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Research Methods in Computer Science

Ullrich Hustadt

Department of Computer ScienceUniversity of Liverpool

Ullrich Hustadt Research Methods in Computer Science 1 / 1
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Introduction and Overview Research

Research Methods in Computer ScienceLecture 1: Introduction and Overview

Ullrich Hustadt


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Today ...

1 Introduction and OverviewAimsLearning outcomesDeliveryAssessment

2 What is Research ?

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Introduction and Overview Research Aims Learning outcomes Delivery Assessment

Aims

1 To provide a deep and systematic understanding of the nature andconduct of Computer Science research

2

To equip students with the ability to undertake independent research3 To enhance existing transferable key skills

4 To develop high-order transferable key skills

5 To remind students of the Legal, Social, Ethical and Professional(LSEP) issues applicable to the computer industry

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Learning Outcomes (1)

1 Have anunderstandingof how establishedtechniques of researchandenquiry are used to extend, create and interpret knowledge inComputer Science

2 Have a conceptualunderstanding sufficient to:

(i) evaluatecriticallycurrent researchand advanced scholarship inComputer Science, and(ii) proposepossiblealternative directionsfor further work

3 Beable to deal with complex issuesat the forefront of the academicdiscipline of Computer Science in a manner,

based on sound judgements, that is both systematic and creative; andbeable to communicate conclusions clearlyto both specialists andnon-specialists


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4 Demonstrate self-direction and originalityin tackling and solvingproblems within the domain of Computer Science, andbeable to act autonomously in planning and implementing solutionsin

a professional manner5 Beable to define and plan a programme of independent research

6 Participate within the professional, legal and ethical framework withinwhich they would be expected to operate as professionals within the IT

industry


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7 Make use of the qualities andtransferable skillsnecessary foremployment requiring:

(i) the exercise of initiativeandpersonal responsibility,(ii) decision making in complex and unpredictable situations, and

(iii) theindependent learning abilityrequired for continuing professionaldevelopment

8 Have theskills setto be able to continue toadvance their knowledgeand understanding, and todevelop new skills to a high level, with

respect to continuing professional development as a self-directedlife-long learner across the discipline of Computer Science


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In short, you should learn

1 tounderstand researchandresearch methodsin Computer Science;

2 tobe able to plan, andconduct your own research, taking into accountethical, legal, and professional limitations; and

3 tobe able to communicate its results


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Delivery of the module (1)

1 Lectures

Monday, 11.00 Ashton Building Seminar Room (Ground Floor)Monday, 12.00 Ashton Building Seminar Room (Ground Floor)Friday, 10.00 Ashton Building Seminar Room (Ground Floor)

2 Seminars

During lectures By members of staffTuesday, 16.00 Departmental research seminar

Ashton Building Seminar Room (Ground Floor)

3 Tutorials/Labs

To be arranged


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Delivery of the module (2)

1 Office hours

Typically, Monday 15.00 to 17.00;

make an arrangement by e-mail ([email protected]) first

2 Website

http://www.csc.liv.ac.uk/~ullrich/COMP516/


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Recommended texts

Christian W. Dawson: Projects in Computing and Information Systems(A Students Guide). Addison Wesley, 2005.Harold Cohen Library, Class No 518.561.D27

Earlier edition:Christian W. Dawson: The essence of computing projects (A studentsguide). Prentice Hall, 2000.Harold Cohen Library, Class No 518.561.D27

Justin Zobel: Writing for Computer Science. Springer, 2004.Harold Cohen Library, Class No 378.962.Z81




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Assessment

2,000 word essay on a topic chosen by the examinerto be handed out on Friday, 28 September 2007to be submitted on Friday, 26 October 2007, 15.30accounts for15% of the module mark

5,000 word essay on an agreed subjectwork can be started as soon as the subject is agreed(Friday, 26 October 2007, at the latest)

to be submitted before the Xmas break(most likely Thursday, 13 December 2007, 15.30)

accounts for85% of the module mark

Pass mark, as usual for MSc modules, is 50%




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Teaching and learning strategy

Lectures, seminars, tutorials/labs only make up a small part of thedelivery of the module

In total you are expected to commit 150 hours to the module, that is,

12.5 hoursper week over 12 weeks(more hours per week than for any other module)

Of those the timetabled activities only make up 4 hours per week

In addition you should spend3.5 hours per weekon reflection,consideration of lecture material and background readingplus5 hours per weekon theassessment tasks


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What is research?

Research (Dictionary)

Noun

1 Scholarly or scientific investigation or inquiry.

2 Close, careful study.

Verb

1 To study (something) thoroughly so as to present in a detailed,accurate manner.

(Example: researching the effects of acid rain.)

Note the difference between the definition of the noun and of the verb.


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What is research?

Study (Dictionary)

Noun

1 The pursuit of knowledge, as by reading, observation, or research.

2

Attentive scrutiny.Verb

1 To apply ones mind purposefully to the acquisition of knowledge orunderstanding of (a subject).

2 To inquire into; investigate.3 To examine closely; scrutinise.




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What is research?

Research (http://en.wikipedia.org/wiki/Research)

anactive, diligent, and systematic process of inquiryin order todiscover, interpret or revisefacts, events, behaviours, or theories, or to

make practical applicationswith the help of such facts, laws, or theories.a collection of information about a particular subject.

derives from the Middle French and the literal meaning isto investigate thoroughly.

Homework: Read the Wikipedia article!


Research Knowledge Knowledge Originality
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Research Methods in Computer ScienceLecture 2: Research (continued)

Ullrich Hustadt





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Previously . . .

1 Introduction and OverviewAimsLearning outcomes

DeliveryAssessment

2

What is Research ?


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Today ...


