This page contains guidelines for writing a Master's or Bachelor's Thesis at the Chair of Algorithms and Data Structures. Here is long list of example theses which have already been completed at our chair. Note that these theses represent the whole spectrum of grades, from 1,0 (very good) to 4,0 (sufficient). If you are looking for a thesis that is exemplary in a certain respect, ask your supervisor (we cannot disclose this information on a public website).

Five Golden Rules

In this section, we discuss five golden rules: "simple sentences", "well-defined terms", "consistency", "specific enough", and "clear context". If you follow all four, your paper will be easy to read and understand. If you neglect only one of these rules, reading your paper will be no fund and it will be hard or even impossible to understand what you wanted to say.

The subsections below give a short description for each of these rules. On this page, you find some example snippetes from random publications. For each snippet, there will be a short discussion of how understandable it is and if it is not well understandable, which of the four rules has been neglected.

Simple sentences

A very simple but important rule of thumb is: write simple sentences. A simple sentence has the form: subject, predicate, object. If your sentences has two or more verbs, consider splitting it into two simpler sentences. It is rarely necessary to have sentences with more than two verbs. The sentences in this document are good examples for this writing style. As an exception, this sentence, which is rather convoluted due to various reasons, including, for example, these nested relative clauses, puts a rather heavy cognitive load on the reader and is also simple too long by way of making several statements all in one sentence which could have just as well have made in, you can guess it, multiple sentences, one sentence per statement. Note that using simple sentences may not be the best style when writing a novel. But it is perfect for scientific text, because it is easy to read and easy to understand.

Well-defined terms

Whenever you use a term that is not commonly understood, you have to define it first. You can assume that your reader has a basic education in computer science, but not more. For example, you don't have to define what an "algorithm" is. But if you say something about the "shortest-path problem", you should first define what the shortest-path problem is.

On rare occasions, it might make sense to explain a term in the sentence right after the one where you introduce it. If you do that, it should really be the very next sentence. And this construct should really be the exception, and not the rule.

Consistency

If you use a particular word to describe something, stick to that word. Variation is maybe nice in a novel. But in scientific work, every single inconsistency makes it harder for the reader. It is also fine to repeat the same word again in the next sentences. In scientific work, it is even good style.

For example, it is perfectly OK to write: "In this section we describe the details of our algorithm. The algorithm proceeds in two phases". You might be tempted to replace "The algorithm" with "It", but it would actually make the text harder to read because a reader has to resolve what "It" refers to (it could also refer to "this section"). It would be even worse to replace "The algorithm" with "Our procedure". Now you have introduced a new word and the reader will be confused about wether "procedure" should refer to "our algorithm" or something else.

Visual consistency is also important. If you use different fonts, use them with a consistent meaning. If you have multiple tables or figures in your thesis, make sure they have a consistent look.

Specific enough

One aspect of "specific enough" is to be concrete, as opposed to vague. It is very easy to be vague with natural language, especially if you have not fully understood something, but you want to make a statement nevertheless. An example of a vague sentence is: "We show that our algorithm is much better than previous ones". First, the quality measure is not clear: better in exactly which way? Second, it is not clear how much better: 10% better, twice better, ten times better? Third, which previous algorithms: all of them them, some of them, and if only some of them, which ones?

Clear context

The first three rules ("Simple sentences" and "Well-defined terms" and "Specific enough") are easy to understand and check. This next point is a bit more subtle, but just as important as the previous three points. When a text is hard to read or understand and you can't really put your finger on why, than it's usually because context is missing or unclear in many sentences.

When you explain something non-trivial, you need multiple sentences. Each sentence of your explanation will refer to entities or statements from earlier sentences. It is important that it is 100% clear what this references refer to. Otherwise the reader has to pause and think or even guess, both of which are a major nuisance when reading.

For example, consider the sentence "We bridge the gap between entities and text using automatic information extraction". This sentence is impossible to understand in isolation. It speaks of "the gap between entities and text". To understand it, one needs to understand three references: (1) what exactly "entities" refers to, (2) what exactly "text" refers to, and (3) what exactly is meant by the "gap" between these two. If the immediately preceding sentences make this clear, the sentence is fine. If the don't make it clear, the sentences is cryptic. Note that another issue with the sentence might be the term "automatic information extraction". It's ok if it has been defined before or if it is defined right in the next sentence. If only "information extraction" was defined, the "automatic" would be an example of a vague term that is not "specific enough" (rule number three).

Frequently asked questions

English or German

You can write your thesis in German or in English. If you write it in English, more people will read it. Plus, it's a great opportunity to practice writing in English. You will almost certainly need that ability in your later job, so why not start now. If you don't want anyone to read your thesis, write it in French.

American English or British English

American English please. For example, write analyze (AE) and not analyse (BE). Write neighbor (AE) and not neighbour (BE). Write labeling (AE) and not labelling. As a rule of thumb: if two variants of a word come to your mind, use the ones with less letters and the one with a z instead of an s.

How much

A typical question is: how much should I write? My typical answer is: write as much as is necessary to understand what you did, how you were doing it, and all the aspects listed in Section "Elements of a thesis" above. No more, no less. The thesis should be self-contained. That is, for someone with a basic education in computer science, it should not be necessary to read anything else than your thesis in order to understand all the main aspects of your thesis.

Typical errors

Please avoid at least the following errors.

