Artificial Intelligence Methods For Optimization Of The Software Testing Process
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Artificial Intelligence Methods for Optimization of the Software Testing Process
Artificial Intelligence Methods for Optimization of the Software Testing Process: With Practical Examples and Exercises presents different AI-based solutions for overcoming the uncertainty found in many initial testing problems. The concept of intelligent decision making is presented as a multi-criteria, multi-objective undertaking. The book provides guidelines on how to manage diverse types of uncertainty with intelligent decision-making that can help subject matter experts in many industries improve various processes in a more efficient way. As the number of required test cases for testing a product can be large (in industry more than 10,000 test cases are usually created). Executing all these test cases without any particular order can impact the results of the test execution, hence this book fills the need for a comprehensive resource on the topics on the how's, what's and whys. To learn more about Elsevier's Series, Uncertainty, Computational Techniques and Decision Intelligence, please visit this link: https://www.elsevier.com/books-and-journals/book-series/uncertainty-computational-techniques-and-decision-intelligence - Presents one of the first empirical studies in the field, contrasting theoretical assumptions on innovations in a real industrial environment with a large set of use cases from developed and developing testing processes at various large industries - Explores specific comparative methodologies, focusing on developed and developing AI-based solutions - Serves as a guideline for conducting industrial research in the artificial intelligence and software testing domain - Explains all proposed solutions through real industrial case studies
Intelligent Assurance: Artificial Intelligence-Powered Software Testing in the Modern Development Lifecycle
Author: Partha Mohapatra
language: en
Publisher: Deep Science Publishing
Release Date: 2025-07-27
Traditional testing can’t match the speed and reliability demanded in modern software development and releases. This book explains the integration of Artificial Intelligence is transforming software testing—enabling smarter, faster, and more scalable quality assurance across the development lifecycle. This book demystifies the integration of AI, machine learning, and natural language processing into modern testing workflows. From automated test case generation and defect prediction to adaptive test maintenance and intelligent prioritization, it offers practical insights and real-world applications that empower QA teams to deliver higher-quality software with greater efficiency. Through a structured, hands-on approach, readers will explore AI-driven testing strategies, tools, and architectures that align with DevOps and agile practices. The book also delves into ethical considerations, challenges in AI adoption, and the future of autonomous testing agents. Whether you're a software tester, QA lead, DevOps engineer, or technology decision-maker, this book equips you with the knowledge to embrace AI-driven testing as a strategic advantage in delivering resilient, secure, and high-performance software.
Artificial Intelligence Methods For Software Engineering
Software is an integral part of our lives today. Modern software systems are highly complex and often pose new challenges in different aspects of Software Engineering (SE).Artificial Intelligence (AI) is a growing field in computer science that has been proven effective in applying and developing AI techniques to address various SE challenges.This unique compendium covers applications of state-of-the-art AI techniques to the key areas of SE (design, development, debugging, testing, etc).All the materials presented are up-to-date. This reference text will benefit researchers, academics, professionals, and postgraduate students in AI, machine learning and software engineering.Related Link(s)