Presenting my thesis during the National Thesis Contest in Computer Science - top 6 PhD Computer Science Thesis in Brasil/ 2013. XXXIV Congresso da Sociedade Brasileira de Computação (CSBC 2014) - CTD.

1. PRODUCTIVITYOFAGILETEAMS:
ANEMPIRICALEVALUATIONOF
FACTORSANDMONITORINGPROCESSES
CSBC 2014 – XXVII CTD
Dr. Claudia Melo
Advisor: Prof. Dr. Fabio Kon
Department of Computer Science, IME-USP

2. OUTLINE
Context & Definitions
Problem statement
Research questions
Research methods & Overview of all studies
Results & Implications for research and industry
Other contributions
3

3. GRAND CHALLENGE – AN INSTANCE
UK government case
Savage, M.: Labour’s Computer Blunders cost 26bn. The Independent, Tuesday 19 January, London (2010)
4

4. UNCOMFORTABLE TRUTH
We are still not very good at software engineering.
§ inability to deal with change requests in the requirements;
§ failure to communicate between the developers and stakeholders;
§ no clear requirements definitions.
Anthony J H Simons and W Michael L Holcombe, Vision Paper: Remodelling Software Systems – the 2020 Grand Challenge for
Software Engineering
5

5. 6
SOFTWARE PRODUCTIVITY IS A GENERAL
STRATEGIC CONCERN IN SEVERAL
INDUSTRIAL SECTORS

6. WORK HAS CHANGED IN
THE 21ST CENTURY
Optimization
Mechanistic
Process centric
Stable, predictable
Individual
Efficiency
Adaptation
Organic
People centric
Turbulent, difficult to predict
Team
Knowledge work
Productivity =
Output/Input
Productivity = ?
7

7. “THEMOSTVALUABLEASSETOFA21STCENTURY
INSTITUTIONWILLBEITSKNOWLEDGEWORKERS
ANDTHEIRPRODUCTIVITY”
PETERDRUCKER,1999
8

8. KNOWLEDGE WORKER PRODUCTIVITY
9
Y. W. Ramírez and D. A. Nembhard, “Measuring knowledge worker productivity: A taxonomy,” Journal of Intellectual Capital, 2004.9
Quantity
Quality
Efficiency
Effectiveness
Timeliness
Profitability
Responsibility
Autonomy
Customer
Satisfaction
Creativity/
Innovation
Project Success
Knowledge
Worker
Productivity
Continuous life-
long learning

9. Agile Manifesto: values and principles
Scrum, XP, Lean software development, Feature Driven Development, DSDM,
Crystal etc. 10

10. “The appearance of Agile methods has
been the most noticeable change to
software process thinking in the last
fifteen years”
Fowler M. (2005). The New Methodology,
www.martinfowler.com.
“Agile methods rapidly joined the
mainstream of development
approaches”
Forrester Research 2010. Agile development: Mainstream
adoption has changed agility - trends in real-world adoption of
agile methods. Technical report, January.
11

11. OPEN PROBLEMS
Productivity definition in the 21st century in the context of software
development, particularly agile teams?
• E.g: Riding the paradox between flexibility and efficiency; Re-thinking studies around productivity
Recent studies discuss productivity factors
• None considers factors impacting agile teams.
• To manage productivity effectively it is important to identify the most relevant difficulties and develop strategies to
cope with them.
High-performance self-organized teams should continuosly monitor their
own performance
• How agile teams can monitor their own productivity?
• How to consider adaptability in this monitoring?
12

12. NATURE OF
THE
PROBLEMS
Social context and technological content are essential to a
proper understanding of the software development.
Mismatch between current used productivity definitions and
actual productivity in the 21st century
• Sometimes paradoxical
Productivity measurement is hard. KW worker productivity might
be extremely hard to measure
As a complex system, there are many possible factors
influencing productivity, it is hard to interpret
• Triangulation of data sources, methods, theories and researchers is necessary
Highly embedded in the industry context
• Manage risks of partnership with industry
13
“the most important
figures that one needs
for management are
unknown or unknowable,
but successful
management must
nevertheless take account
of them.”
W. E. Deming (1986)
Out of Crisis.

