How To Write A Dissertation - Page 3

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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 nor well-engineered). 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:

• 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, and unambiguous.

• 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 central idea.

• Chapter 6: Conclusions

  Summarize what was learned and how it can be applied. Mention the possibilities for future research.

• Abstract:

  A short (few paragraphs) summary of the the dissertation. Describe the problem and the research approach. Emphasize the original contributions.

Suggested Order For Writing:

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 manner.

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 dissertation.

After great pain, a formal feeling comes.