Feature weighting techniques for CBR in software effort estimation studies: A review and empirical evaluation

Boyce Sigweni, Martin Shepperd

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

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.

Original languageEnglish
Title of host publication10th International Conference on Predictive Models in Software Engineering, PROMISE 2014
PublisherAssociation for Computing Machinery
Pages32-41
Number of pages10
ISBN (Print)9781450328982
DOIs
Publication statusPublished - Jan 1 2014
Event10th International Conference on Predictive Models in Software Engineering, PROMISE 2014 - Turin, Italy
Duration: Sep 17 2014Sep 17 2014

Other

Other10th International Conference on Predictive Models in Software Engineering, PROMISE 2014
CountryItaly
CityTurin
Period9/17/149/17/14

Fingerprint

Case based reasoning
Software engineering

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Sigweni, B., & Shepperd, M. (2014). Feature weighting techniques for CBR in software effort estimation studies: A review and empirical evaluation. In 10th International Conference on Predictive Models in Software Engineering, PROMISE 2014 (pp. 32-41). Association for Computing Machinery. https://doi.org/10.1145/2639490.2639508
Sigweni, Boyce ; Shepperd, Martin. / Feature weighting techniques for CBR in software effort estimation studies : A review and empirical evaluation. 10th International Conference on Predictive Models in Software Engineering, PROMISE 2014. Association for Computing Machinery, 2014. pp. 32-41
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Sigweni, B & Shepperd, M 2014, Feature weighting techniques for CBR in software effort estimation studies: A review and empirical evaluation. in 10th International Conference on Predictive Models in Software Engineering, PROMISE 2014. Association for Computing Machinery, pp. 32-41, 10th International Conference on Predictive Models in Software Engineering, PROMISE 2014, Turin, Italy, 9/17/14. https://doi.org/10.1145/2639490.2639508

Feature weighting techniques for CBR in software effort estimation studies : A review and empirical evaluation. / Sigweni, Boyce; Shepperd, Martin.

10th International Conference on Predictive Models in Software Engineering, PROMISE 2014. Association for Computing Machinery, 2014. p. 32-41.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Sigweni B, Shepperd M. Feature weighting techniques for CBR in software effort estimation studies: A review and empirical evaluation. In 10th International Conference on Predictive Models in Software Engineering, PROMISE 2014. Association for Computing Machinery. 2014. p. 32-41 https://doi.org/10.1145/2639490.2639508