More Definitions of Research

4 KnowledgeA HierarchyData

InformationKnowledge

5 KnowledgeTheories

6 OriginalityDefinitionThe importance of repeating the work of others


Research Knowledge Knowledge Originality Research 2
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What is research?


anactive, diligent, and systematic process of inquiryin order todiscover, interpret or revisefacts, events, behaviours, or theories, or to

make practical applicationswith the help of such facts, laws, or theories.a collection of information about a particular subject.

derives from the Middle French and the literal meaning isto investigate thoroughly.

Homework: Read the Wikipedia article!


Research Knowledge Knowledge Originality Research 2
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What is research?

Research (Higher Education Funding Council for England)Original investigationundertaken in order togain knowledge andunderstanding, including

work of direct relevance to the needs of commerce and industry and tothe public and voluntary sectors

scholarship (research infrastructure)

the invention and generation of ideas, images, performances andartifacts including design, where these lead to new or substantiallyimproved insights;

the use of existing knowledge in experimental development to producenew or substantially improved materials, devices, products andprocesses, including design and construction.

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Research Knowledge Knowledge Originality Hierarchy Data Information Knowledge


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Knowledge: Data and Information

Datum/Data

statements accepted at face value (a given) and presented as numbers,characters, images, or sounds.

a large class of practically important statements are measurementsorobservationsof variables, objects, or events.

in a computing context, in a form which can be assessed,stored,processed, andtransmittedby a computer.



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Knowledge: Data and Information

Information

Dataon its own has no meaning, only wheninterpretedby some kind ofdata processing systemdoes it take on meaning and becomesinformation

Example:Thehuman genome projecthas determined the sequence of the 3 billionchemical base pairs that make up human DNA

identifying base pairs producesdata informationwould tell us what they do!



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Knowledge: Alternative definitions (1)

Knowledge (Dawson 2005)

higher level understanding of things

represents our understanding of the why instead of the mere what

interpretation of information in the form of rules, patterns, decisions,

models, ideas, etc.

Innatural sciences, understanding why is too ambitious most of time;understanding how is usually what we aim for

In other areas, understanding how is trivial, understanding why ischallenging



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Knowledge (Davenport et al. 1998)

a fluid mix offramed experience,contextual information, values andexpert insightthat provides aframework for evaluating andincorporating new experiences and information.

information combined with experience, context, interpretation, and

reflection

high-value form of information that is ready to apply to decisions andactions

Second point similar to last point in the previous definition

Last point seems to imply that knowledge has to be useful(is astrophysics useful?)



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Knowledge (http://en.wikipedia.org/wiki/Knowledge)

theawarenessandunderstanding of facts, truths or information gainedin the form of experience or learning (a posteriori), or through deductivereasoning (a priori)

an appreciation of the possession ofinterconnected detailswhich, inisolation, are of lesser value

both knowledge and information consist of true statements, butknowledge is information that has apurpose or use(information plus

intentionality)


Research Knowledge Knowledge Originality Theories

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Knowledge and theories: Definition

Scientific knowledge is often organised intotheories.

Theory (http://en.wikipedia.org/wiki/Theories)

alogically self-consistent modelor framework describing the behaviourof a certain natural or social phenomenon, thus either originating fromobservable facts or supported by them

formulated, developed, and evaluated according to thescientific method



Knowledge and theories: Criteria
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A body of (descriptions of) knowledge is usually only called a theoryonceit has afirm empirical basis, that is, it

1 isconsistent with pre-existingtheory to the extent that the pre-existing

theory was experimentally verified, though it will often showpre-existing theory to be wrong in an exact sense,

2 issupported by many strands of evidence rather than a singlefoundation, ensuring that it probably is a good approximation if nottotally correct,





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A body of (descriptions of) knowledge is usually only called a theoryonceit has afirm empirical basis, that is, it

3 makes (testable) predictionsthat might someday be used to disprove

the theory, and4 hassurvived many critical real world teststhat could have proven it

false,5 is a/thebest known explanation, in the sense of Occams Razor, of the

infinite variety of alternative explanations for the same data.



Knowledge and theories: Facts versus theories


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Knowledge and theories: Facts versus theories

This (e.g. evolution) is only a theorynot afact

Fact

1. atruth(statement confirming toreality)or

2.datasupported by ascientific experiment

Status of a truth is by and large unachievable

Atheoryis formulated, developed, and evaluated according to thescientific method

Given enoughexperimental supportatheorycan be(a scientific)fact


Research Knowledge Knowledge Originality Definition Repeating work

Originality (1)
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Originality (1)

Research(HEFCE):Original investigationundertaken in order togain

knowledge and understandingOriginality

Doing something that has not been done before

Dawson (2005):There is no point in repeating the work of others and dis-covering or producing what is already known

Only true for what is truly known (i.e. very little)

Theories make predictions, which need to be testedThe people performing those tests are neitherinfalliblenortrustworthyTests need to be repeated and resultsreplicated


Research Knowledge Knowledge Originality Definition Repeating work

(In)Fallibility
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(In)Fallibility

Cold fusion

(http://en.wikipedia.org/wiki/Cold_fusion)Cold fusion: Nuclear fusion reaction that occurs well below thetemperature required for thermonuclear reactions, that is, near ambienttemperature instead of millions of degrees Celsius

First reported to have been achieved by Pons (University of Utah) andFleischmann (University of Southampton) in 1989

Scientists tried to replicate their results shortly after initialannouncement

Teams at Texas A&M University and the Georgia Institute ofTechnology first confirmed the results, but then withdraw those claimsdue to lack of evidence

Vast majority of experiments failed


Investigation Knowledge Originality Gain Research
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Research Methods in Computer ScienceLecture 3: Research (continued)

Ullrich Hustadt




Previously
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Previously . . .


More Definitions of Research

4 KnowledgeA HierarchyData

InformationKnowledge

5 KnowledgeTheories

6 OriginalityDefinitionThe importance of repeating the work of others



What is research?
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What is research ?