13. RESEARCH QUESTIONS
RQ1. How important is productivity for companies adopting
agile methods and how do they define productivity?
RQ2: What factors impact agile team productivity and how is this
impact from the team point of view? Which agile practices are
perceived to impact on a given team’s productivity?
RQ3: How to monitor productivity factors, considering agility and
adaptability? How do agile metrics support productivity
monitoring?
14

14. OVERVIEW OF ALL STUDIES
Multiple-case studies on Agile
team productivity definitions and
agile team productivity factors
2010-2011
Survey
on Agile productivity
expectations
and benefits
2011-2012
Action research for
exploring and assessing
productivity monitoring and
measurement in agile teams
2012-2013
Warm-up
studies on Agile
methods impact on
productivity
2009-2010
Study of software
productivity
definitions, factors
and metrics
2009-2010
Study of Agile productivity
metrics and performance
monitoring, measurement
dysfunctions, and monitoring in
self-managed teams
2011-2012
Phase I Phase II Phase III
P1,P2
P4, P6,
P7, P9
P3
IR1
P5, P8
IR2
Research study
Paper
Industry report
In collaboration with Norwegian University of Science
and Technology
August 2009 March 2013
15

15. RESEARCH METHODS
• Quantitative studies by exploring the importance of
productivity for agile teams and related context
• Qualitative studies by exploring and explaining factors
and monitoring approaches
• Sometimes using quantitative data
• The rationale for mixed methods has been:
• Triangulation
• It is useful to take benefit from all available data
• Answering questions that are not possible to answer otherwise
16
16

16. Web-based survey in Brazil (May, 2011 – October, 2011).
Organizations adopting agile methods to develop software.
■ Industry and Universities.
■ 471 respondents, 17 states
Exploratory research using non-probabilistic sampling
Snowball sampling.
Convenience sampling.
■ Mailing lists, attendees of past agile conferences, and Agilcoop business contacts.
Statistical analysis: descriptive and inferential
Open data, Replication.
17
17
SURVEY

17. MULTIPLE-CASE STUDIES – RESEARCH METHOD
3 large Brazilian companies (> 250 employees) : Financial, Cloud
computing/data center, Internet content and services
3 types of data sources (~6 months collecting data): Semi-
structured interviews (19), Retrospective sessions
documentation, Observation field notes
Data analysis and synthesis method:
• Cross-case analysis, data source/theory/researcher
triangulations
• Thematic analysis1 and Thematic Networks2
• Data-driven approach (Inductive)
181 Boyatzis, R. E., 1998. Transforming qualitative information: thematic analysis and code development. Sage Publications.
2Attride-Stirling, J., Dec. 2001. Thematic networks: an analytic tool for qualitative research. Qualitative Research 1 (3), 385–405. 18

18. 19
1919
NVivo 9
• 6-Month Interviews transcribed
in 400 pages + Observation
notes + Artifacts
• Research and data source
triangulation, incrementally
analyzed in 12 months
• Conceptual framework
developed in the first months.
Updated in the last months.
ANALYZING QUALITATIVE DATA

19. ACTION RESEARCH METHOD
10-month action research
• Strongly oriented toward collaboration and change (researchers & subjects).
• Iterative research process
• Solve practical problems while expanding scientific knowledge
• Capitalizes on learning by both researchers and subjects within the context of
the subjects’ social system
Multinational company
Distributed project on B2C
National research team
Multi-method data collection, Triangulation
Thematic analysis
20

20. 11 FINDINGS
21

21. 22
KEY FINDING 1: PRODUCTIVITY AS AN IMPORTANT
REASON FOR ADOPTING AGILE METHODS
count
Champion
Developer
Team leader
Project manager
Development manager
CIO/CTO
President/CEO
26.3%
23.6%
13.8%
11.5%
10.4%
8.3%
0 50 100 150
count
Worries
Lack of documentation
Lack of predictability
Lack of upfront planning
Loss of management control
Lack of team training
Development team opposed to change
Lack of engineering discipline
Regulatory compliance
Reduced software quality
51%
43.5%
41%
37.6%
34.8%
32.1%
25.7%
24.8%
21.2%
0 50 100 150 200 250
Percentage
ReasonsforadoptingAgile
Accelerate time to market
Enhance ability to manage changing priorities
Enhance software maintainability extensibility
Enhance software quality
Improve alignment between IT and business
Improve engineering discipline
Improve project visibility
Improve team morale
Increase productivity
Reduce cost
Reduce risk
Simplify development process
73%
86%
66%
83%
72%
59%
65%
64%
91%
47%
69%
80%
27%
14%
34%
17%
28%
41%
35%
36%
9%
53%
31%
20%
0 20 40 60 80 100
Response
Highest importance
Very important
Somewhat important
No important at all
MELO, C. O.; SANTOS, V. A.; CORBUCCI, H.; KATAYAMA, E.; GOLDMAN, A.; KON, F. Agile methods in Brazil: state of the practice in teams and organizations
(in Portuguese). Technical Report MAC-2012-03. Department of Computer Science IME-USP. May, 2012. http://agilcoop.org.br/MetodosAgeisBrasil2011.
CORBUCCI, H. ; GOLDMAN, A. ; KATAYAMA, E. ; KON, F. ; MELO, C. O. ; SANTOS, V. S.. Genesis and Evolution of the Agile Movement in Brazil – A
perspective from the Academia and the Industry. In: Proceedings of 25th Brazilian Symposium on Software Engineering (SBES), 2011, pp. 98-107.

