You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

Artificial Intelligence Methods for Optimization of the Software Testing Process With Practical Examples and Exercises

Publication Type:

Book

Publisher:

Elsevier


Abstract

This book provides several Artificial Intelligence (AI) approaches for optimizing the testing process from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. The lack of available data is one of the obstacles to implementing AI in the daily use industry. Therefore, in this book, we provide several industrial use cases collected from two large companies in Sweden: Ericsson AB and Alstom AB Sweden. Moreover, applying the proposed AI-based solutions in this book to both telecommunication use cases at Ericsson, and also safety-critical system cases at Alstom, can help us overcome the problem of feasibility and generalizability. The conducted case studies in this book cover industrial data gathering, utilizing different artificial intelligence and machine learning models, and also how we can deal with small datasets. However, large datasets might cause other problems for the usage of advanced machine learning models. Inaccurate data, noises, and duplicates are some of the issues that we faced during our research. In this regard, this book provides several strategies to deal with large datasets in industries as well. The topics covered in this book include decision making, optimization under uncertainty, data preparation and feature engineering, dimensional reduction for high-dimensional data, semantic and syntactic analysis, dealing with an imbalanced dataset, cluster and classification analysis, understanding data with visualization, AI platforms, and benefits of utilizing artificial intelligence in the software testing process.

Bibtex

@book{Tahvili6414,
author = {Sahar Tahvili and Leo Hatvani},
title = {Artificial Intelligence Methods for Optimization of the Software Testing Process With Practical Examples and Exercises},
isbn = {978-0323919135},
editor = {Elsevier},
month = {July},
year = {2022},
publisher = {Elsevier},
url = {http://www.es.mdh.se/publications/6414-}
}