Research (Dictionary)

1 Scholarly or scientific investigation or inquiry.2 Close, careful study.


anactive, diligent, and systematic process of inquiryin order todiscover, interpret or revisefacts, events, behaviours, or theories, or tomake practical applicationswith the help of such facts, laws, or theories.

a collection of information about a particular subject.

Research (Higher Education Funding Council for England)

Original investigationundertaken in order togain knowledge andunderstanding



Today ...
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Today ...

7 Investigation

8 Knowledge

9 OriginalityAreas of originality

10 Gain

11 What is Research?Summary

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Knowledge: A hierarchy


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Datum/Data

statements accepted at face value (a given) and presented as numbers,characters, images, or sounds.a large class of practically important statements are measurementsorobservationsof variables, objects, or events.

InformationData interpretedby some kind ofdata processing systemwhich gives itmeaning

Knowledge (Dawson 2005)

higher level understanding of thingsrepresents our understanding of the why instead of the mere whatinterpretation of information in the form of rules, patterns, decisions,models, ideas, etc.


Investigation Knowledge Originality Gain Research Areas of originality

Research and Originality (1)
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Research(HEFCE):Original investigationundertaken in order togain

knowledge and understandingOriginality

Doing something that has not been done before

Dawson (2005):There is no point in repeating the work of others and dis-covering or producing what is already known

Only true for what is truly known (i.e. very little)

Theories make predictions, which need to be testedThe people performing those tests are neitherinfalliblenortrustworthyTests need to be repeated and resultsreplicated


Investigation Knowledge Originality Gain Research Areas of originality

Research and Originality (2)


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Areas of originality (Cryer 1996)Exploring the unknownInvestigate a field that no one has investigated before

Exploring the unanticipated

Obtaining unexpected results and investigating new directions in analready existing field

The use of dataInterpret data in new ways

Tools, techniques, procedures, and methodsApply new tools/techniques to alternative problemsTry procedures/methods in new contexts



Gain


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Research(HEFCE):Original investigationundertaken in order togainknowledge and understanding

Contribution

Research is supposed to add to the worlds body of knowledge andunderstanding (in contrast to adding to the researchers knowledge andunderstanding)


Investigation Knowledge Originality Gain Research Summary

What is research?


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In summary, what are thethree key aspects of research?

(10 minutes group discussion)



What is research?


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An active, diligent, and systematic process of inquiry in order to discover,interpret or revise facts, events, behaviours, or theories, or to makepractical applications with the help of such facts, laws, or theories.

Research (Higher Education Funding Council for England)Original investigation undertaken in order to gain knowledge andunderstanding

Sharp et al. (2002)

Seeking through methodical process to add to ones own body ofknowledge and to that of others, by the discovery of non-trivial facts andinsights



What is research?
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Anactive, diligent, and systematic process of inquiryin order todiscover,interpret or revisefacts, events, behaviours, or theories, or to makepractical applicationswith the help of such facts, laws, or theories.

Research (Higher Education Funding Council for England)Original investigationundertaken in order togain knowledge andunderstanding

Sharp et al. (2002)

Seeking through methodical process toaddto ones own body ofknowledge and to that of others, by the discovery ofnon-trivial facts andinsights

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Process models


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Research Methods in Computer ScienceLecture 4: Research process models

Ullrich Hustadt



Process models

Previously . . .


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7 Investigation

8 Knowledge

9 OriginalityAreas of originality

10 Gain

11 What is Research?Summary



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Process models Sequential Generalised Circulatory Evolutionary

Research process models


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All definitions agree thatresearchinvolves asystematicor methodicalprocess

Dawson (2005), following Baxter (2001), identifies

four common views of theresearch process:Sequential

Generalised

Circulatory

Evolutionary



Research process models: Sequential (1)


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Research process asSeries of activities

Performed one after another (sequentially)

In a fixed, linear series of stages

Example:Research process model of Greenfield (1996):

1 Review the field2 Build a theory3 Test the theory4 Reflect and integrate





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Example:Sharp et al (2002):

1 Identify the broad area of study2 Select a research topic3 Decide on an approach4 Plan how you will perform the research

5 Gather data and information6 Analyse and interpret these data7 Present the result and findings





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Greenfield (1996):

1

Review the field2 Build a theory3 Test the theory4 Reflect and integrate

Sharp et al (2002):

1

Identify the broad area of study2 Select a research topic3 Decide on an approach4 Plan how you will perform the research5 Gather data and information

6 Analyse and interpret these data7 Present the result and findings

What do you think about this research process model?What is wrong with it?






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Greenfield (1996):

1

Review the field2 Build a theory3 Test the theory4 Reflect and integrate

Sharp et al (2002):

1

Identify the broad area of study2 Select a research topic3 Decide on an approach4 Plan how you will perform the research5 Gather data and information

6 Analyse and interpret these data7 Present the result and findings

Problems with the sequential (and generalised) process model:

1 Stages not subject specific

2 No repetition or cycles

3 Starting point and order fixed



Research process models: Generalised (1)


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Thegeneralised research process model recognises that the stages of theresearch process depend on thesubjectandnatureof the researchundertaken

Example:

Data gatheringanddata analysisplay no role for research inpuremathematicsand large parts ofcomputer scienceInstead researchers makeconjectureswhich theyprove mathematically

Thegeneralised research process modelprovidesalternative routesdepending on thesubjectandnatureof the research undertaken

But eachrouteis stillsequential



Research process models: Generalised (2)


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Example:

(1) Identify the broad area of study(2) Select a research topic

In natural sciences:(3) Decide on an approach(4) Plan the research(5) Gather data and information(6) Analyse and interpret these data

