Improving a Model for NFR Estimation Classifying Equal Size Bands with KNN
https://doi.org/10.15514/ISPRAS-2023-35(6)-2
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.
About the Authors
Francisco VALDÉS-SOUTO
National Autonomous University of Mexico Science Faculty
Mexico
Mexico
Doctor in Software Engineering with a specialty in Software Measurement and Estimation at the École de Technologie Supérieure (ETS) in Canada, two master's degrees in Mexico and France. President of COSMIC. Associate Professor of the Faculty of Sciences of the National Autonomous University of Mexico (UNAM). Founder of the Mexican Association of Software Metrics (AMMS). More than 25 years of experience in critical software development. He currently has more than 50 publications including articles in Indexed Journals, Proceedings, books and book chapters. He is the main promoter of the topic of formal software metrics in Mexico, promoting COSMIC (ISO/IEC 19761) as a National Standard. Member of the National System of Researchers (SNI). Research interests: software measurement and estimation applied to software project management, scope management, productivity and economics in software projects.
Jorge VALERIANO-ASSEM
SPINGERE
Mexico
Mexico
Master in Computer Science and Engineering from the National Autonomous University of Mexico, specialist consultant in formal software measurement and estimation since 2016. Areas of interest: Software metrics (COSMIC), Software estimation models, Software Validation Models, Estimation of Functional and Non-Functional Requirements, Evaluation of the Performance of Software Development Projects aligned to Software Metrics, Evaluation of the Quality of the Software Development Product.
Daniel TORRES-ROBLEDO
National Autonomous University of Mexico Science Faculty
Mexico
Mexico
Master student at Research Institute in Applied Mathematics and Systems, degree in Computer Science from Science Faculty of the UNAM.
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Review
For citations:
VALDÉS-SOUTO F., VALERIANO-ASSEM J., TORRES-ROBLEDO D. Improving a Model for NFR Estimation Classifying Equal Size Bands with KNN. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2023;35(6):29-42. (In Russ.) https://doi.org/10.15514/ISPRAS-2023-35(6)-2
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