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Journal of Intelligent Systems

Editor-in-Chief: Fleyeh, Hasan

CiteScore 2018: 1.03

SCImago Journal Rank (SJR) 2018: 0.188
Source Normalized Impact per Paper (SNIP) 2018: 0.533

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Analogy-Based Approaches to Improve Software Project Effort Estimation Accuracy

V Resmi
  • Corresponding author
  • Department of Computer Applications, Udaya School of Engineering, Vellamodi, Tamil Nadu, 629 204, India
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/ S Vijayalakshmi
  • Department of Computer Applications, Thiagarajar College of Engineering, Madurai, Tamil Nadu, 625 015, India
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Published Online: 2019-06-27 | DOI: https://doi.org/10.1515/jisys-2019-0023


In the discipline of software development, effort estimation renders a pivotal role. For the successful development of the project, an unambiguous estimation is necessitated. But there is the inadequacy of standard methods for estimating an effort which is applicable to all projects. Hence, to procure the best way of estimating the effort becomes an indispensable need of the project manager. Mathematical models are only mediocre in performing accurate estimation. On that account, we opt for analogy-based effort estimation by means of some soft computing techniques which rely on historical effort estimation data of the successfully completed projects to estimate the effort. So in a thorough study to improve the accuracy, models are generated for the clusters of the datasets with the confidence that data within the cluster have similar properties. This paper aims mainly on the analysis of some of the techniques to improve the effort prediction accuracy. Here the research starts with analyzing the correlation coefficient of the selected datasets. Then the process moves through the analysis of classification accuracy, clustering accuracy, mean magnitude of relative error and prediction accuracy based on some machine learning methods. Finally, a bio-inspired firefly algorithm with fuzzy analogy is applied on the datasets to produce good estimation accuracy.

Keywords: Effort estimation; analogy-based estimation; classification; clustering; firefly optimization; fuzzy analogy; linear regression; multilayer perceptron; k-means algorithm; EM algorithm


  • [1]

    A. J. Albrecht and J. A. Gaffney, Software function, source lines of codes, and development effort prediction: a software science validation, IEEE Trans. Softw. Engg. 9 (1983), 639–648.Google Scholar

  • [2]

    R. de A. Araujo, A. L. I. Oliveira and S. Meira, A class of hybrid multilayer perceptrons for software development effort estimation problems, Expert Syst. Appl. 90 (2017), 1–12.CrossrefWeb of ScienceGoogle Scholar

  • [3]

    M. Azzeh, A replicated assessment and comparison of adaptation techniques for analogy-based effort estimation, Empir. Softw. Eng. 17 (2012), 90–127.Web of ScienceCrossrefGoogle Scholar

  • [4]

    M. Azzeh and A. B. Nassif, Analogy-based effort estimation: a new method to discover set of analogies from dataset characteristics, IET Softw. 9 (2015), 39–50.CrossrefWeb of ScienceGoogle Scholar

  • [5]

    B. W. Boehm, Software engineering economics, Prentice Hall, Englewood Cliffs, NJ, 1981.Google Scholar

  • [6]

    B. W. Boehm and R. Valerdi, Achievements and challenges in cocomo-based software resource estimation, IEEE Softw. 25 (2008), 74–83.Web of ScienceCrossrefGoogle Scholar

  • [7]

    E. Borandag, F. Yucalar and S. Z. Erdogan, A case study for the software size estimation through MK II FPA and FP methods, Int. J. Comput. Appl. Technol. 53 (2016), 309–314.Web of ScienceCrossrefGoogle Scholar

  • [8]

    S. D. Conte, H. E. Dunsmore and V. Y. Shen, Software engineering metrics and models, Benjamin-Cummings Publishing, Redwood City, 1986.Google Scholar

  • [9]

    GitHub. https://dzone.com/articles/deep-learning-via-multilayer-perceptron-classifier.

