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Improving a Model for NFR Estimation Using Band Classification and Selection with KNN

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Abstract

Any software development project needs to estimate non-functional requirements (NFR). Typically, software managers are forced to use expert judgment to estimate the NFR. Today, NFRs cannot be measured, as there is no standardized unit of measurement for them. Consequently, most estimation models focus on the functional user requirements (FUR) and do not consider the NFR in the estimation process because these terms are often subjective. The objective of this paper was to show how an NFR estimation model was created using fuzzy logic, and K-Nearest Neighbors classifier algorithm, aiming to consider the subjectivity embedded in NFR terms to solve a specific problem in a Mexican company. The proposed model was developed using a database with real projects from a Mexican company in the private sector. The results were beneficial and better than the initial model considering quality criteria like mean magnitude of relative error (MMRE), standard deviation of magnitude of relative error (SDMRE) and prediction level (Pred 25%). Additionally, the proposed approach allows the managers to identify quantitative elements related to NFR that could be used to interpret the data and build additional models.

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Notes

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  2. https://scikit-learn.org/

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Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

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Authors and Affiliations

  1. National Autonomous University of Mexico Science Faculty, CDMX, México, Mexico

    F. Valdés-Souto & D. Torres-Robledo

  2. SPINGERE, CDMX, México, Mexico

    J. Valeriano-Assem

Authors
  1. F. Valdés-Souto
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  2. J. Valeriano-Assem
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  3. D. Torres-Robledo
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Correspondence to F. Valdés-Souto, J. Valeriano-Assem or D. Torres-Robledo.

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APPENDIX A

APPENDIX A

Table 6. Test data set
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Valdés-Souto, F., Valeriano-Assem, J. & Torres-Robledo, D. Improving a Model for NFR Estimation Using Band Classification and Selection with KNN. Program Comput Soft 49, 822–831 (2023). https://doi.org/10.1134/S0361768823080236

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