Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/10056
Title: Feature weighting techniques for CBR in software effort estimation studies: A review and empirical evaluation
Authors: Sigweni, B
Shepperd, M
Keywords: Case-based reasoning;Feature subset selection;Feature weighting;Software effort estimation
Issue Date: 2014
Publisher: Association for Computing Machinery
Citation: PROMISE '14 Proceedings of the 10th International Conference on Predictive Models in Software Engineering: 32 - 41, (2014)
Abstract: Context : Software effort estimation is one of the most important activities in the software development process. Unfortunately, estimates are often substantially wrong. Numerous estimation methods have been proposed including Case-based Reasoning (CBR). In order to improve CBR estimation accuracy, many researchers have proposed feature weighting techniques (FWT). Objective: Our purpose is to systematically review the empirical evidence to determine whether FWT leads to improved predictions. In addition we evaluate these techniques from the perspectives of (i) approach (ii) strengths and weaknesses (iii) performance and (iv) experimental evaluation approach including the data sets used. Method: We conducted a systematic literature review of published, refereed primary studies on FWT (2000-2014). Results: We identified 19 relevant primary studies. These reported a range of different techniques. 17 out of 19 make benchmark comparisons with standard CBR and 16 out of 17 studies report improved accuracy. Using a one-sample sign test this positive impact is significant (p = 0:0003). Conclusion: The actionable conclusion from this study is that our review of all relevant empirical evidence supports the use of FWTs and we recommend that researchers and practitioners give serious consideration to their adoption.
URI: http://dl.acm.org/citation.cfm?doid=2639490.2639508
http://bura.brunel.ac.uk/handle/2438/10056
DOI: http://dx.doi.org/10.1145/2639490.2639508
ISBN: 9781450328982
Appears in Collections:Dept of Computer Science Research Papers

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