Improving analogy-based software cost estimation by a resampling method

Mittas, Nikolaos; Athanasiades, Marinos; Angelis, Lefteris

doi:10.1016/j.infsof.2007.04.001

Cited by 47 publications

(36 citation statements)

References 12 publications

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“…They indicated that the adjusted estimation was significantly more accurate than EA without adjustment. Mittas et al [30] employed iterative resampling method to improve EA, they claimed that EA is closely related to formal nearest neighbor non parametric regression. On the other hand, Mendes et al [28,29] investigated the use of CBR and adaptation rules on data collected from web hypermedia projects.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy grey relational analysis for software effort estimation

2009

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Article:Azzeh, Mohammad, Neagu, Daniel and Cowling, Peter I. orcid.org/0000-0003-1310-6683 (2010) Fuzzy grey relational analysis for software effort estimation. Empirical Software Engineering. pp. 60-90. ISSN 1382-3256 https://doi.org/10.1007/s10664-009-9113-0 eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Fuzzy Grey Relational Analysis for Software Effort Estimation AbstractAccurate and credible software effort estimation is a challenge for academic research and software industry. From many software effort estimation models in existence, Estimation by Analogy (EA) is still one of the preferred techniques by software engineering practitioners because it mimics the human problem solving approach. Accuracy of such a model depends on the characteristics of the dataset, which is subject to considerable uncertainty. The inherent uncertainty in software attribute measurement has significant impact on estimation accuracy because these attributes are measured based on human judgment and are often vague and imprecise. To overcome this challenge we propose a new formal EA model based on the integration of Fuzzy set theory with Grey Relational Analysis (GRA). Fuzzy set theory is employed to reduce uncertainty in distance measure between two tuples at the k).GRA is a problem solving method that is used to assess the similarity between two tuples with M features. Since some of these features are not necessary to be continuous and may have nominal and ordinal scale type, aggregating different forms of similarity measures will increase uncertainty in the similarity degree. Thus the GRA is mainly used to reduce uncertainty in the distance measure between two software projects for both continuous and categorical features. Both techniques are suitable when relationship between effort and other effort drivers is complex. Experimental results showed that using integration of GRA with FL produced credible estimates when compared with the results obtained using Case-Based Reasoning, Multiple LinearRegression and Artificial Neural Networks methods.

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Section: Related Workmentioning

confidence: 99%

Fuzzy grey relational analysis for software effort estimation

2009

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“…EbA can be considered as a non-parametric model (Mittas et al, 2008) imposing no restrictions on the data, whereas the whole procedure is based on finding a few similar or neighbour projects to the one that has to be estimated after some calibration regarding the distance metric, number of closest neighbours, standardization etc.…”

Section: Methodology Of the Case Studymentioning

confidence: 99%

A permutation test based on regression error characteristic curves for software cost estimation models

Mittas

Angelis

2011

Empir Software Eng

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Background Regression Error Characteristic (REC) curves provide a visualization tool, able to characterize graphically the prediction power of alternative predictive models. Due to the benefits of using such a visualization description of the whole distribution of error, REC analysis was recently introduced in software cost estimation to aid the decision of choosing the most appropriate cost estimation model during the management of a forthcoming project. Aims Although significant information can be retrieved from a readable graph, REC curves are not able to assess whether the divergences between the alternative error functions can constitute evidence for a statistically significant difference. Method In this paper, we propose a graphical procedure that utilizes (a) the process of repetitive permutations and (b) and the maximum vertical deviation between two comparative Regression Error Characteristic curves in order to conduct a hypothesis test for assessing the statistical significance of error functions. Results In our case studies, the data used come from software projects and the models compared are cost prediction models. The results clearly showed that the proposed statistical test is necessary in order to assess the significance of the superiority of a prediction model, since it provides an objective criterion for the distances between the REC curves. Moreover, the procedure can be easily applied to any dataset where the objective is the prediction of a response variable of interest and the comparison of alternative prediction techniques in order to select the best strategy.Empir Software Eng (Conclusions The proposed hypothesis test, accompanying an informative graphical tool, is more easily interpretable than the conventional parametric and non-parametric statistical procedures. Moreover, it is free from normality assumptions of the error distributions when the samples are small-sized and highly skewed. Finally, the proposed graphical test can be applied to the comparisons of any alternative prediction methods and models and also to any other validation procedure.

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“…Cost estimation Bottom-up process [34,35] Top-down process [34] Feature-based model [36] Activity-based model [37,38] Case-based model [39,40] Process-based model [41,42] Operation-based model [33,43] Genetic-causal model [31] Tolerance-based model [44,45] Uncertainty involved model [46,47] Financial model [48] Parametric method [49,50] Analogous method [51,52] Fuzzy logic method [53,54] Neural networks method [55,56] Accounting method [57] Cost estimation model specific techniques -CAD/CAE techniques [36] -Activity chain analysis [37,38] -Case-based reasoning techniques [39,40] -Production process time analysis [41,42] -Operation process time, non-productive time, setup time analysis [33,43] -Causal relationship analysis [31] -Cost tolerance relationship analysis [44,45] -Stochastic analysis [46,47] Cost calculation method specific techniques -Regression parameterization techniques - [49,50] -Cost time analysis techniques …”

Section: Estimation Process Estimation Model Calculation Model Assistmentioning

confidence: 99%