UMR 5205 "Methodology" of research in computer science Lionel Brunie National Institute of Applied...

UMR 5205

"Methodology"of

research in computer science

Lionel Brunie

National Institute of Applied Sciences (INSA)LIRIS Laboratory/DRIM Team – UMR CNRS 5205

Lyon, France

http://liris.cnrs.fr/lionel.brunie

Master Course, Lyon, January 2015 - Methodology of Research in CS

Agenda

(Disclaimer)

Discovery vs. invention

What is a research result in computer science?

What is a research paper in computer science?

General structure(s) of a research paper in CS

Searching for black boxes, traps and black holes

How is research evaluated?

And now?

Master Course, Lyon, January 2015 - Methodology of Research in CS 3

Agenda

Disclaimer



What is a research paper in computer science?

General structure(s) of a research paper in CS



And now?


Disclaimer

This is NOT a lecture !

I apologize not to be an epistemologist

I am just a practitioner of computer science that have co-

authored some research papers and read/evaluated several

hundreds ones

This open discussion is absolutely NOT objective

Goal : open the windows and discuss how we ACTUALLY

work (and not how we should work…)

All questions, remarks, contradictions are welcome!!!


Agenda

Disclaimer

Discovery vs. inventionWhat is a research result in computer science?

What is a research paper in computer science ? General structure(s) of a research paper in CS



And now?


Discovery vs. Invention

My feeling is that there are two main ways of practicing science : discovery vs. invention

Biologists, physicists, chemists, researchers in psychology… are discoverers

(at least, software) Computer scientists, researchers in nanotech, researchers in process engineering or in industrial engineering… are inventors

However inventing often needs a priori discoveries (e.g., of your target/domain of interest); discovering often needs inventing (e.g., measuring instruments)

Mathematicians… are mathematicians :-)


Discovering (just two words)

Understanding the world : what are atoms constituted of, why a disease in inherited, why do people have dreams, etc.

Understanding means first asking questions, then observing, inquiring, modeling, evaluating

At the end of the research process, one has an answer to the initial question (question of research)

An answer is the most often not definitive. It is an explanation of a small piece of the natural world under some hypotheses

Famous recent example: the “proof of the existence” of Higgs’ boson at CERN/LHC


Inventing (1/2)

Software computer science produces inventions

Computers do not exist by themselves. They have been created by human beings => there is nothing to discover in a computer or in a software

The objective of research in CS is “just” to make computers/computer networks and computer applications more efficient, more easy to use, more reliable, more powerful… i.e. more useable/useful


Inventing (2/2)

As a consequence, a research result in (software) CS has no (real) intrinsic value. It has only the value that the research community and/or the society gives to it. A useless invention has no value!

This has to be moderated when one considers research in fundamental and theoretical CS where people study formal problems => context actually close to research in maths

This has also to be moderated when one considers social- or bio- or something-inspired CS where researchers attempt to understand and model an “external” (social or biological or…) phenomenon


Agenda

Disclaimer


What is a research result in computer

science?

What is a research paper in computer science? General

structure(s) of a research paper in CS



And now?


A difficult answer…

The endless debate about fundamental research, basic research, applied research, finalized research…

A first and definitive answer: a result has some scientific value as soon as it has been accepted by a scientific publication (a scientific committee), i.e., research is a social activity (hence, fashions, trends…)

A second answer: a result has some scientific value if it interests people and if it is novel (original) (and if it has some genericity) i.e., refer to the scientific method

The most difficult (and so the most interesting) problems have often been raised by applications, e.g. medical applications. Investigating these problems have allowed going through theoretical scientific developments


Agenda

Disclaimer



What is a research paper in computer science? General structure(s) of a research paper in CSSearching for black boxes, traps and black holes


And now?


Introduction-Motivations

State of the art

Proposal

Experiments

Discussion

Conclusion and future work

=> Computing the oror!

Structure of a research paper in CS


3 (+1) key sections (1/4)

Having 1 very good idea in a scientist’s life is… very good; having two is exceptionalOne paper => one problem and at most one (two) contribution(s) to defend

A first key section: the statement of the problemWhy is the problem important? Remark: notion of fashion (e.g., Big

Data)Why is the problem difficult (→ link to the state of the art)?What are the hypotheses? What is/are the context/conditions?What are the objectives i.e., what are the performance/quality

criterions with respect to which the contribution should be evaluated?

Then, add the outline of the contribution


3 (+1) key sections (2/4)A second key section: the state of the art

A state of the art must not be “flat” or general purpose

It must be focused on your scientific and applicative targets and “critical and compared/taxonomic”

Critical: the contribution/limitations of each cited paper should be analyzed wrt your problem

Compared: papers should be organized in taxonomies

The state of the art establishes/describes the scientific basis wrt which your work should be compared, i.e., your // will be evaluated

Ideally, all pertinent papers should be analyzed. Practically, it is often untracktable

“Making the bibliography” is a key component of the scientific method



A third key section: the discussionGoal of the discussion: analyzing your

contribution/proposalWrt to your initial objective/motivationWrt to the state of the artWrt the experiments you have run… and have not runWrt the implementation choices you have madeWrt genericity

A discussion is not a conclusion

The discussion is often distributed over the paper ( :-( ?)