In mathematics:(3) Make a conjecture(4) Prove the conjecture

(7) Present the result and findings

Problems with the generalised process model:1 No repetition or cycles

2 Starting point and order fixed



Research process models: Circulatory


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Thecirculatory research process modelrecognises that any research is

part of acontinuous cycleofdiscoveryandinvestigationthat never endsIt allows the research process to bejoinedat any point

One can alsorevisit(go back to)earlier stages

ConceptualFramework(theory, literature)

Research Question

Empirical ObservationData Collection

Data Analysis



Research process models: Evolutionary (1)


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Theevolutionary research process model recognises that research(methods) itselfevolveandchange over time

That is, over time our concept of

What research questions are admissible

What extend and methods of data collection are possible, necessary, ethical,or reliable

What methods are data analysis are available

What constitutes sufficient evidence for a hypothesis

What we mean by a systematic approach to research changes





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As an example, we can consider research inmathematics, in particular,its use ofcomputers

With respect tomathematical proofswe can make the following

distinctions:(1) Proofs created solely by humans

typically sketchy, omitting steps that are considered obvious

(2) Computer-aided mathematical proofs Structure and deductive steps still provided by humans, but

certain computations are delegated to a computer

(3) Fully formal, computer generated and validated proofs

Every step of a proof is conducted and validated by a computer,possibly under guidance by humans





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Computer-aided mathematical proofs (1)

Four colour theoremAny planar map can be coloured with at most four colours in a

way that no two regions with the same colour share a border.

Conjectured in 1852 by Guthrie. Proved in 1976 by Appel and Haken.Proof involves a case analysis of about 10,000 cases for which the helpof a computer was used

Proof seems generally accepted, but not by all Mathematician





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Computer-aided mathematical proofs (2)

Sphere packing theorem

Close packing is the densest possible sphere packing.

Conjectured in 1611 by Kepler. Hayes published a proof plan in (1997).Execution of the plan involved solving about 100,000 linear optimisationproblems using a computer. The computer files for the related programsand data requires more than 3GB of space

At one point it was suggested that the proof will be published with adisclaimer, saying that it is impossible for a human to check itscorrectness



Research process models: Conclusion


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Among the four common views of theresearch processSequential

Generalised

Circulatory

Evolutionary

theevolutionary research process model best describes the realresearch process

While theevolutionary research process modelallows for the rules of thegame to change over time, this does not imply there arent any rules

For a young researcher it is best to follow the current establishedresearch process



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Scientific method Intellectual discovery Problem solving

Previously . . .


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10 Research process modelsSequential

GeneralisedCirculatoryEvolutionary



Topics
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11 Scientific methodElements

12 Intellectual discovery

DeductionAbductionInductionProcess model

13 Problem solving


Scientific method Intellectual discovery Problem solving Elements

Scientific method
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Scientists useobservationsandreasoningtodevelop technologiesandpropose explanationsfor natural phenomena in the form ofhypotheses

Predictionsfrom thesehypothesesare tested by experimentand furthertechnologies developed

Anyhypothesiswhich is cogent enough to make predictions can then betested reproducibly in this way

Once it has been established that ahypothesisissound, it becomes atheory.

Sometimesscientific developmenttakes place differently with atheory

first being developed gaining support on the basis of its logic andprinciples


Scientific method Intellectual discovery Problem solving Elements

Elements of a scientific method
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The essential elements of a scientific method are iterations,recursions,interleavingsandorderingsof the following:

Characterisations(Quantifications, observations and measurements)

Hypotheses(theoretical, hypothetical explanations of observations and

measurements)Predictions(reasoning including logicaldeductionfrom hypotheses and theories)

Experiments

(tests of all of the above)Bothcharacterisationsandexperimentsinvolve data collection


Scientific method Intellectual discovery Problem solving Deduction Abduction Induction Process model

Intellectual discovery

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Knowing what theelementsof ascientific methodare does not tell ushow to come up with the rightinstancesof these elements

What predictions does a theory make?What is the right hypothesis in a particular situation?

What is the right experiment to conduct?

These are commonly derived by a process involvingDeductive reasoning

Abductive reasoning

Inductive reasoning

Classification by Charles Sanders Peirce (1839-1914)See http://plato.stanford.edu/entries/peirce/for additionaldetails



Intellectual discovery: Deduction (1)

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Deductive reasoningproceeds from our knowledge of the world(theories) and predicts likely observations

Example:

Assume we know that A implies B. A has been observed. Then we should also obverse B.

Useful forexperiment generationfor theories

Example:

Newtons theory of gravity versus Einsteins theory of relativity

Largely make the same predictions

Both predict that the suns gravity should bend rays of light

However, Einsteins theory predicts a greater deflection

Correctness of Einsteins prediction confirmed by observation in 1919



Intellectual discovery: Deduction (2)

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Deductive reasoningis often saidnotto lead to new knowledge(Note: This implies pure mathematicians largely waste

their time)

Seriously underestimates the computational effort involvedindeductive reasoning

Most theories areundecidable

(There is no algorithm that even given infinite time coulddetermine whether a statements follows from a theory ornot)

Thus, establishing that a statement follows from a theory

extendsour knowledge



Intellectual discovery: Abduction

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Abductive reasoningproceeds from observations to causes

Example: The phenomenon X is observed. Among hypotheses A, B, C, and D,

only A and B are capable of explaining X. Hence, there is a reason to assume that A or B holds.

Requires atheorylinking A, B, C, D to X

Useful forhypothesis generation

Hypothesesmust then be confirmed / eliminated through furtherobservation

It is not easy from the outside to decide whether someone usesdeductionorabduction The two are often confused



Intellectual discovery: Induction (1)

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Inductive reasoningproceeds from a set of observations to a generalconclusion

Example:

Tycho Brahe, a 16th century astronomer, collected data onthe movement of the Mars.

Johannes Kepler analysed that data which was consistent

with Mars moving in an elliptic orbit around the sun.

