Analysis design framework software di khan tabrez (6 risultati)

Softcover/Taschenbuch. Condizione: 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. …

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Taschenbuch. Condizione: Neu. Analysis and Design of a Framework for Software Design Risk Prediction | Tabrez Khan (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630474152 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…

Paperback. Condizione: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …