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A replicated assessment and comparison of adaptation techniques for analogy-based effort estimation

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Abstract

Variants of adaptation techniques have been proposed in previous studies to improve the performance of analogy-based effort estimation. The results of these studies are often contradictory and cannot simply be generalized because there are many uncontrollable source of variations between adaptation studies. The study presented in this paper has been carried out in order to replicate the assessment and comparison of different adaptation techniques utilised in analogy-based software effort prediction. Empirical evaluation of variants of adaptation techniques with Jack-knifing procedure have been carried out. Seven datasets come from PROMISE data repository were used for benchmarking. The results are also investigated within the presence/absence of feature subset selection algorithm. The current study permitted us to discover that linear adjustment approaches are more accurate than nonlinear adjustment because of the nature of the employed datasets that have, in most cases, normality characteristics.

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Notes

  1. All datasets used in this study are available on PROMISE website(http://www.promisedata.org)

  2. Local similarity degree can be measured based on single feature, while global can be measured by aggregating all local similarity degrees.

  3. \( {\text{PRED = }}100 \times (\lambda /n) \) is used to count the percentage of estimates that have magnitude relative error less than 25%., where \( \lambda \) is the number of projects where magnitude relative error less than or equal 25%., and n is the number of all

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Acknowledgement

The author is grateful to the Applied Science Private University, Amman, Jordan, for the financial support granted to this research project.

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  1. Department of Software Engineering, Applied Science University, Amman, Jordan, PO BOX 166

    Mohammad Azzeh

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  1. Mohammad Azzeh
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Correspondence to Mohammad Azzeh.

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Editors: Martin Shepperd and Tim Menzies

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Azzeh, M. A replicated assessment and comparison of adaptation techniques for analogy-based effort estimation. Empir Software Eng 17, 90–127 (2012). https://doi.org/10.1007/s10664-011-9176-6

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