Hyphens in noun phrases: multi-word noun phrases have a hyphen only if you use them as an adjective. Here is an example: (1) This problem has a large scale. (2) This is a large-scale problem. Putting a hyphen in (1) would be a mistake. Not putting the hyphen in (2) would be a mistake. Understand the purpose of the hyphen. It says what belongs together. In sentence (2), without the hyphen, it would not be clear whether it is a "scale problem" that is large, or whether the problem has a "large scale".

Commas after introductory clauses: there is always a comma after clauses like "However", "In this section", "Therefore", ...

TODO: what else?

Spelling

As a final step, ALWAYS run a spell checker over your write-up. It is very embarrassing indeed if your write-up contains mistakes that any spell checker would have found.

TODO: spell-check this web page.

Structure

This section provides a list of the typical elements of a thesis, together with short descriptions. In facts, these are the typical elements of any scientific article or paper.

Every section and every subsection -- except the Abstract and the Introduction -- should start with a small introduction that tells the reader what comes next. The following four sentences are an example: "In this section, we will give a high-level description of the algorithm. The algorithm has two phases. In the first phase, ... (Section 3.1). In the second phase, ... (3.2)." Note how simple the sentences are (Golden Rule #1).

A good way to start your write-up is to write down all the section and subsection headers. The names of the headers should be carefully chosen, and they should be consistent in their style.

Title

As a minimum, a title should be informative. Think of it as the shortest way to say what you did in one short sentence. A secondary criterion is that it is catchy. This is not necessary, however, and sometimes hard to achieve. Also a catchy title should be informative. If you build a system, a typical title is the name of the system, followed by a colon, followed by a short description of what the system does. Avoid titles that cannot be understood before reading any of the contents.

Abstract

An abstract should be self-contained and understandable to a non-expert. Think of it as the shortest way to say what you did in one paragraph. As a minimum, it should clarify the problem dealt with in the thesis, and the main results that were obtained. If a short example can be given, it should be given, but this is not always possible. If space permits, add a sentence or two about the underlying techniques and how the thesis advances the state of the art.

Introduction

An introduction should be self-contained and understandable to a non-expert. Think of it as the shortest way to say what you did in a couple of pages. As a minimum, it should clarify the problem dealt with, the motivation for dealing with that problem, the main results, the main challenge and the line of attack used to overcome it, and how it advances the state of the art. In the abstract, you have only one or two sentences (if any) for each of these aspects. In the introduction, you have more space. For the problem statement, it is almost always a good idea to provide a figure or screenshot with a (carefully chosen) example.

One common mistake (and nuisance) is to have relative vague and informal statements in the introduction and then later a separate section with a more formal problem statement. The corresponding reader experience is that upon first reading one either does not understand the introduction or does not find it very useful or both. You can fix this as follows: whatever is relatively easy to understand and can already be defined in the introduction should already be defined in the introduction.

Related Work

Most probably, other researchers have worked on the problem of your thesis, or a strongly related problem, before. Your thesis should include a section which summarizes the most relevant of these works. Typically this is Section 2, right after the Introduction. For each of these works, it should be explained in a nutshell what they do, what they achieve, and how this differs from what you do in your thesis. This should be understandable without the need to actually read the papers referred to. For each related work, think of the description as the shortest way to say this in one paragraph.

When there is a lot of work about a particular topic / problem, it is ok to focus on the most recent / most advanced approaches. Where there is no work on the exact problem from your thesis, the Related Work section should be about work on similar problems and it should explain how these related problems differ. Note that sometimes a problem looks different only on the surface, and the solutions can actually be applied to your problem as well.

Theoretical analysis

Most probably, your work will make use of some algorithms and data structures. Either your own, or such from previous work, or a combination of the two. In any case, provide information about the basic complexity of your algorithms, in particular their running time. Do this also if the the statement appears straightforward to you. For example, the running time of one of your algorithms may obviously be linear in the size of the input data. In any case, say it and provide an argument / proof for it!

Empirical analysis

Most probably, your work involves the implementation of an algorithm or data structure, or of a whole system. Whatever it is, your implementation should be thoroughly evaluated. The kind of evaluation depends on the nature of your problem. If the focus is on results of a particular quality, that should be evaluated. If the focus is on efficiency, running time and (if relevant) space consumption should be evaluated. Even if the focus is on quality, efficiency should be evaluated, too. One always wants to know the running time of a procedure and (if relevant) its space consumption. If there is a pre-processing phase, this should be evaluated separately. If the pre-processing consumes a lot of intermediate disk space or memory, that should also be evaluated. Think about the evaluation from the perspective of someone who wants to use your software in practice. What is it that you would want to know then?

Typically, there are other approaches which can be used (either directly or with small modifications / adjustments) to solve your problem. As a minimum, compare to the best one of these approaches. If there is a variety of principally different approaches, pick the best one for each principle. If there is no solution yet for your problem, think of a simple baseline algorithm (= the straightforward solution) and compare to that. Sometimes there are two or even three simple baseline algorithms. Do your evaluation on at least three different data sets of different kinds and sizes. If the amount of work needed per data set is very large, it is OK to use only two data sets.

Future work

Most probably, your work will leave various open ends. Make a list of what could be done next to improve on what you did. For each item, give a short description of the possible improvement + an idea for how, in principle, it could be achieved. Also give an estimate for the necessary time to realize that improvement (hours, days, weeks, months). Order your list by importance / significance. That is, the thing that should be improved next / gives the biggest improvement should come first.

Bibliography

Make sure that the entries in your bibliography have a consistent style. That is, abbreviate all conferences in the same way or use the full names for all, but do not mix the two styles. Same for author names. Same for capitalization of titles. Same for page numbers. It makes a very careless and untidy impression, if the bibliography entries are inconsistent in their style.