An experimental analysis of traditional machine learning techniques for classification of nonfunctional requirements

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Authors

Publication Details

Journal
International Journal of Grid and Utility Computing
Published
2025-01-01
DOI
https://doi.org/10.1504/ijguc.2025.10070436
Primary Topic
Software System Performance and Reliability
Type
article
Field-Weighted Citation Impact
0.00
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article

An experimental analysis of traditional machine learning techniques for classification of nonfunctional requirements

Laxman Singh, Anil Kumar Solanki, Devendra Kumar
International Journal of Grid and Utility Computing
Software System Performance and Reliability
article

An experimental analysis of traditional machine learning techniques for classification of nonfunctional requirements

Laxman Singh, Anil Kumar Solanki, Devendra Kumar
article en

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

International Journal of Grid and Utility ComputingVol. 1(1)
Reduced inequalities
Openalex Percentile: Top 94%
Software System Performance and Reliability
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