Do not believe authors: make your personal discussion!



A fourth obvious section : your contributionMust be “sound”

Must be comprehensive and complete

Should allow people to reproduce the experiments you use

Must be self-contained


A short focus on the“Introduction – Motivation” section

Inventing some “thing” that interests nobody has no real senseInventing some “thing” that addresses a problem that has an existing satisfactory solution has a limited interestTo be interesting, a work/paper should address a problem :

With a high (scientific or economic or social or…) valueAnd no solution

(already noted) A paper should list the criteria wrt which authors consider that their proposal is to be evaluated


Experiments (1/3)

The often dark side of research in CS

A first reason : cultural/educational : computer scientists have an unsatisfactory education in statistics/experiment planning/performance evaluation

However, things become better :-)


Experiments (2/3)

A second reason : real life experiments are often intractable because comparing two inventions is often intractable

Concurrent software are often not availableConcurrent complex systems/softwares cannot be re-

develop from scratch at a reasonable priceEvaluation criteria are differentUsers of your software are reluctant to spend/waste their

time on alpha versionsIt often exists no benchmarksResearch works often address very small range/specific

issues that are part of a complex systems but integration is often not realized

Research teams have neither the manpower nor the competence to develop “products” and limit their development to demonstrator or prototypes that cannot be tested by real end users


Experiments (3/3)

However experiments are mandatory to evaluate the pertinence of your work

Experiments should allowTo evaluate the performance/pertinence/efficiency of your

proposal wrt your initial objective and wrt all the possible contexts (not only in the best case)

To evaluate the influence of the various parameters on the performance

To compare your proposal wrt the state of the art

Experimental conditions must be made completely explicit


Agenda

Disclaimer



What is a research paper in computer science? General structure(s) of a research paper in CS

Searching for black boxes, traps and black holesHow is research evaluated?

And now?


Reading a paper as an investigation

A key behavior: criticism (cf. methodical doubt by Descartes)

A first obvious step : analyze what is written is the proposal scientifically sound?are the performances good?is the problem really important?is the state of the art exhaustive?etc.

A second step : analyze what is not written


Black boxes and assumptions

Computer systems/applications/software are too complex/too large so that a single team/paper can address the entire problem

=> Focus on a small part or specific issue

=> Assumptions on the other parts

+ inputs: it exists/will exist (soon) some “system” can can provide your prototype with the data/information it needs

+ outputs: the data produced by your prototype could be/will (soon) be used by the end-user side of the global system

Assumptions are not always explicit

Always ask yourself if these assumptions are valid/realistic, what do they imply


Analyzing experiments (1/3)

The border line“Science” definitely condemns treachery and falsification:

modifying the result of an experiment is beyond the “red line”Experimental measurements that are presented in a paper are

(supposed to be) true (I personally have always supposed it)The question is about omitted/”forgotten” resultsThe question is about uncertainty and statistical errors on the

measurementsThe question is about some generalization of

results/experimentsFrom a scientific point of view, a negative result is often as

interesting as a positive resultHowever, as CS has to deal with invention, a negative result is

usually considered as a depreciated fact



So the temptation to “avoid” including negative measurements often exists:

“This measurement is not really important but as I do not have enough space to explain it, it is more important, from a scientific point of view (of course), to discuss in deep other more important experiments”

“The situation that leads to this bad result never happens in the real life”

“With a slight modification of the prototype, it is obvious that one would never get such a result, so let us focus on the results concerning the core of the prototype”

etc.

are reasons that a scientist may use to self-persuade him that presenting this experiment is not pertinent



Where is the yellow line? Where is the red line?

Always analyze if experiments are exhaustive

If not, always analyze if the missing experiments are

important to evaluate the performance of the prototype

and try to infer what could be the behavior of the

prototype wrt these experiments


Agenda

Disclaimer





How is a research paper evaluated?And now?


Evaluation of a research paper

Is the topic of the paper in the domain of interest of the conference/journal?

(Is the bibliography complete, critical and taxonomic?)

Is the contribution original?

Is the paper technically sound/correct?

Is the paper easy to read?

Is the contribution significant?

What is the level of expertise of the evaluator / what is the confidence of the evaluator in his judgement

Discussion within the program or scientific committee


Menu

Disclaimer






And now?


Conclusion?

Read, read, read, read!

Be positively critical

Never admit supposed evidences. Always doubt (Descartes)

All questions are good

Be modest


Bring your creativity!

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