Artificial Intelligence in Software Engineering

ISBN 10: 3725877211 / ISBN 13: 9783725877218

Published by MDPI AG, 2026

About this Item

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.

Seller Inventory # 135468242

Bibliographic Details

• Title: Artificial Intelligence in Software Engineering
• Publisher: MDPI AG
• Publication Date: 2026
• Language: English
• ISBN 10: 3725877211
• ISBN 13: 9783725877218
• Binding: Buch
• Condition: Neu
• Dimensions: 250W x 175H x 21D millimeters 250 millimeters width by 175 millimeters height by 21 millimeters depth
• Weight: 835 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.