22. 23
KEY FINDING 2: PRODUCTIVITY AS PERCEIVED
BENEFIT FROM ADOPTING AGILE METHODS
23
MELO, C. O. ; KATAYAMA, E.; SILVA, V. S.; CORBUCCI, H.; PRIKLADNICKI, R. GOLDMAN, A.;KON, F. Genesis and Evolution of the Agile
Movement in Brazil – A perspective from the Academia and the Industry. Journal of Brazilian Computer Society 19(4):523-552 (2013).

23. KEY FINDING 3:
REASONS AND PERCEPTION OF PRODUCTIVITY
WHEN ADOPTING AGILE METHODS
ARE NOT ASSOCIATED
WITH THE COMPANY SIZE
NOR EXPERIENCE WITH AGILE
(SPEARMAN’S RANK-ORDER - rho - CORRELATION TEST)
MELO, C. O. ; KATAYAMA, E.; SILVA, V. S.; CORBUCCI, H.; PRIKLADNICKI, R. GOLDMAN, A.;KON, F. Genesis and Evolution of the Agile
Movement in Brazil – A perspective from the Academia and the Industry. Journal of Brazilian Computer Society 19(4):523-552 (2013).
24

24. KEY FINDING 4:
AGILE PRACTICES ADOPTED BY
COMPANIES PERCEIVING SIGNIFICANTLY
IMPROVED PRODUCTIVITY
ARE
ITERATION PLANNING,
RETROSPECTIVES,
UNIT TESTING,
DAILY STANDUP, AND
REFACTORING
MELO, C. O. ; KATAYAMA, E.; SILVA, V. S.; CORBUCCI, H.; PRIKLADNICKI, R. GOLDMAN, A.;KON, F. Genesis and Evolution of the Agile
Movement in Brazil – A perspective from the Academia and the Industry. Journal of Brazilian Computer Society 19(4):523-552 (2013).
25

25. 26
E.g.: Timeliness, Quantity (~traditional productivity
definition), Quality, Customer satisfaction
KEY FINDING 5: THE DEFINITION OF AGILE TEAM
PRODUCTIVITY IS DIFFUSE
MELO, C. O. ; CRUZES, D. S. ; KON, F. ; CONRADI, R. Agile Team Perceptions of Productivity Factors. In: Proceedings of the
Agile Development Conference (AGILE), Salt Lake City, USA, 2011, pp. 57-66.

26. 27
KEY FINDING 6: AGILE TEAM PRODUCTIVITY
FACTORS ARE STRONGLY RELATED TO TEAM
MANAGEMENT
MELO, C. O. ; CRUZES, D. S. ; KON, F. ; CONRADI, R. Interpretative Case Studies on Agile Team Productivity and Management.
Information & Software Technology 55(2): 412-427 (2013).

27. 28

28. 29
KEY FINDING 7: PAIR PROGRAMMING AND
COLLOCATION AS KEY PRACTICES
MELO, C. O. ; CRUZES, D. S. ; KON, F. ; CONRADI, R. Agile Team Perceptions of Productivity Factors. In: Proceedings of the Agile Development
Conference (AGILE), Salt Lake City, USA, 2011, pp. 57-66.

29. 30
KEY FINDING 8: NEW MOTIVATORS MIGHT
INFLUENCE AGILE TEAM PRODUCTIVITY
e.g.: Challenging work, participation,
sense of contribution and progress.
MELO, C. O. ; SANTANA, C.; KON, F. Developers motivation in agile teams. 38th Euromicro Conference on Software Engineering and
Advanced Applications (SEAA), Çesme, Izmir, 2012, p. 376-383.

