This book summarizes the research findings presented at the 13th International Joint Conference on Knowledge-Based Software Engineering (JCKBSE 2020), which took place on August 24–26, 2020. JCKBSE 2020 was originally planned to take place in Larnaca, Cyprus. Unfortunately, the COVID-19 pandemic forced it be rescheduled as an online conference. JCKBSE is a well-established, international, biennial conference that focuses on the applications of artificial intelligence in software engineering. The 2020 edition of the conference was organized by Hiroyuki Nakagawa, Graduate School of Information Science and Technology, Osaka University, Japan, and George A. Tsihrintzis and Maria Virvou, Department of Informatics, University of Piraeus, Greece. This research book is a valuable resource for experts and researchers in the field of (knowledge-based) software engineering, as well as general readers in the fields of artificial and computational Intelligence and, more generally, computer science wanting to learn more about the field of (knowledge-based) software engineering and its applications. An extensive list of bibliographic references at the end of each paper helps readers to probe further into the application areas of interest to them.

This book contains extended versions of the works and new research results presented at the 14th International Joint Conference on Knowledge-based Software Engineering (JCKBSE2022). JCKBSE2022 was originally planned to take place in Larnaca, Cyprus. Unfortunately, the COVID-19 pandemic forced it to be rescheduled as an online conference. JCKBSE is a well-established international biennial conference that focuses on the applications of Artificial Intelligence on Software Engineering. The 14th International Joint Conference on Knowledge-based Software Engineering (JCKBSE2022) was organized by the Department of Informatics of the University of Piraeus, Greece. This book is a valuable resource for experts and researchers in the field of (knowledge-based) software engineering. It is also valuable to general readers in the fields of artificial and computational intelligence and, more generally, computer science, wishing to learn more about the exciting field of (knowledge-based) software engineering and its applications. An extensive list of bibliographic references at the end of each chapter helps readers to probe deeper into the application areas of interest to them.

In the Guide to the Software Engineering Body of Knowledge (SWEBOK(R) Guide), the IEEE Computer Society establishes a baseline for the body of knowledge for the field of software engineering, and the work supports the Society's responsibility to promote the advancement of both theory and practice in this field. It should be noted that the Guide does not purport to define the body of knowledge but rather to serve as a compendium and guide to the knowledge that has been developing and evolving over the past four decades. Now in Version 3.0, the Guide's 15 knowledge areas summarize generally accepted topics and list references for detailed information. The editors for Version 3.0 of the SWEBOK(R) Guide are Pierre Bourque (Ecole de technologie superieure (ETS), Universite du Quebec) and Richard E. (Dick) Fairley (Software and Systems Engineering Associates (S2EA)).

This book presents innovative and high-quality research on the implementation of conceptual frameworks, strategies, techniques, methodologies, informatics platforms and models for developing advanced knowledge-based systems and their application in different fields, including Agriculture, Education, Automotive, Electrical Industry, Business Services, Food Manufacturing, Energy Services, Medicine and others. Knowledge-based technologies employ artificial intelligence methods to heuristically address problems that cannot be solved by means of formal techniques. These technologies draw on standard and novel approaches from various disciplines within Computer Science, including Knowledge Engineering, Natural Language Processing, Decision Support Systems, Artificial Intelligence, Databases, Software Engineering, etc. As a combination of different fields of Artificial Intelligence, the area of Knowledge-Based Systems applies knowledge representation, case-based reasoning, neural networks, Semantic Web and TICs used in different domains. The book offers a valuable resource for PhD students, Master’s and undergraduate students of Information Technology (IT)-related degrees such as Computer Science, Information Systems and Electronic Engineering.

This volume constitutes the revised selected papers from the three workshops collocated with the 18th International Conference on Software Engineering and Formal Methods, SEFM 2020, held in Amsterdam, The Netherlands, in September 2020. The 15 full papers presented together with 8 short papers in this volume were carefully reviewed and selected from a total of 35 submissions. The contributions that are collected in this volume have been selected from the presentations at the following workshops: ASYDE 2020: Second International Workshop on Automated and Verifiable Software System Development; CIFMA 2020: Second International Workshop on Cognition: Interdisciplinary Foundations, Models and Applications; and CoSim-CPS 2020: Fourth International Workshop on Formal Co-Simulation of Cyber-Physical Systems. Due to the Corona pandemic this event was held virtually.

This book, Innovative Trends in Personalized Software Engineering and Information Systems – The Case of Intelligent and Adaptive E-learning Systems, studies the development of personalized and knowledge-based systems with the purpose of developing fully-automated adaptive software, mainly focusing on enhancing digital learning. The authors cover several integral phases of software engineering and their application in the effective implementation of sophisticated learning technology systems. Systems development and systems evaluation, tailored to adaptive e-learning, are examined in depth. More specifically, intelligent and knowledge-based techniques, such as artificial neural networks, fuzzy logic, genetic algorithms, pattern recognition, learning analytics and data mining, as well as evaluation frameworks adapted to digital learning software are presented. This publication will be of interest to scientists from various disciplines, including computer science, artificial intelligence, education and psychology, and will help researchers working in computer science and engineering to build intelligent and adaptive learning technology systems by employing personalization techniques while following the basic concepts of software engineering and information systems. It will also serve as a valuable tool for junior and senior scientists, supporting their research activities in the field of personalized and knowledge-based software engineering, and instructors will be able to use the book as a guide to the effective design and development of intelligent systems in education.