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Inhaltsangabe
This book presents an intelligent framework for identifying, predicting, prioritizing, and estimating software risks during the design phase of the Software Development Life Cycle (SDLC). Software design is exposed to diverse risks arising from requirements, architectural decisions, design activities, programming technologies, validation, and documentation, which can adversely affect software quality, cost, and delivery schedules.The book systematically examines the characteristics, sources, and causes of software design risks and investigates the relationships among identified risk factors. It presents a Bayesian Network-based Risk Prediction Model (RPM) for predicting potential risks, supported by risk trees and graphical analysis. A Fuzzy Inference System (FIS) is employed to prioritize risks according to their severity, detectability, and dependability. Furthermore, an Extreme Learning Machine (ELM)-based approach is presented for risk estimation and impact analysis.
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Über die Autorin bzw. den Autor
Tabrez Khan is working as an assistant professor at Era University in Lucknow. Mohammad Faisal is working as Prof & HoD in the Department of Computer Application, Integral University, Lucknow.
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Analysis and Design of a Framework for Software Design Risk Prediction
Verlag:
LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6630474155
ISBN 13: 9786630474152
Neu
Softcover/Taschenbuch
Anbieter: Home-set, Mannheim, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Softcover/Taschenbuch. Zustand: Neu. This book presents an intelligent framework for identifying, predicting, prioritizing, and estimating software risks during the design phase of the Software Development Life Cycle (SDLC). Software design is exposed to diverse risks arising from requirements, architectural decisions, design activities, programming technologies, validation, and documentation, which can adversely affect software quality, cost, and delivery schedules.The book systematically examines the characteristics, sources, and causes of software design risks and investigates the relationships among identified risk factors. It presents a Bayesian Network-based Risk Prediction Model (RPM) for predicting potential risks, supported by risk trees and graphical analysis. A Fuzzy Inference System (FIS) is employed to prioritize risks according to their severity, detectability, and dependability. Furthermore, an Extreme Learning Machine (ELM)-based approach is presented for risk estimation and impact analysis. Sprache: Englisch Gewicht in Gramm: 358. Artikel-Nr. IB-489146
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