30. I1. Group characteristics
Team design (e.g., team size,
collocation, team, diversity)
Team member turnover
Beliefs
I2. Stage of team
development
I3. Nature of task
(e.g., task design, task duration,
team autonomy,
interdependency)
I4. Organizational context
I5. Supervisory behaviors
(e.g., transactional versus
transformational, degree of
supervision – directive or self-
managed teams)
G1. Internal and External
processes
(e.g., Cohesion,
Communication, Conflict
management, Coordination,
Sharing of expertise, Work
procedures)
Inputs
O1. Agile team
productivity outcomes
(Team's perception on
dimensions of productivity,
e.g., Customer satisfaction,
quantity of work, innovation,
creativity, timeliness, product
quality, absenteeism,
profitability, and team
efficiency and effectiveness)
O2. Attitudinal and
Behavioral outcomes
Outcomes
Group processes
AGILE TEAM PRODUCTIVITY CONCEPTUAL FRAMEWORK
31

31. UPDATED AGILE TEAM PRODUCTIVITY CONCEPTUAL
FRAMEWORK
Inputs OutcomesGroup processes
Conflict management
Agile team
productivity
Stage of team
development
Member turnover
Sharing of (new) expertise
Intrateam coordination
Agile practices establisment
(work procedures)
Agile practices establisment
(work procedures)
Group characteristics
Team design
Personality
Behavioral outcomes
Member turnover
Teammembersturnover
Agile team
productivity
Sharing of expertise
Intra team coordination
Conflict management
Intra team coordination
Communication
Group characteristics
Team design
Attitudinal outcomes
Commitment
Teamdesignchoices
Small teams
Diversity
(mixed teams)
Full-time
allocation
Collocation
Communication
Cohesion
Planning/RE negotiation
(work procedures)
Intra team coordination
Agile team
productivity
Nature of task
Team autonomy/
interdependency
Attitudinal outcomes
(lack of)
Commitment
Interteamcoordination
Inter team coordination
Agile practices establisment
(work procedures)
InterteammanagementIntrateammanagement
MELO,C.O.;CRUZES,D.S.;KON,F.;CONRADI,R.InterpretativeCaseStudiesonAgileTeam
ProductivityandManagement.Information&SoftwareTechnology55(2):412-427(2013).
32

32. 33
33
Diagnosing
Typical day
- daily meeting
- update task board
- update burndown and
selected metrics (if
applicable)
Agile project - Release n
Retrospective...
Action Planning Action Taking Evaluating Specify learning
1. Appreciate
problem situation
through:
- Focus groups
- Problem solving
template
- Self-assessment
questionnaire
- Researcher
Immersion and
previous
knowledge of
the company
2. Study
literature:
- Productivity
metrics for the
context
3. Select solution
approach through:
- Focus groups
4. Develop solu-
tion framework:
- Data collection
method and
frequency
- Tools
5. Apply approach
6. Monitor
through:
- Observation
- Informal
meetings
- Project events
7. Evaluate
experiences
through:
- Focus groups
- Informal
meetings
Planning
8. Assess metrics
usefulness:
- Focus groups
- Interviews
- Self-assessment
questionnaire
9. Elicit research
results
releases

33. CYCLE 0: JUN, 2012 – AUG, 2012
Monitoring productivity:
• Process: efficiency and speed
• Product: timeliness
Learning outcomes:
• Productivity definitions between client and team were
misaligned
• Disfunctional measurement
• Confirming that agile team productivity was an issue (action
research principle)
34
34

34. CYCLE 1: SEP, 2012 – DEC, 2012
Monitoring productivity:
• Personnel: anti-patterns related to trust and motivation
• Product: quality
Learning outcomes:
• Productivity monitored through qualitative measurement
(patterns identification)
• Actions on trust and motivation prevented staff turnover
(confirming our previous conceptual framework)
35
35

35. CYCLE 2: JUNE, 2012 – AUG, 2012
Monitoring productivity:
Process: Leanness/Flow
Product: Quality
Personnel: Teamwork
Learning outcomes:
• Teamwork assessment generates insights for teamwork improvement
• Metrics/Charts have both positive and negative aspects for productivituy
monitoring
36

