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International Conference on Knowledge-Based and Intelligent Information and Engineering Systems

KES 2011: Knowlege-Based and Intelligent Information and Engineering Systems pp 315–324Cite as

Adjusted Case-Based Software Effort Estimation Using Bees Optimization Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6882))

Abstract

Case-Based Reasoning (CBR) has achieved a considerable interest from researchers for solving non-trivial or ill-defined problems such as those encountered by project managers including support for software project management in predictions and lesson learned. Software effort estimation is the key factor for successful software project management. In particular, the use of CBR for effort estimation was favored over regression and other machine learning techniques due to its performance in generating reliable estimates. However, this method was subject to variety of design options which therefore has strong impact on the prediction accuracy. Selection of CBR adjustment method and deciding on the number of analogies are such two important decisions for generating accurate and reliable estimates. This paper proposed a new method to adjust the retrieved project efforts and find optimal number of analogies by using Bees optimization algorithm. The Bees algorithm will be used to search for the best number of analogies and features coefficient values that will be used to reduce estimates errors. Results obtained are promising and the proposed method could form a useful extension for Case-based effort prediction model.

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, Applied Science University, Amman, Jordan, P.O. Box 166

    Mohammad Azzeh

Authors
  1. Mohammad Azzeh
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Editor information

Editors and Affiliations

  1. Institute of Integrated Sensor Systems, University of Kaiserslautern, Erwin-Schroedinger-str. 12, 67663, Kaiserslautern, Germany

    Andreas König

  2. Knowledge-Based Systems Group, Department of Computer Science, University of Kaiserslautern, P.O. Box 3049, 67653, Kaiserslautern, Germany

    Andreas Dengel

  3. School of Business, University of Applied Sciences Northwestern Switzerland, Riggenbachstr. 16, 4600, Olten, Switzerland

    Knut Hinkelmann

  4. Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, 599-8531, Sakai,, Osaka, Japan

    Koichi Kise

  5. KES International, P.O. Box 2115, BN43 9AF, Shoreham-by-sea, UK

    Robert J. Howlett

  6. University of South Australia, Adelaide, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2011 Springer-Verlag Berlin Heidelberg

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Azzeh, M. (2011). Adjusted Case-Based Software Effort Estimation Using Bees Optimization Algorithm. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowlege-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6882. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23863-5_32

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  • DOI: https://doi.org/10.1007/978-3-642-23863-5_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23862-8

  • Online ISBN: 978-3-642-23863-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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