Inductive conclusion:Mars, and all other planets, move in elliptic orbits around theSun, with the Sun at one of the focal points of the ellipse.

Primary tool fortheory formation



Intellectual discovery: Induction (2)

An incomplete set of observations can easily lead to incorrect inductive
http://find/


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An incomplete set of observations can easily lead to incorrect inductiveconclusions

Example:

All swans Ive ever seen are white Inductive conclusion: All swans are white



Scientific method: A model
http://find/http://goback/


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Observations

induction

Theory Hypothesis

deduction

Predictions

test

New Observations

Confirm predictions?

no

yes

Theory

Observations

abduction

Fact Hypothesis

test



Intellectual discovery: Problems

Deductive reasoning tells us that from A and A implies B we can
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Deductive reasoningtells us that from A and A implies B we canconclude B

However, it cannot tell us whether A or A implies B holds, norwhether B is what we want to show

Abductive reasoningtells us that from B and A implies B we mayconclude A

However, it cannot tell us whether B or A implies B hold, nor how toestablish that A is the case

Inductive reasoningtells us that from A(o1), . . . , A(on) andB(o1), . . . , B(on) we may conclude x.A(x)B(x).However, it cannot tell us what the properties A( ) and B( ) are (nor

how large the number n needs to be)

To overcome these problems we need additional techniques.



Problem solving

Analogy: Look for similarity between one problem and another one


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Analogy: Look for similarity between one problem and another onealready solved

Partition: Break the problem into smaller sub problems which are easierto solve

Random/Motivated Guesses: Guess a solution to the problem thenprove it correct

Generalise: Take the essential features of the specific problem and posea more general problem

Particularise: Look for a special case with a narrower set of restrictionthan the more general case

Subtract: Drop some of the complicating features of the originalproblem

Add: A difficult problem may be resolved by adding an auxiliary problem


Research classification Research methods
http://find/http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


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Research Methods in Computer ScienceLecture 6: Research methods

Ullrich Hustadt




Previously . . .


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13 Scientific methodElements

14 Intellectual discovery

DeductionAbductionInductionProcess model

15 Problem solving



Topics
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16 Classifying research

17 Research methodsOverviewExperimentsQuestionnaires



Classifying research (1)

Research can be classified from three different perspectives:


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Research can be classified fromthree different perspectives:

1 Field

Position of the research within a hierarchy of topics

Example:Artificial IntelligenceAutomated Reasoning

First-Order Reasoning Decidability

2 ApproachResearch methods that are employed as part of the research process

Examples:Case study, Experiment, Survey, Proof

3 NaturePure theoretical developmentReview of pure theory and evaluation of its applicabilityApplied research




Pure theory:


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yDeveloping theories and working on their consequences, with regard to

experimentation or applicationDescriptive studies:Reviewing and evaluating existing theories, including describing thestate of the art, comparing predictions with experimental data

Exploratory studies:Investigating an entirely new area of research, exploring a situation ora problemSee http://www2.uiah.fi/projects/metodi/177.htm

Explanatory studies:

Explaining or clarifying some phenomena or identifying the relationshipbetween things

Ullrich Hustadt Research Methods in Computer Science 106 / 117 Research classification Research methods


Causal studies:
http://www2.uiah.fi/projects/metodi/177.htmhttp://www2.uiah.fi/projects/metodi/177.htmhttp://find/http://-/?-


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Assessing the causal relationship between things

Normative studies:Producing a theory of design (or of other development) likerecommendations, rules, standards, algorithms, advices or other tools forimproving the object of study

Problem-solving studies:Resolving a problem with a novel solution and/or improving somethingin one way or another

Development and Application studies:Developing or constructing something novel

Ullrich Hustadt Research Methods in Computer Science 107 / 117 Research classification Research methods Overview Experiments Questionnaires

Quantitative and qualitative research methods

Q i i h h d


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Quantitative research methods

Methods associated withmeasurements(on numeric scales)Stemming from natural sciences

Used totest hypothesesor create aset of observationsfor inductivereasoning

Accuracy and repeatability of vital importance

Qualitative research methods

Methods involving case studies and surveys

Stemming from social sciences

Concerned with increasing understanding of an are, rather than anexplanation

Repeatability usually a problem


Research methods (1)

Action research


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Action research:

Pursues action (or change) and understanding at the same timeContinuously alternates between action and critical reflection, while refiningmethods, data and interpretation in the light of the understanding developedin the earlier cycles

Example: Reflective teaching

Case study:

In-depth exploration of a single situation

Usually generates a large amount of (subjective) data

Should not merely report the data obtained or behaviour observed butattempt to generalise from the specific details of the situation observed

Example: Case study of open source software development


Research methods (2)


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Survey:

Usually undertaken using questionnaires or interviewsQuestionnaire and interview design important!(See Dawson 2005 for details)Determination of sample size and sample elements important!(See specialist literature for details)

Example: Survey on the popularity or use of programming languages

Experiment:

Investigation of causal relationships using test controlled by the researcher

Usually performed in development, evaluation and problem solving projects

Example: Evaluation of processor performance


Key elements of an experiment


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A precisehypothesisthat the experiment will confirm or refuteA completely specifiedexperimental system, which will be modified insome systematic way to elicit the effects predicted by the hypothesis

Quantitativemeasurementof the results of modifying the experimentalsystem

Use ofcontrolsto ensure that the experiment really tests the hypothesis

Analysisof the measured data to determine whether they are consistentwith the hypothesis

Reportof procedures and results so that others can replicate theexperiment


Key issues for questionnaires


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Consider the following questions

What are the key issues for conducting a survey by questionnaire?

Regarding the questionnaire itself, what types of questions do you know

and what is each of them used for?




