This book teaches computer science and software engineering researchers how to design, conduct, and report empirical research within their discipline, including formulating a viable research question, selecting a method, applying ethics, analysing data, and defending the results in a thesis or paper.
Readers follow a linear structure of ten chapters, beginning with foundational concepts, research designs, and quality criteria. They continue through specific methods, literature reviews, and statistics, into organising and writing an academic thesis. This is followed by two chapters devoted to evaluating AI-assisted research and empirically studying AI-based systems. The final chapter gives a dedicated exploration of research ethics that addresses computing-specific dilemmas, including repository and forum data consent, algorithmic fairness trade-offs, and the ethics of using AI in one's own research. Authors provide an extensive glossary of research-methods terminology tailored to computer science and software engineering. Each method and example is drawn from computing practice, not adapted from the social sciences, and more than thirty original decision-tree diagrams translate abstract methodological choices (such as picking a research design or a statistical test) into concrete, actionable guidance.
Written for PhD students, master's and bachelor's candidates, and early-career researchers in computer science and software engineering, the book equally suits supervisors and instructors looking for a single, computing-specific reference to recommend across a research methods course.