Concept Vs. Instance:
A reader can become confused when a concept and an instance of it
are blurred. Common examples include: an algorithm and a particular
program that implements it, a programming language and a compiler, a
general abstraction and its particular implementation in a computer
system, a data structure and a particular instance of it in memory.Terminology For Concepts And Abstractions
When defining the terminology for a concept, be careful to decide
precisely how the idea translates to an implementation. Consider the
following discussion:VM systems include a concept known as an address space. The system
dynamically creates an address space when a program needs one, and
destroys an address space when the program that created the space
has finished using it. A VM system uses a small, finite number to
identify each address space. Conceptually, one understands that
each new address space should have a new identifier. However, if
a VM system executes so long that it exhausts all possible address
space identifiers, it must reuse a number.
The important point is that the discussion only makes sense because it
defines ``address space'' independently from ``address space identifier''.
If one expects to discuss the differences between a concept and its
implementation, the definitions must allow such a distinction.Knowledge Vs. Data
The facts that result from an experiment are called ``data''. The term
``knowledge'' implies that the facts have been analyzed, condensed, or
combined with facts from other experiments to produce useful information.Cause and Effect:
A dissertation must carefully separate cause-effect relationships from
simple statistical correlations. For example, even if all computer
programs written in Professor X's lab require more memory than the
computer programs written in Professor Y's lab, it may not have anything
to do with the professors or the lab or the programmers (e.g., maybe the
people working in professor X's lab are working on applications that
require more memory than the applications in professor Y's lab).Drawing Only Warranted Conclusions:
One must be careful to only draw conclusions that the evidence supports.
For example, if programs run much slower on computer A than on computer B,
one cannot conclude that the processor in A is slower than the processor
in B unless one has ruled out all differences in the computers' operating
systems, input or output devices, memory size, memory cache, or internal
bus bandwidth. In fact, one must still refrain from judgement unless one
has the results from a controlled experiment (e.g., running a set of
several programs many times, each when the computer is otherwise idle).
Even if the cause of some phenomenon seems obvious, one cannot draw a
conclusion without solid, supporting evidence.Commerce and Science:
In a scientific dissertation, one never draws conclusions about the
economic viability or commercial success of an idea/method, nor does
one speculate about the history of development or origins of an idea.
A scientist must remain objective about the merits of an idea independent
of its commercial popularity.
In particular, a scientist never assumes that commercial success is a
valid measure of merit (many popular products are neither well-designed
Thus, statements such as ``over four hundred vendors make products using
technique Y'' are irrelevant in a dissertation.Politics And Science:
A scientist avoids all political influence when assessing ideas.
Obviously, it should not matter whether government bodies, political
parties, religious groups, or other organizations endorse an idea. More
important and often overlooked, it does not matter whether an idea
originated with a scientist who has already won a Nobel prize or a
first-year graduate student. One must assess the idea independent of
the source.Canonical Organization:
In general, every dissertation must define the problem that motivated
the research, tell why that problem is important, tell what others have
done, describe the new contribution, document the experiments that
validate the contribution, and draw conclusions.
There is no canonical organization for a dissertation; each is unique.
However, novices writing a dissertation in the experimental areas of CS
may find the following example a good starting point:
Suggested Order For Writing:
- Chapter 1: Introduction
An overview of the problem; why it is important; a summary
of extant work and a statement of your hypothesis or specific
question to be explored. Make it readable by anyone.
- Chapter 2: Definitions
New terms only. Make the definitions precise, concise,
- Chapter 3: Conceptual Model
Describe the central concept underlying your work. Make
it a ``theme'' that ties together all your arguments. It should
provide an answer to the question posed in the introduction at
a conceptual level. If necessary, add another chapter to give
additional reasoning about the problem or its solution.
- Chapter 4: Experimental Measurements
Describe the results of experiments that provide
evidence in support of your thesis. Usually experiments
either emphasize proof-of-concept (demonstrating the
viability of a method/technique) or efficiency (demonstrating
that a method/technique provides better performance than
those that exist).
- Chapter 5: Corollaries And Consequences
Describe variations, extensions, or other applications of the
- Chapter 6: Conclusions
Summarize what was learned and how it can be applied.
Mention the possibilities for future research.
A short (few paragraphs) summary of the the dissertation.
Describe the problem and the research approach. Emphasize
the original contributions.
The easiest way to build a dissertation is inside-out. Begin by
writing the chapters that describe your research (3, 4, and 5 in the
above outline). Collect terms as they arise and keep a definition for
each. Define each technical term, even if you use it in a conventional
Organize the definitions into a separate chapter. Make the
definitions precise and formal. Review later chapters to verify that
each use of a technical term adheres to its definition.
After reading the middle chapters to verify terminology, write the
conclusions. Write the introduction next. Finally, complete an
abstract.Key To Success:
By the way, there is a key to success: practice. No one ever
learned to write by reading essays like this. Instead, you
need to practice, practice, practice. Every day.Parting thoughts:
We leave you with the following ideas to mull over. If they don't
mean anything to you now, revisit them after you finish wirting a
After great pain, a formal feeling comes.
-- Emily Dickinson
A man may write at any time, if he will set himself doggedly to it.
-- Samuel Johnson
Keep right on to the end of the road.
-- Harry Lauder
The average Ph.D. thesis is nothing but the transference of bones from one graveyard to another.