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Research Methods in Computer ScienceLecture 7: Who is Who in Computer Science Research

Ullrich Hustadt



Prizes and Awards

Scientific achievementis often recognised by prizesandawards


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g y p

Conferencesoften give abest paper award, sometimes also abeststudent paper award

Example:IJCAR Best Paper PrizeFor the best paper, as judged by the program committee

Professional organisationsalso giveawardbased on varying criteria

Example:British Computer Society Roger Needham AwardMade annually for a distinguished research contribution in computer

science by a UK based researcher within ten years of their PhD.

Arguably, the most prestigious award in Computer Science is theA. M. Turing Award


Alan M. Turing (1912-1954)

Considered to be the father of modern computer science


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Considered to be the father of modern computer science

In a 1936 paper introducedTuring machines, as athought experiment about the limits of mechanicalcomputationGives rise to the concept ofTuring completenessandTuring reducability

In 1939/40, Turing designed an electromechanical machine whichhelped to break the german Enigma codeHis main contribution was ancryptanalytic machinewhich usedlogic-based techniques

In the 1950 paper Computing machinery and intelligence Turingintroduced an experiment, now called theTuring test, to define astandard for a machine to be called sentient


Turing Award

TheA. M. Turing Awardis given annually by the Association forC ti M hi t


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Computing Machinery to

an individual selected for contributions of a technical nature made tothe computing community. The contributions should be of lasting andmajor technical importance to the computer field.


Turing Award Winners

What contribution have the following people made?Who among them has received the Turing Award?


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g g

Frances E. AllenLeonard M. AdlemanPaul BaranTimothy J. Berners-LeeVinton G. CerfEdgar F. CoddStephen A. CookLawrence J. EllisonDouglas Engelbart

William H. Gates IIIJames A. GoslingIrene Greif

Alan KayDonald E. KnuthRobin MilnerTheodor H. NelsonLawrence PageAlan J. PerlisAmir PnueliDennis M. RitchieRonald R. Rivest

Adi ShamirRichard M. StallmanKen Thompson






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Who among them has received the Turing Award?

Frances E. AllenLeonard M. AdlemanPaul BaranTimothy J. Berners-Lee

Vinton G. CerfEdgar F. CoddStephen A. CookLawrence J. EllisonDouglas Engelbart

William H. Gates IIIJames A. GoslingIrene Greif

Alan KayDonald E. KnuthRobin MilnerTheodor H. Nelson

Lawrence PageAlan J. PerlisAmir PnueliDennis M. RitchieRonald R. Rivest

Adi ShamirRichard M. StallmanKen Thompson





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Who among them has received the Turing Award?

Frances E. Allen Leonard M. Adleman Paul Baran Timothy J. Berners-Lee

Vinton G. Cerf Edgar F. Codd Stephen A. Cook Lawrence J. Ellison Douglas Engelbart

William H. Gates III James A. Gosling Irene Greif

Alan Kay Donald E. Knuth Robin Milner Theodor H. Nelson

Lawrence Page Alan J. Perlis Amir Pnueli Dennis M. Ritchie Ronald R. Rivest

Adi Shamir Richard M. Stallman Ken Thompson


The ones who havent made it (yet)

Paul BaranO f h h i f k i h d k l i h


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One of the three inventors of packet-switched networks, along with

Donald Davies and Leonard Kleinrock in the early 1960s

Timothy J. Berners-LeeTogether with Robert Cailliau invented the World Wide Web in 1989

Lawrence J. Ellison

Co-founder and CEO of the database softwar company OracleWilliam H. Gates III

Co-founder, together with Paul Allen, of Microsoft; held thepositions of CEO, chief software architect, and chairman

James A. GoslingInvented theJava programming languagein 1994; devised theoriginal design of Java and implemented its original compiler andvirtual machine


The ones who havent made it (yet)

Irene GreifT h i h P l C h i d d h f C


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Together with Paul Cashman introduced the concept of Computer

Supported Collaborative Work (CSCW) in 1984

Theodor H. NelsonCoined the term hypertext in 1963 and published it in 1965; theidea itself goes back to 1945, Vannevar Bushs Memex device

Lawrence PageCo-founder, together with Sergey Brin, of Google; developed thePageRank algorithm in 1998 on which Google is based

Richard M. StallmanSoftware freedom activist, hacker, software developer; lauched the

GNU Project in 1983

Ull i h H st dt R s h M th ds i C t S i 126 / 143

Frances E. Allen

Received the Turing award in 2006

F i i ib i h h d


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For pioneering contributions to the theory and

practice of optimizing compiler techniques thatlaid the foundation for modern optimizingcompilers and automatic parallel execution.

First woman to receive the award

Her 1966 paper on Program Optimization and a 1971 paper with JohnCocke provide the conceptual basis for the systematic analysis andtransformation of computer programs

Work forms the basis for modern machine- and language-independent

program optimizersLead an IBM project which developed the concept ofprogramdependence graph, the primary structuring method used by mostparallelizing compilerstoday

Ull i h H t dt R h M th d i C t S i 127 / 143

Vinton G. Cerf, Robert E. Kahn


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Received the Turing award in 2004For pioneering work on internetworking, including the design andimplementation of the Internets basic communications protocols,TCP/IP, and for inspired leadership in networking.

Led the design and implementation of the Transmission ControlProtocol and Internet Protocol (TCP/IP)

Basis for current internetworking


Alan Kay


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For pioneering many of the ideas at the root ofcontemporary object-oriented programming languages,leading the team that developedSmalltalk, and forfundamental contributions to personal computing.

Development started in 1969, publicly available since 1980

First complete dynamicobject-oriented programming

Influenced the design ofC++ andJava

Included a completevisual programming environmentEnvisaged to be part of a user-centered approach to computing


Leonard M. Adleman, Ronald R. Rivest, Adi Shamir


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For their ingenious contribution for making public-keycryptography useful in practice.

