Preface
Writing for Computer Science is an introduction to doing and describing research.
For the most part the book is a discussion of good writing style and effective
research strategies, with a focus on the skills required of graduate research students.
Some of the material is accepted wisdom, some is controversial, and some are my
opinions. The book is intended to be comprehensive; it is broad rather than deep,
but, whi le some readers may be interested in exploring topics further, for most
readers this book should be sufficient.
The first edition of this book was almost entirely about writing. The second
edition, in response to reader feedback, and in response to issues that arose in my
own experiences as an advisor, researcher, and referee, was additionally about
research methods. Indeed, the two topics—writing about and doing research—are
not clearly separated: it is a small step from asking how do I write? to asking what
is it that I write about?
In this new edition, the third, I’ve further expanded the material on research
methods, as well as refining and extending the guidance on writing. There is a new
chapter, on professional communication beyond academia; the chapters on getting
started, reading, reviewing, hypotheses, experiments, and statistics have been
expanded and reorganized; and there is additional or new material on many topics,
including theses, posters, presentations, literature revie ws, measurement and vari-
ability, evidence, data, and common failings in papers. Every chapter has had some
revision, and reader feedback has again been importance in shaping changes. The
references have been removed; with so many excellent, up-to-date reading lists
available at the click of a search button, a static list seemed anachronistic. The
example slides have been dropped too; there are limits to the advice that can be
given on dynamic visual presen tations in a printed text book.
As in the earlier editions, the guidance on writing focuses on resear ch, but is
intended to be broadly applicable to general technical and professional communi-
cation. Likewise, the guidance on the practice of research has wider lessons; for
example, a practitioner trying a new algorithm or explaining to colleagues why one
solution is preferable to another should be confident that the arguments are built on
robust foundations . And, while this edition has a stronger emphasis on the doing of
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