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Data-Driven Framework for Adaptability Predictions Using Object-Oriented Metrics

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Bibliographic Details
Title: Data-Driven Framework for Adaptability Predictions Using Object-Oriented Metrics
Authors: Obike G. Peter, Edward, N. Udo
Source: Covenant Journal of Informatics and Communication Technology; CJICT: VOL. 12, NO. 2, December 2024 ; 2354-3507 ; 2354-3566
Publisher Information: Covenant University
Publication Year: 2024
Collection: Covenant Journals (Covenant University)
Subject Terms: software adaptability level, Random Forest, object-oriented software metrics, software design, software quality
Description: Software adaptability is a critical attribute in modern systems, enabling them to evolve with changing requirements and environments. Existing approaches to predicting adaptability often rely on subjective assessments or high-level architectural metrics, which may lack precision and scalability. This study aims to enhance adaptability prediction by integrating object-oriented metrics and machine learning models, addressing limitations of traditional methods. Decision Trees and Random Forests were employed to model relationships between object-oriented design metrics such as Coupling Between Objects, Lack of Cohesion in Methods, Depth of Inheritance Tree, and Number of Children and adaptability. A comparative analysis using Guava and Apache Commons Lang datasets revealed that the Random Forest model outperforms Decision Trees, achieving an F1-score of 0.9760 and a ROC AUC of 0.9858, highlighting its accuracy and feature importance. Key metrics like coupling and cohesion emerged as pivotal for adaptability prediction. This study contributes a robust, data-driven framework for adaptability prediction, offering valuable insights to developers for creating flexible and maintainable systems. These advancements improve software design practices, ensuring resilience and relevance of applications in dynamic technological landscapes.
Document Type: article in journal/newspaper
File Description: application/vnd.openxmlformats-officedocument.wordprocessingml.document
Language: English
Relation: https://journals.covenantuniversity.edu.ng/index.php/cjict/article/view/4859/1750; https://journals.covenantuniversity.edu.ng/index.php/cjict/article/view/4859
Availability: https://journals.covenantuniversity.edu.ng/index.php/cjict/article/view/4859
Accession Number: edsbas.440EFB05
Database: BASE
Description
Abstract:Software adaptability is a critical attribute in modern systems, enabling them to evolve with changing requirements and environments. Existing approaches to predicting adaptability often rely on subjective assessments or high-level architectural metrics, which may lack precision and scalability. This study aims to enhance adaptability prediction by integrating object-oriented metrics and machine learning models, addressing limitations of traditional methods. Decision Trees and Random Forests were employed to model relationships between object-oriented design metrics such as Coupling Between Objects, Lack of Cohesion in Methods, Depth of Inheritance Tree, and Number of Children and adaptability. A comparative analysis using Guava and Apache Commons Lang datasets revealed that the Random Forest model outperforms Decision Trees, achieving an F1-score of 0.9760 and a ROC AUC of 0.9858, highlighting its accuracy and feature importance. Key metrics like coupling and cohesion emerged as pivotal for adaptability prediction. This study contributes a robust, data-driven framework for adaptability prediction, offering valuable insights to developers for creating flexible and maintainable systems. These advancements improve software design practices, ensuring resilience and relevance of applications in dynamic technological landscapes.