Created the worlds most widely used public-key cryptography system,

RSA, in 1977Clifford Cocks described an equivalent system in an internal GHCQdocument in 1973, but it was never deployed and kept secret until 1997


Douglas Engelbart



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For an inspiring vision of the future of interactivecomputing and the invention of key technologies tohelp realize this vision.

Vision of a computer and communications based working environment

Invention of key tools and systems that helped start thepersonalcomputerrevolution:

Computer mouseMultiple on screen windowsLinked hypermediaShared screen teleconferencing and computer aided meetingsOnline publishing

Presented in 1968 as part of the mother of all demos


Amir Pnueli


F i l k i d i l l i i


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For seminal work introducing temporal logic intocomputing science and for outstanding contributionsto program and system verification.

Major breakthrough in theverification and certification of concurrent

and reactive systems

Landmark 1977 paper The Temporal Logic of Programsin Proc. 18th IEEE Symp. Found. of Comp. Sci., 1977, pp. 4657.

Focus on ongoing behaviour of programs (rather than input/output

behaviour)Allows to easily specify qualitative progress properties of concurrent programs

Careful logic design enables automated verification of concurrent programs


Robin Milner


F h di i d l hi


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For three distinct and complete achievements:1 LCF, the mechanization of Scotts Logic of

Computable Functions, probably the first theoreticallybased yet practical tool for machine assisted proofconstruction;

2 ML, the first language to include polymorphic type

inference together with a type-safe exception-handlingmechanism;

3 CCS, a general theory of concurrency.

In addition, he formulated and strongly advanced fullabstraction, the study of the relationship betweenoperational and denotational semantics.


Dennis M. Ritchie, Ken Thompson


F th i d l t f i ti t


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For their development ofgeneric operating systemstheoryand specifically for the implementation of theUnixoperating system.

Development of UNIX began in 1969

Seminal paper published in 1973 on The UNIX Time-Sharing Systemat the Fourth ACM Symposium on Operating Systems Principles

UNIX was the first commercially important portable operating system

Usable (almost without change) across a wide range of hardware from

smartphones to supercomputers



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Edgar F. Codd


For his fundamental and continuing contributions to


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For his fundamental and continuing contributions tothe theory and practice of database managementsystems.

Developed therelational approachtodatabase management

Seminal paper published in 1970 on A Relational Model of Data forLarge Shared Data Banks

Provided the impetus for widespread research into numerous relatedareas, including database languages, query subsystems, database

semantics, locking and recovery, and inferential subsystemsOther contributions: Boyce-Codd Normal Form

Online analytical processing (OLAP)


Donald E. Knuth


For his major contributions to the analysis of


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For his major contributions to the analysis ofalgorithms and the design of programming languages.

Author of the multi-volume book series The Art of ComputerProgramming

First volume published in 1968, seven volumes planned, currently working onfourth volume

One of the most highly respected references in the computer science field

Created the field of rigorousanalysis of algorithms

Creator of theTEX typesetting systemand of the Metafont font designsystem


Alan J. Perlis

First recipient of the Turing award in 1966

For his influence in the area of advanced


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For his influence in the area of advancedprogramming techniques and compilerconstruction.

One of the developers of the ALGOL programminglanguage


How to become a Turing award winner

To increase your chances to become a Turing award winner it might beadvantageous to work in one of the following fields:


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programming language design and implementation(Backus, Floyd, Hoare, Milner, Naur, Iverson (APL), Dijkstra, Naur,Perlis (Algol), Fortran (Backus), Pascal, Modula (Wirth), Dahl,Nygaard (Simula), Kay (Smalltalk))program compilation(Cocke, Perlis), program optimisation(Allen)

program verification(Floyd, Pnueli)analysis and theory ofalgorithms includingcomplexity theory(Blum, Cook, Hopcroft, Hartmanis, Knuth, Karp, Rabin, Scott, Stearns,Tarjan, Yao)theory and practice ofdatabases(Bachman, Codd, Gray)theory and practice ofoperating systems(Brooks, Corbato, Ritchie,Thompson, Lampson)artificial intelligence(Feigenbaum, Minsky, Newell, Reddy, Simon)


Practical 1 Reading research papers


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Research Methods in Computer ScienceLecture 8: Reading research paper

Ullrich Hustadt




Topics


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18 Practical 1

19 Reading research papers



Todays questions


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1 Each of you has compiled a list of five to ten concepts that you did notunderstand. Discuss those, see whether someone else in your groupcan give you an explanation, then, as a group, compile a short list ofconcepts that remain unclear

2 For each of the papers list at least three claims that they put forwardand note what evidence they provide to support those claims




Practical 1: Claims and Evidence

Paper 1: A SAT-based decision procedure for ALC


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Paper 1: A SAT baseddecision procedure for ALC

1 Ksatoutperforms KRISby several orders of magnitude empirical evidence on randomly generated samples

2 SAT-based decision procedures are intrinsically bound to be moreefficient than tableau-based decision procedures in contrast to SAT-based procedures, tableau-based proce-

dures consider the same truth assignment more than once

3 There is partial evidence of an easy-hard-easy pattern empirical evidence; easy-hard-easy pattern evident for

Ksat(but not KRIS)




Paper 2: On evaluating decision procedures for modal logic


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1 Ksatdoes not qualitatively outperform KRIS withpre-processing empirical evidence on the same randomly generated sam-

ples, but using KRIS with pre-processing

2 Non-eager application of simplifications causes inferiorperformance ofKRIS (even with pre-processing) empirical evidence and analysis of behaviour ofKRIS

