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 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.

Improve Your Creativity, Effectiveness, and Ultimately, Your Code In Modern Software Engineering, continuous delivery pioneer David Farley helps software professionals think about their work more effectively, manage it more successfully, and genuinely improve the quality of their applications, their lives, and the lives of their colleagues. Writing for programmers, managers, and technical leads at all levels of experience, Farley illuminates durable principles at the heart of effective software development. He distills the discipline into two core exercises: learning and exploration and managing complexity. For each, he defines principles that can help you improve everything from your mindset to the quality of your code, and describes approaches proven to promote success. Farley's ideas and techniques cohere into a unified, scientific, and foundational approach to solving practical software development problems within realistic economic constraints. This general, durable, and pervasive approach to software engineering can help you solve problems you haven't encountered yet, using today's technologies and tomorrow's. It offers you deeper insight into what you do every day, helping you create better software, faster, with more pleasure and personal fulfillment. Clarify what you're trying to accomplish Choose your tools based on sensible criteria Organize work and systems to facilitate continuing incremental progress Evaluate your progress toward thriving systems, not just more "legacy code" Gain more value from experimentation and empiricism Stay in control as systems grow more complex Achieve rigor without too much rigidity Learn from history and experience Distinguish "good" new software development ideas from "bad" ones Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Skills to grow from a solo coder into a productive member of a software development team, with seasoned advice on everything from refactoring to acing an interview. In Skills of a Successful Software Engineer you will learn: The skills you need to succeed on a software development team Best practices for writing maintainable code Testing and commenting code for others to read and use Refactoring code you didn’t write What to expect from a technical interview process How to be a tech leader Getting around gatekeeping in the tech community Skills of a Successful Software Engineer is a best practices guide for succeeding on a software development team. The book reveals how to optimize both your code and your career, from achieving a good work-life balance to writing the kind of bug-free code delivered by pros. You’ll master essential skills that you might not have learned as a solo coder, including meaningful code commenting, unit testing, and using refactoring to speed up feature delivery. Timeless advice on acing interviews and setting yourself up for leadership will help you throughout your career. Crack open this one-of-a-kind guide, and you’ll soon be working in the professional manner that software managers expect. About the technology Success as a software engineer requires technical knowledge, flexibility, and a lot of persistence. Knowing how to work effectively with other developers can be the difference between a fulfilling career and getting stuck in a life-sucking rut. This brilliant book guides you through the essential skills you need to survive and thrive on a software engineering team. About the book Skills of a Successful Software Engineer presents techniques for working on software projects collaboratively. In it, you’ll build technical skills, such as writing simple code, effective testing, and refactoring, that are essential to creating software on a team. You’ll also explore soft skills like how to keep your knowledge up to date, interacting with your team leader, and even how to get a job you’ll love. What's inside Best practices for writing and documenting maintainable code Testing and refactoring code you didn’t write What to expect in a technical interview How to thrive on a development team About the reader For working and aspiring software engineers. About the author Fernando Doglio has twenty years of experience in the software industry, where he has worked on everything from web development to big data. Table of Contents 1 Becoming a successful software engineer 2 Writing code everyone can read 3 Unit testing: delivering code that works 4 Refactoring existing code (or Refactoring doesn’t mean rewriting code) 5 Tackling the personal side of coding 6 Interviewing for your place on the team 7 Working as part of a team 8 Understanding team leadership

This book is about software product lines (SPLs) designed and developed taking UML diagrams as the primary basis, modeled according to a rigorous approach composed of an UML profile and a systematic process for variability management activities, forming the Stereotype-based Management of Variability (SMarty) approach. The book consists of five parts. Part I provides essential concepts on SPL in terms of the first development methodologies. It also introduces variability concepts and discusses SPL architectures finishing with the SMarty approach. Part II is focused on the design, verification and validation of SMarty SPLs, and Part III concentrates on the SPL architecture evolution based on ISO/IEC metrics, the SystEM-PLA method, optimization with the MOA4PLA method, and feature interaction prevention. Next, Part IV presents SMarty as a basis for SPL development, such as, the M-SPLearning SPL for mobile learning applications, the PLeTs SPL for testing tools, the PlugSPL plugin environment for supporting the SPL life cycle, the SyMPLES approach for designing embedded systems with SysML, the SMartySPEM approach for software process lines (SPrL), and re-engineering of class diagrams into an SPL. Eventually, Part V promotes controlled experimentation in UML-based SPLs, presenting essential concepts on how to plan, conduct, and document experiments, as well as showing several experiments carried out with SMarty. This book aims at lecturers, graduate students and experienced practitioners. Lecturers might use the book for graduate level courses about SPL fundamentals and tools; students will learn about the SPL engineering process, variability management, and mass customization; and practitioners will see how to plan the transition from single-product development to an SPL-based process, how to document inherent variability in a given domain, or how to apply controlled experiments to SPLs.

This open access book provides a basic introduction to feature modelling and analysis as well as to the integration of AI methods with feature modelling. It is intended as an introduction for researchers and practitioners who are new to the field and will also serve as a state-of-the-art reference to this audience. While focusing on the AI perspective, the book covers the topics of feature modelling (including languages and semantics), feature model analysis, and interacting with feature model configurators. These topics are discussed along the AI areas of knowledge representation and reasoning, explainable AI, and machine learning.

This book unveils the most advanced techniques and innovative applications in the natural language processing (NLP) field. It uncovers the secrets to enhancing language understanding, and presents practical solutions to different NLP tasks, as text augmentation, paraphrase generation, and restoring spaces and punctuation in multiple languages. It unlocks the potential of hierarchical multi-task learning for cross-lingual phoneme recognition, and allows readers to explore more real-world applications such as error correction, aggregating industrial security findings as well as predicting music emotion values from social media conversations. "Practical Solutions for Diverse Real-World NLP Applications" is the suitable guidebook for researchers, students, and practitioners as it paves the way for them by delivering invaluable insights and knowledge.