)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2020.2998581
Kumar SMishra RMitra ABiswas SDe SKarmakar R(2020)A Relative Comparison of Training Algorithms in Artificial Neural Network2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)10.1109/ICCE50343.2020.9290718(315-319)Online publication date: 5-Sep-2020
https://doi.org/10.1109/ICCE50343.2020.9290718
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https://dl.acm.org/doi/10.1007/s10515-014-0161-3
Malhotra RRaje R(2014)An empirical comparison of machine learning techniques for software defect predictionProceedings of the 8th International Conference on Bioinspired Information and Communications Technologies10.4108/icst.bict.2014.257871(320-327)Online publication date: 1-Dec-2014
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Treiber MSchall DDustdar SScherling COssowski SLecca P(2012)Creating mobile ad hoc workflows with TwitterProceedings of the 27th Annual ACM Symposium on Applied Computing10.1145/2245276.2232107(1998-2000)Online publication date: 26-Mar-2012
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Singh YBhatia PSangwan O(2011)Software reusability assessment using soft computing techniquesACM SIGSOFT Software Engineering Notes10.1145/1921532.192154836:1(1-7)Online publication date: 24-Jan-2011
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