Chetna Gupta – författare

Research and Evidence in Software Engineering: From Empirical Studies to Open Source Artifacts introduces advanced software engineering to software engineers, scientists, postdoctoral researchers, academicians, software consultants, management executives, doctoral students, and advanced level postgraduate computer science students.This book contains research articles addressing numerous software engineering research challenges associated with various software development-related activities, including programming, testing, measurements, human factors (social software engineering), specification, quality, program analysis, software project management, and more. It provides relevant theoretical frameworks, empirical research findings, and evaluated solutions addressing the research challenges associated with the above-mentioned software engineering activities.To foster collaboration among the software engineering research community, this book also reports datasets acquired systematically through scientific methods and related to various software engineering aspects that are valuable to the research community. These datasets will allow other researchers to use them in their research, thus improving the quality of overall research. The knowledge disseminated by the research studies contained in the book will hopefully motivate other researchers to further innovation in the way software development happens in real practice.

Research and Evidence in Software Engineering: From Empirical Studies to Open Source Artifacts introduces advanced software engineering to software engineers, scientists, postdoctoral researchers, academicians, software consultants, management executives, doctoral students, and advanced level postgraduate computer science students.This book contains research articles addressing numerous software engineering research challenges associated with various software development-related activities, including programming, testing, measurements, human factors (social software engineering), specification, quality, program analysis, software project management, and more. It provides relevant theoretical frameworks, empirical research findings, and evaluated solutions addressing the research challenges associated with the above-mentioned software engineering activities.To foster collaboration among the software engineering research community, this book also reports datasets acquired systematically through scientific methods and related to various software engineering aspects that are valuable to the research community. These datasets will allow other researchers to use them in their research, thus improving the quality of overall research. The knowledge disseminated by the research studies contained in the book will hopefully motivate other researchers to further innovation in the way software development happens in real practice.

The proceedings volume presents selected papers from the International Conference on Sustainability in Software Engineering & Business Information Management: Innovation & Applications (SSEBIM 2022) held in Olten, Switzerland from September 23-24, 2022. It includes research related to sustainability from both a business and technical point of view. From a business perspective, it not only addresses how to make the business operations more sustainable, but also considers factors such as human values, ethics, environment and responsibility of the businesses. From the technical perspective of software development companies, it focuses on sustainability in software engineering ranging from practices, tools, techniques and methods. The contributions reflect how software engineering teams exhibited pro-activeness in their approaches to lead to sustainable development of the software that is of highest quality and reliability.It is intended for a broad audience, including students, researchers and practitioners who work in software engineering and business information management fields.

This book presents the first practice-based account of Generative AI (GenAI) integration in postgraduate courses, structured across three approaches: course redesign and delivery, assessment redesign, and evaluation adaptations. The book introduces GenAI as a knowledge collaborator and reports the practical approaches, such as redesigning group activities into applied, research-driven tasks, providing detailed GenAI usage instructions, and requiring acknowledgement and reporting tools such as AI Contribution Forms, GenAI Integration Rubrics, and AI Contribution Assessment Forms, while adapting evaluation rubrics to ensure that the technology cannot act as a substitute for student work. Through guided experimentation in these areas, students were able to explore GenAI’s capabilities and limitations, reflect on appropriate and inappropriate use cases, and strengthen their digital literacy, reflective synthesis, and ethical awareness.A rigorous Monitoring & Evaluation (M&E) framework, built on a Theory of Change, pretested survey instruments, and detailed research protocols, was used to assess impacts on student knowledge, confidence, application, and awareness, both in the short term and the medium term. All instruments and procedures are included, making them directly reusable or adaptable by peers seeking to replicate or scale similar interventions in their courses.This book is a practical and methodological resource for educators and institutions, offering tested integration strategies, ready-to-use instruments, and a replicable evaluation framework to embed GenAI responsibly and effectively into academic practice while safeguarding academic integrity.