1. LOG6306 : Études empiriques sur les patrons logiciels
Course 1 – Research and course content

2. Preamble (1/3)
Welcome into the programs of DESS, M.Sc.A., M.Sc., and Ph.D. at DGIGL

3. Preamble (2/3)
DESS : « complete their engineering degree with graduate courses. »
M.Sc.A et M.Sc. : « broaden technological and scientific knowledge about computer engineering. »
Ph.D. : « to graduate students with detailed knowledge, academic rigour, scientific curiosity, and creativity »

4. Preamble (3/3) Engineering focuses on solutions...
… research focuses on problems!

5. Definitions (1/9)
Scientific research is the set of actions and tools used to produce and develop scientific knowledge
By metonymic extension, scientific research includes also the social, economical, institutional and legal contexts of the actions

6. Definitions (2/9)
Scientific knowledge include any piece of knowledge of universal value, characterised by a domain and a method and based on objective and verifiable relations

7. Definitions (3/9)
Universal and fundamental, perennial  technologies!
It’s the priority
It’s the contribution

8. Definitions (4/9) Concretely, these are Theories Hypotheses
Implementation
Evaluations
Experimentations
Discussions
Critics

9. Definitions (5/9) Theories
A model or a framework to understand nature or people, often based on observations and experiences
Few theories in software engineering
Immaterial as mathematics!
Too many variables in the observations?

10. Definitions (6/9) Hypothesis
Proposition or explanation that we state without taking position on its truth, i.e., without affirming or negating it
You must always have hypotheses
To follow the experimental method
To be systematic
To be objective et dispassionate
To reduce the number of variables

11. Definitions (7/9) Implementation
Creation of a product from design documents, a specification, or directly from an idea
You must always have an implementation
To show a proof-of-concept
To allow experimentations
Knowing how “to code”, it is like knowing how to use a hammer for a carpenter

12. Definitions (8/9) Evaluations
Method to evaluate a result and thus to know the value of the results that cannot be directly measured
You must always evaluate your results
Minimum: compare with the state of the art
Experimental method
Users
Oracles

13. Definitions (9/9) Discussions
Putting into perspective the advantages and limitations of a scientific result or of a research product
You must always discuss your work
Be critical
Accept critics
Be critical of the work, not the person!
Only means to advance knowledge

14. Actions (1/5) Identify/define a relevant problem
If possible within a theory
From a set of reasonable hypotheses
From an intuition or a known problem
Thanks to your supervisor’s suggestion 
Relate this problem to the state-of-the-art
Read, read, read!
Critical reading (cf. reading notes)
(Potentials co-researchers, cf. economy)

15. Actions (2/5) Search for a solution Research per se
No out-of-the box solution but
Existing solutions in the state of the art
Recurring tools (cf. tools)
Discuss around you, a solution may exist in another domain
Constraint programming, economy…

16. Actions (3/5) Implement the solution
A theoretical solution is interesting
An implementation is even better!
Implement
Quickly
Cleanly
Efficiently
Tests, tests, tests!
A lack of expertise is no excuse!

17. Actions (4/5) Evaluate and discuss the solution Qualitative comparison
With the state of the art
Quantitative comparison
With an oracle
Varying different parameters
User studies
Performance studies
Theoretical and practical

18. Actions (5/5) Conclude and identify future work
Advantages and limitations
Of the solution
Of its implementation
Go back to the evaluation
More evaluations
Improvements …

19. Tools (1/6) Code analyses Static (e.g., PADL) Dynamic (e.g., MoDeC)
Versions (e.g., Ibdoos)
Metrics

20. Tools (2/6) Search algorithms Taboo search Simulated annealing
Genetic algorithm

21. Tools (3/6) Statistics Correlations (Pearson, Spearman…)
Hypotheses testing
Precision and recall, F-measure
Odds ratios
(Cf. R)

22. Tools (4/6) Clustering and rules Clustering Formal concept analysis
Bayesian networks
Rule-based classifiers
(Cf. Weka)

23. Tools (5/6) User studies Questionnaires
Usability tests (e.g., eye-tracking)
Cross validation

24. Tools (6/6) Performance Linguistic analyses Profiling
Varying inputs, parameters
Linguistic analyses
LSI
WordNet
Domain analysis
Systematic literature survey

25. Frameworks (1/5) Social Context Beliefs Return on investment
Multicultural society, Descartes’ dualism mind-body
Beliefs
Freedom of thoughts and religion
Scientific!
Return on investment
Software engineering on the cover of Science or Nature? 

26. Frameworks (2/5) Economics Society invests money
the money through grants
Funding agencies
split and share
Professors apply
to grants and
fund students
Professors and students carry out research work
Professors and students publish and spin-off results
evaluate publications and results of spin-offs
Funding agencies
International agencies
compare and publish results of comparisons
(Return on investment for the society)
Companies invest

27. Frameworks (3/5) Economics
One of professors’ top priority: funding (students too!)
Obtained grants
Jobs
Patents
Spin-offs
Money produced
Society
International competition

28. Frameworks (4/5) Institutional Costs Means Taxes Fees
Investment from the society
Labs.
Offices, networks, prints…

29. Frameworks (5/5) Legal Research ethics Social responsibilities
Laws
Environment
Respect
Professional responsibilities
Image
Decisions
Ethics