36. 37
KEY FINDING 9: PRODUCTIVITY MONITORING
INSTRUMENT
3737
Dimension Goal How to monitor
Product
[Quality, Innovation,
etc.]
Personnel
[Teamwork, Trust,
Motivation etc.]
Project [Speed, Scope etc.]
Process
[Leanness, Efficiency,
etc.]
Organizational
[Inter-team
coordination etc
Actions Evaluation
• 5 dimensions, from personnel to organizational aspects
• Incorporating Knowledge worker productivity aspects
• Light approach that can be incorporated by agile teams

37. 38
KEY FINDING 10: A FRAMEWORK FOR DEVELOPING
AGILE TEAM PRODUCTIVITY MONITORING
38
Design
Identifying key
monitoring
goals
Monitor
and
Measure
Review
Act
Implementation Assessing Challenging
Identifying/
Developing
[qualitative or
quantitative]
measures
Implementation of
monitoring and
measurement
Reflect
Diagnosing
Action
Planning
Action Taking Evaluating
Specifying
Learning
Developing Agile team monitoring approaches from a Practical perspective
Developing Agile team monitoring approaches from a Theoretical perspective
Dynamically review of
targets, measures,
and goals
MELO,C.O.Productivityandadaptabilityofagileteams:leveragingtheparadoxtowards
innovation(inPortuguese,toappear),In:AntologiaThoughtWorksBrasil.CasadoCódigo.2014.

38. KEY FINDING 11: PRODUCTIVITY METRICS USEFULNESS
§ No overhead
§ Productivity metrics might help just some groups
of team members
§ Metrics might drive learning and change
§ But sometimes people need guidance to enable learning
§ It was not always clear why or when some
metrics were introduced
39
3939

39. CONTRIBUTION FOR THE SPECIFIC PROJECT
§ In this particular instance:
§ Project initially under cancellation threat
§ Project recovery
§ Project became a business case for the client
40
4040

40. CONTRIBUTIONS AND RESEARCH QUESTIONS
Contribution RQ
Related papers (P), Technical
reports (IR), and Book
Chapters (CH)
C1. Empirical verification of the importance
of productivity for companies adopting
agile, and perceived benefits.
RQ1 P5, P8
IR2
CH1
C2. Rationale on productivity definition in
agile methods context.
RQ1 P3, P4, P6, P7
IR1
C3. Empirical verification of agile team
productivity factors.
RQ2 P4, P7
C3.1. Motivators in agile teams RQ2 P9
C4. A framework of agile team productivity
factors and their impact, to be tested.
RQ2 P7
C5. A case on team productivity monitoring
process considering adaptability and
evaluation of agile team productivity
metrics’ usefulness.
RQ3 P10
CH2
41

41. WHYSUCHSTUDIESARE
IMPORTANT
For research community and industry
42

42. 43
“Closing the gap between research and
practice by encouraging a stronger
emphasis on methodological rigor while
focusing on relevance for practice”
Barbara A. Kitchenham, Tore Dyba, and Magne Jorgensen. 2004. Evidence-Based Software Engineering. In Proceedings of the 26th International
Conference on Software Engineering (ICSE '04). IEEE Computer Society, Washington, DC, USA, 273-281.

43. 44
Anna Sandberg, Lars Pareto and Thomas Arts. Agile collaborative research:
Action principles for industry-academia collaboration. IEEE Software, 28(4):74–83, 2011
Better researcher by working on relevant
problems,
better practitioner by identifying and applying
scientific methods