3 Easy-hard-easy pattern is an artificial phenomenon ofKsat empirical evidence; translation approach has no easy-hard-

easy pattern although it solves the hardest samples fasterthan Ksat





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Paper 3: More evaluation of decision procedures for modal logics1 All the claims in Paper 1 are correct empirical evidence on a new, less flawed set of randomly

generated samples; repetition of the argument about truth

assignments2 KsatC also outperforms the translation approach empirical evidence



Practical 1: Conclusion

Tableau methodsconstruct refutations bycase distinctionand theapplication ofdecomposition rules


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SAT-based procedurescan be seen astableau methodsusinga specific kind ofcase distinctionanda specificstrategyfor the application ofdecomposition rules

Question is nottableau-basedvsSAT-basedprocedures, but

what kind ofcase distinctionis best (if any) andwhatstrategyfor the application ofdecomposition rulesisbest (if any)

These questions are still open

sets of modal logic/description logic expressions are infinitelack of real-world samples



Practical 1: Learning points

Regardingresearch papers:


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Notions need precise definitions

Claims need to be formulated unambiguously

Evidence needs to be constructed carefully

Regarding theresearch process:

Peer review does not prevent mistakes

Research can progress without resolving contradictions

Research is also a social process



Reading research papers


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Researchaims to add theworlds body of knowledge Requires a researcher to be aware of what the

worlds body of knowledge(in the area s/he works in)

Frontiersof theworlds body of knowledge arenotdocumented intext

books, but injournal articles

reliability conference papersworkshop papers timelinesstechnical reports



Get organised

Maintain a database of all the books and papers you read

Data stored should at least include title, author, place of publication,


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and storage locationPreferably you should also keep a record of the answers to some or all ofthe following questions:

1 What is the main topic of the article?2 What was/were the main issue(s) the author said they want to discuss?

3 Why did the author claim it was important?4 How does the work build on others work, in the authors opinion?5 What simplifying assumptions does the author claim to bemaking?



Get organised

Maintain a database of all the books and papers you read

Data stored should at least include title, author, place of publication,


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and storage locationPreferably you should also keep a record of the answers to some or all ofthe following questions:

6 What did the author do?7 How did the author claim they were going to evaluate their work and

compare it to others?8 What did the author say were the limitations of their research?9 What did the author say were the important directions for future research?



Evaluating research papers

Whenever you read a research paper, you should try toevaluateat the


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same time.Try toanswer the following questions:

1 Is the topic of the paper sufficiently interesting (for you personally or ingeneral)?

2 Did the author miss important earlier work?

3 Are the evaluation methods adequate?4 Are the theorems and proofs correct?5 Are arguments convincing?6 Does the author mention directions for future research that interest you?

Given the answers to these questions for a number of research papers,

you should be able to construct a research proposalby considering howyou could improve the work presented in them


Structure of research papers Hints


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Research Methods in Computer ScienceLecture 9: Structure of research papers

Ullrich Hustadt




Previously . . .


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18 Practical 1

19 Reading research papers



Topics


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20 Structure of research papers

21 Hints



Todays questions

Taking the research papers you have been given and others that you may

h i h (h f ll ) i l


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have come across in the past as a (hopefully) representative sample,consider the following questions:

1 What elements constitute the structure of the papers? Are theelements and their order identical for all the papers? If not, whichelements do the papers have in common and which elements only

appear in only some of the papers?2 What characterises each of the elements of the papers? That is,

looking at each element of each of the papers, what do they havecommon?




Structure of a research paper

1 Title

2 Li t f th ( d th i t t d t il )
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2 List of authors (and their contact details)3 Abstract

4 Introduction

5 Related Work (either part of or following introduction or before

summary).6 Outline of the rest of the paper

7 Body of the paper

8 Summary and Future Work (often repeats the main result)

9 Acknowledgements10 List of references



Title

As short as possible, but without abbreviations or acronyms(unless they are commonly understood)

A ifi d l ibl


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As specific as necessary and as general as possible(e.g.The Complexity of Theorem-Proving Procedures

introduced the notion of NP-Completenessstarting point ofcomplexity theory)

Include key phrases which are likely to be used in a search on the topicof the paper(e.g.modal logic, calculus, decision procedure)

Avoid phrases which are too common(e.g. novel)

Use phrases that describe distinctive features of the work(e.g.Real-world Reasoning with OWL)



Authors (1)

Anauthorof a paper is an individual who1 made a significantintellectual contributionto the work described in the

paper


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paper(in contrast, for example, to amonetary contribution);2 made a contribution todrafting, reviewing and/or revisingthe paper for

itsintellectual contribution(in contrast, for example, tospell checkingortypesetting); and

3 approved the final version of the paper including references

Some organisations / publishers have strict rules regardingauthorship

Order ofauthorsmay depend onsubject area: pure theory oftenalphabetical

applied research often based oncontribution

research assessment(e.g. bibliographic measures associating order with contribution)

cultural context



Authors (2)

In Computer Science,academic degreesandmembership of professionalorganisationsare typically not indicated

List of authors is typically followed by contact information consisting of


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List of authors is typically followed bycontact informationconsisting ofaffiliationande-mail address(not postal address)

Some journals allow authors to provide longer descriptions of themselvesincluding photographs



Abstract

Typically not more than 100150 words

Should aim tomotivatepeople to read the paper

Highlight the problem and the principal results


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Highlight theproblemand theprincipal resultsThe abstract will be included inliterature databases Make surekey phraseswhich might be used in searches are included

(same principle as for titles)

Keepreferencesto a minimumKeepequationsand othermathematical expressionsto a minimum



Introduction

State the generalarea of research(unless this is obvious from the context in which the paper appears)

Introduce the problem /


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Introduce theproblemstate why the problem is important and/or interesting

Outline theapproachtaken to solve the problem

Outline thesolutionorprincipal resultsstate why the results are important and/or interesting

Do not repeat theabstract

Avoid platitudes and cliches



Related work

Related workis previous work by the same or other authors whichaddresses the same or closely relatedproblems/topics

Section on related work gives cr

all lectures about articles

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