  • [10]

    J. Han and M. Kamber, Data mining concepts and techniques, 2nd ed., Elsevier, Amsterdam, The Netherlands, Reprinted 2008.Google Scholar

  • [11]

    S. Haykin, Neural networks: a comprehensive foundation, 2nd ed. Prentice Hall, New York, 1998.Google Scholar

  • [12]

    M. Humayun and C. Gang, Estimating effort in global software development projects using machine learning techniques, Int. J. Inform. Edu. Technol. 2 (2012), 208–211.Google Scholar

  • [13]

    C. Jones, Estimating software costs: bringing realism to estimating, 2nd ed., McGraw-Hill, New York, 2007.Google Scholar

  • [14]

    M. Jørgensen, Experience with the accuracy of software maintenance task effort prediction models, IEEE Trans. Softw. Engg. 21 (1995), 674–681.CrossrefGoogle Scholar

  • [15]

    A. Kaushik, S. Verma, H. J. Singh and G. Chhabra, Software cost optimization integrating fuzzy system and COA-cuckoo optimization algorithm, Int. J. Syst. Assur. Eng. Manage. 8 (2017), 1461–1471.CrossrefWeb of ScienceGoogle Scholar

  • [16]

    J. Keung, B. Kitchenham and D. R. Jeffery, Analogy-X: providing statistical inference to analogy-based software cost estimation, IEEE Trans. Softw. Engg. 34 (2008), 471–484.CrossrefGoogle Scholar

  • [17]

    B. V. Khatibi, D. N. A. Jawawi, S. Z. M. Hashim and E. Khatibi, Increasing the accuracy of software development effort estimation using projects clustering, IET Softw. 6 (2012), 461–473.Web of ScienceCrossrefGoogle Scholar

  • [18]

    B. V. Khatibi, D. N. A. Jawawi and E. Khatibi, Increasing the accuracy of analogy based software development effort estimation using neural networks, Int. J. Comput. Commun. Engg. 2 (2013), 78–81.Google Scholar

  • [19]

    E. Kocaguneli, T. Menzies and A. Bener, Exploiting the essential assumptions of analogy-based effort estimation, IEEE Trans. Softw. Eng. 38 (2012), 425–438.Web of ScienceCrossrefGoogle Scholar

  • [20]

    J. Z. Li, G. Ruhe, A. Al-Emran and M. M. Ritcher, A flexible method for software effort estimation by analogy, Empir. Softw. Eng. 12 (2007), 65–106.CrossrefWeb of ScienceGoogle Scholar

  • [21]

    S. Malathi and S. Sridhar, Estimation of effort in software cost analysis for heterogeneous dataset using fuzzy analogy, Int. J. Comput. Sci. Inform. Security, 10 (2012), arXiv:1211.1136.Google Scholar

  • [22]

    E. A. Nelson, Management handbook for the estimation of computer programming costs, System Developer Corp., Santa Monica, CA, USA, 1966.Google Scholar

  • [23]

    Prabhakar and M. Dutta, Prediction of software effort using artificial neural network and support vector machine, Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3 (2013), 40–46.Google Scholar

  • [24]

    L. H. Putnam, A general empirical solution to the macrosoftware sizing and estimating problem, IEEE Trans. Softw. Engg. 4 (1987), 345–361.Google Scholar

  • [25]

    S. K. Sarangi and V. Jaglan, Performance comparison of machine learning algorithms on integration of clustering and classification techniques, Int. J. Emerg. Technol. Comput. Appl. Sci. (2013) 251–257.Google Scholar

  • [26]

    S. M. Satapathy, M. Kumar and S. K. Rath, Fuzzy-class point approach for software effort estimation using various adaptive regression methods, CSI Trans. ICT 1 (2013), 367–380.CrossrefGoogle Scholar

  • [27]

    M. Shepperd and C. Schofield, Estimating software project effort using analogies, IEEE Trans. Softw. Engg. 23 (1997), 736–743.CrossrefGoogle Scholar

  • [28]

    X. S. Yang, Nature-inspired metaheuristic algorithms, Luniver Press, London, 2008.Google Scholar

  • [29]

    X. S. Yang, Firefly algorithms for multimodal optimization, in: Stochastic algorithms: foundations and applications, vol. 5792, pp. 169–178, SAGA. Lecture Notes in Computer Sciences, Springer, Heidelberg, Berlin, 2009.Google Scholar

  • [30]

    F. Yücalar, D. Kilinc, E. Borandag and A. Ozcift, Regression analysis based software effort estimation method, Int. J. Softw. Eng. Knowledge Eng. 26 (2016) 807–826.CrossrefWeb of ScienceGoogle Scholar

About the article

Received: 2019-01-15

Accepted: 2019-05-22

Published Online: 2019-06-27

Citation Information: Journal of Intelligent Systems, 20190023, ISSN (Online) 2191-026X, ISSN (Print) 0334-1860, DOI: https://doi.org/10.1515/jisys-2019-0023.

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