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Case-Based Reasoning

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Software Project Effort Estimation

Abstract

Case-based reasoning represents a memory-based, data-driven estimation method. In other words, it is an estimation method in which estimates are based solely on the analysis of quantitative project data and in which the data need to be available at the time of estimation.

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Notes

  1. 1.

    Later version of the tool can also be found under the name ArchANGEL.

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Author information

Authors and Affiliations

  1. Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern, Germany

    Adam Trendowicz

  2. The University of New South Wales, Sydney, New South Wales, Australia

    Ross Jeffery

Authors
  1. Adam Trendowicz
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  2. Ross Jeffery
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Further Reading

Further Reading

  • S.J. Delany and P. Cunningham (2000), The Application of Case-Based Reasoning to Early Software Project Cost Estimation and Risk Assessment, Report TCD-CS-2000-10, Department of Computer Science, UCD School of Computer Science and Informatics, Dublin, Ireland.

    This report investigates the applicability of case-based reasoning for estimating software project effort in the early phases of the software development life cycle. The authors analyze the relevant strengths and weaknesses of CBR and discuss the issue of effort factors and their incomplete knowledge in the early stages of software development projects. They investigate the impact that missing and uncertain effort factors may have on the predictive performance of CBR.

  • E. Mendes, N. Mosley, and S. Counsell (2003), “Replicated Assessment of the Use of Adaptation Rules to Improve Web Cost Estimation,” Proceedings of the 2nd International Symposium on Empirical Software Engineering, September 29 – October 4, Rome, Italy.

    E. Mendes, S. Counsell, N. Mosley, “Do Adaptation Rules Improve Web Cost Estimation,” Proceedings of the 14th Conference on Hypertext and Hypermedia, Nottingham, UK, 2003, pp. 173–183.

    These two publications present an empirical comparison of various approaches for adapting analogs in the context of CBR effort estimation. Authors investigate the impact of various adaptation rules on the estimation accuracy of CBR. Additionally, they analyze the impact of applying feature subset selection on the predictive performance of CBR. Authors perform their study in the context of “messy” project data, that is, data containing outliers and collinearities.

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Trendowicz, A., Jeffery, R. (2014). Case-Based Reasoning. In: Software Project Effort Estimation. Springer, Cham. https://doi.org/10.1007/978-3-319-03629-8_11

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  • DOI: https://doi.org/10.1007/978-3-319-03629-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03628-1

  • Online ISBN: 978-3-319-03629-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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