Ranking and Clustering Software Cost Estimation Models through a Multiple Comparisons Algorithm

Mittas, Nikolaos; Angelis, Lefteris

doi:10.1109/tse.2012.45

Cited by 185 publications

(134 citation statements)

References 41 publications

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“…Prediction (PRE), mean error relative (MER), (mean magnitude of error relative) MMER were used as selection criteria in this research. Mittas and Angelis (2013) ranked various effort estimation models by using different criteria as mean absolute error (MAE), magnitude of relative error (MRE) and mean magnitude of relative error (MMRE). Preeth et al (2014) used three selection criteria for the evaluation of effort estimation model such as magnitude of relative error (MRE), mean magnitude of relative error (MMRE) and prediction (PRED).…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-criteria decision making approach for the selection of software effort estimation model

Bansal¹,

Kumar²,

Garg³

2017

10.5267/j.msl

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Software development with minimum effort has become a challenging task for the software developers. Software effort may be defined as the prediction process of the effort required to develop any software. Many software effort estimation models have been developed in the past, but it is observed that none of them can be applied successfully in all kinds of projects in different environments that raise the problem of the software effort estimation model selection. To select the suitable software effort estimation model, many conflicting selection criteria must be considered in the decision process. The present study emphasizes on the development of a fuzzy multi-criteria decision making approach by integrating Fuzzy Set Theory and Weighted Distance Based Approximation. To show the consistency of the proposed approach, methodology validation is also performed by making comparison with existing methodologies and to check the criticality of the selection criterion, sensitivity analysis is also performed.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-criteria decision making approach for the selection of software effort estimation model

Bansal¹,

Kumar²,

Garg³

2017

10.5267/j.msl

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“…Replication of previous methods and Snott-Knott algorithm [45] were used to shortlist best out of available techniques for ensembles. The results obtained in this research work showed better estimation accuracy by adapting ensembles in comparison to solo estimation models and identified 15 best estimation ensembles [46].…”

Section: Literature Reviewmentioning

confidence: 99%

Effort Estimation Techniques for Web Application Development: A Review

Saif

2017

ijarcs

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In present era of information technology web applications have become an indispensable and most pivotal source to disseminate information across geographically distant nodes. Most of the organisation public or private have switched their existence from traditional software based systems to web based software systems. Many characteristic differences between traditional software and web based software application made web based existence more popular with wide range adoption. The sudden surge in web application usability across different organisation have posed a grave challenge for web development companies to deliver their products onetime and within budget with much expected accurately, efficiency and reliability. Effort estimation is main domain that decides the budgetary constraints associated with web application development. Accurate estimates are very essential for both developer and client. Accurate estimates make web development companies to maintain their reputation, competitive edge and client base. Inaccuracy in effort estimates results to lose revenue, client base and competitive strength. Effort estimation is very import for every development organisation to estimate the development work in early stages of development to prove efficient, accurate and much reliable budget estimates. This paper is an effort to explore various effort estimation techniques for web development, their challenges and measures to predict accurate efforts.

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“…A few examples such applications are: bioinformatics (Fortino et al, 2014), marketing (Nachev and Hogan, 2014), oceanography (Cortese et al, 2013), software project costs (Mittas and Angelis, 2013), and telemedicine (Romano et al, 2014).…”

Section: Regressionmentioning

confidence: 99%

A tutorial on using the rminer R package for data mining tasks

Cortez¹

2015

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This tutorial explores the rminer package of the R statistical tool. Following a learn by example approach, several code recipes are presented and the obtained results analyzed. The goal is to demonstrate the package capabilities for executing classification and regression data mining tasks, including in particular three CRISP-DM stages: data preparation, modeling and evaluation. The particular case of time series forecasting is also addressed.

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Ranking and Clustering Software Cost Estimation Models through a Multiple Comparisons Algorithm

Cited by 185 publications

References 41 publications

Multi-criteria decision making approach for the selection of software effort estimation model

Multi-criteria decision making approach for the selection of software effort estimation model

Effort Estimation Techniques for Web Application Development: A Review

A tutorial on using the rminer R package for data mining tasks

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