9783725877218 - Artificial Intelligence in Software Engineering (7 resultados)

Condición: New.

Hardcover. Condición: new. Hardcover. The rapid advancement of Artificial Intelligence (AI) has profoundly reshaped software engineering (SE). As software systems grow in scale and complexity, traditional methods often struggle to meet demands for speed, adaptability, and reliability. This reprint explores how AI technologies-including machine learning (ML) and natural language processing (NLP)-are revolutionizing the entire SE lifecycle. This Reprint brings together high-quality research demonstrating how tailored AI methodologies reduce human error, improve efficiency, and drive continuous improvement across key development phases: 1. Requirements & Design: Utilizing NLP to analyze stakeholder inputs, and leveraging AI models to automate coding, generate optimized designs, and suggest architectural improvements. 2. Testing & Quality Assurance: Applying ML to detect anomalies, predict failure-prone components, generate robust test cases, and optimize resource allocation. 3. Deployment & Management: Using predictive analytics to foresee risks and accurately estimate timelines, alongside AI-driven CI/CD pipelines for adaptive, autonomous software delivery. 4. Autonomous Maintenance: Highlighting self-healing mechanisms and predictive analytics that minimize downtime and extend software longevity.Featuring theoretical and experimental studies, novel frameworks, and comprehensive surveys, this issue bridges the gap between AI research and SE practice. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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

Hardcover. Condición: new. Hardcover. The rapid advancement of Artificial Intelligence (AI) has profoundly reshaped software engineering (SE). As software systems grow in scale and complexity, traditional methods often struggle to meet demands for speed, adaptability, and reliability. This reprint explores how AI technologies-including machine learning (ML) and natural language processing (NLP)-are revolutionizing the entire SE lifecycle. This Reprint brings together high-quality research demonstrating how tailored AI methodologies reduce human error, improve efficiency, and drive continuous improvement across key development phases: 1. Requirements & Design: Utilizing NLP to analyze stakeholder inputs, and leveraging AI models to automate coding, generate optimized designs, and suggest architectural improvements. 2. Testing & Quality Assurance: Applying ML to detect anomalies, predict failure-prone components, generate robust test cases, and optimize resource allocation. 3. Deployment & Management: Using predictive analytics to foresee risks and accurately estimate timelines, alongside AI-driven CI/CD pipelines for adaptive, autonomous software delivery. 4. Autonomous Maintenance: Highlighting self-healing mechanisms and predictive analytics that minimize downtime and extend software longevity.Featuring theoretical and experimental studies, novel frameworks, and comprehensive surveys, this issue bridges the gap between AI research and SE practice. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condición: new. Hardcover. The rapid advancement of Artificial Intelligence (AI) has profoundly reshaped software engineering (SE). As software systems grow in scale and complexity, traditional methods often struggle to meet demands for speed, adaptability, and reliability. This reprint explores how AI technologies-including machine learning (ML) and natural language processing (NLP)-are revolutionizing the entire SE lifecycle. This Reprint brings together high-quality research demonstrating how tailored AI methodologies reduce human error, improve efficiency, and drive continuous improvement across key development phases: 1. Requirements & Design: Utilizing NLP to analyze stakeholder inputs, and leveraging AI models to automate coding, generate optimized designs, and suggest architectural improvements. 2. Testing & Quality Assurance: Applying ML to detect anomalies, predict failure-prone components, generate robust test cases, and optimize resource allocation. 3. Deployment & Management: Using predictive analytics to foresee risks and accurately estimate timelines, alongside AI-driven CI/CD pipelines for adaptive, autonomous software delivery. 4. Autonomous Maintenance: Highlighting self-healing mechanisms and predictive analytics that minimize downtime and extend software longevity.Featuring theoretical and experimental studies, novel frameworks, and comprehensive surveys, this issue bridges the gap between AI research and SE practice. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The rapid advancement of Artificial Intelligence (AI) has profoundly reshaped software engineering (SE). As software systems grow in scale and complexity, traditional methods often struggle to meet demands for speed, adaptability, and reliability. This reprint explores how AI technologies-including machine learning (ML) and natural language processing (NLP)-are revolutionizing the entire SE lifecycle. This Reprint brings together high-quality research demonstrating how tailored AI methodologies reduce human error, improve efficiency, and drive continuous improvement across key development phases: 1. Requirements & Design: Utilizing NLP to analyze stakeholder inputs, and leveraging AI models to automate coding, generate optimized designs, and suggest architectural improvements. 2. Testing & Quality Assurance: Applying ML to detect anomalies, predict failure-prone components, generate robust test cases, and optimize resource allocation. 3. Deployment & Management: Using predictive analytics to foresee risks and accurately estimate timelines, alongside AI-driven CI/CD pipelines for adaptive, autonomous software delivery. 4. Autonomous Maintenance: Highlighting self-healing mechanisms and predictive analytics that minimize downtime and extend software longevity.Featuring theoretical and experimental studies, novel frameworks, and comprehensive surveys, this issue bridges the gap between AI research and SE practice.

Buch. Condición: Neu. Artificial Intelligence in Software Engineering | Buch | Englisch | 2026 | MDPI AG | EAN 9783725877218 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.