A comparative study of hybrid models of selective classification and dynamic selection of analogies for software development effort estimation

Gautam, Swarnima Singh; Singh, Vrijendra

doi:10.1109/iciinfs.2017.8300360

2017 IEEE International Conference on Industrial and Information Systems (ICIIS) 2017

DOI: 10.1109/iciinfs.2017.8300360

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A comparative study of hybrid models of selective classification and dynamic selection of analogies for software development effort estimation

Swarnima Singh Gautam

Vrijendra Singh

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Cited by 2 publications

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The state‐of‐the‐art in software development effort estimation

Gautam

Singh

2018

J Software Evolu Process

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The software developers and researchers have been facing difficulties regarding software development effort estimation (SDEE) since 1960s. Both overestimation and underestimation are problematic for future software development. The software engineering field is continuously adapting new technologies and development methodologies, so there is always a requirement to have an accurate SDEE method that can cater the needs of continually growing software industry. The major purpose of this state‐of‐the‐art review is to provide an additional insight of existing SDEE studies while considering five points of reference: techniques used to construct models, strengths and weaknesses of different models, availability of benchmark data sets, data set characteristics, generalization ability of models. We have performed a comprehensive review of SDEE studies published in the period 1981‐2016. We have defined a new scheme of categorizing existing SDEE models. We have found that a majority of available data sets do not include complete information of projects, which misleads the direction of research. To compare SDEE models, we recommend to use same data sets while focusing on specific aspects of accuracy as none of SDEE studies has yet been able to compare all the existing models over same data sets while considering same aspects of accuracy.

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The state‐of‐the‐art in software development effort estimation

Gautam

Singh

2018

J Software Evolu Process

Self Cite

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RETRACTED ARTICLE: Ensemble learning with recursive feature elimination integrated software effort estimation: a novel approach

Rao

2020

Evol. Intel.

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A comparative study of hybrid models of selective classification and dynamic selection of analogies for software development effort estimation

Cited by 2 publications

References 8 publications

The state‐of‐the‐art in software development effort estimation

The state‐of‐the‐art in software development effort estimation

RETRACTED ARTICLE: Ensemble learning with recursive feature elimination integrated software effort estimation: a novel approach

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