44. PAPERS, INDUSTRY REPORTS, AND BOOK CHAPTERS
P1. MELO, C. O. ; FERREIRA, G. R. M. Adopting Agile in a Large Government Institution – a case study (in Portuguese). In:
Workshop Brasileiro de Métodos Ágeis (WBMA), Conferência Brasileira sobre Métodos Ágeis de Desenvolvimento de
Software (Agile Brazil 2010). Porto Alegre. p. 104-117.
P2. MELO, C. O. ; SANTOS Jr., C. D. ; FERREIRA, G. R. M. ; KON, F. An exploratory study of factors associated with learning
in agile teams on industry (in Portuguese). Proceedings of 7th Experimental Software Engineering Latin American
Workshop, 2010, Goiânia.
P3. MELO, C. O. ; KON, F. Empirical evaluation of agile practices impact on team productivity. In: 12th International
Conference on Agile Software Development (XP), Doctoral Symposium, Madrid, 2011, pp. 322-323.
P4. MELO, C. O. ; CRUZES, D. S. ; KON, F. ; CONRADI, R. Agile Team Perceptions of Productivity Factors. In: Proceedings
of the Agile Development Conference (AGILE), Salt Lake City, USA, 2011, pp. 57-66.
P5. CORBUCCI, H. ; GOLDMAN, A. ; KATAYAMA, E. ; KON, F. ; MELO, C. O. ; SANTOS, V. S. Genesis and Evolution of the
Agile Movement in Brazil – A perspective from the Academia and the Industry. In: Proceedings of 25th Brazilian
Symposium on Software Engineering (SBES), 2011, pp. 98-107.
P6. MELO, C. O. ; KON, F. Productivity of agile teams (in Portuguese). Software Engineering Magazine, Brazil, v. 43, p. 1 -
9, 05 dez. 2011.
P7 MELO, C. O. ; CRUZES, D. S. ; KON, F. ; CONRADI, R. Interpretative Case Studies on Agile Team Productivity and
Management. Information & Software Technology 55(2): 412-427 (2013).
P8 MELO, C. O. ; KATAYAMA, E.; SILVA, V. S.; CORBUCCI, H.; PRIKLADNICKI, R. GOLDMAN, A.;KON, F. Genesis and
Evolution of the Agile Movement in Brazil – A perspective from the Academia and the Industry. Journal of Brazilian
Computer Society 19(4):523-552 (2013).
45

45. PAPERS, INDUSTRY REPORTS, AND BOOK CHAPTERS
(CONT.)
P9 MELO, C. O. ; SANTANA, C.; KON, F. Developers motivation in agile teams. 38th Euromicro Conference on Software
Engineering and Advanced Applications (SEAA), Çesme, Izmir, 2012, p. 376-383.
P10 MELO, C. O.; KON, F. Agile team productivity monitoring: it is all about learning. In preparation for the Information
and Software Technology.
IR1 MELO, C. O. ; KON, F. Productivity Factors in Agile teams - an exploratory study in Brazilian Companies (in
Portuguese). March, 2012.
IR2 MELO, C. O.; SANTOS, V. A.; CORBUCCI, H.; KATAYAMA, E.; GOLDMAN, A.; KON, F. Agile methods in Brazil: state of the
practice in teams and organizations (in Portuguese). Technical Report MAC-2012-03. Department of Computer
Science. IME-USP. May, 2012. Available at: http://agilcoop.org.br/MetodosAgeisBrasil2011.
CH1 GOLDMAN, A; MELO, C. O. ; KON, F.; CORBUCCI, H.; SANTOS, V. The History of Agile Methods in Brazil (in
Portuguese), Chapter 2, In: Métodos Ágeis Para Desenvolvimento De Software. Bookman, 2014.
CH2 MELO, C. O. Productivity and adaptability of agile teams: leveraging the paradox towards innovation (in Portuguese,
to appear), In: Antologia ThoughtWorks Brasil. Casa do Código. 2014.
46
4646

46. RELATED RESEARCH WORK
Conference Papers
OLIVEIRA, R. M. ; MELO, C. O. ; Goldman, A . Designing and Managing Agile Informative Workspaces: Discovering
and Exploring Patterns. In: 2013 46th Hawaii International Conference on System Sciences (HICSS), p.
4790-4799, Wailea.
§ Nominated for the best paper award (http://www.hicss.hawaii.edu/hicss_46/bp46/bestpapersnoms1219.pdf)
TAKEMURA, C. ; MELO, C. O. Studying agile organizational design to sustain innovation. In: Agile Brazil, 2012, São
Paulo. Proceedings of the III Brazilian Workshop on Agile Methods (WBMA 2012), 2012. p. 13-24.
SOUSA, T. C. ; MELO, C. O. . Generating Fit acceptance tests from B Specifications (in portuguese). In: IV Workshop
de Desenvolvimento Rápido de Aplicações do Simpósio Brasileiro de Qualidade de Software (WDRA-SBQS),
2010, Belém. Anais do Workshop de Desenvolvimento Rápido de Aplicações do Simpósio Brasileiro de
Qualidade de Software, 2010. v. 1. p. 1-8.
Book chapters
Bertholdo, Ana Paula O. ; da Silva, Tiago Silva ; de O. Melo, Claudia ; KON, FABIO ; Silveira, Milene Selbach. Agile
Usability Patterns for UCD Early Stages. Lecture Notes in Computer Science, 2014, v. 8517, p. 33-44.
Silva, Tiago Silva ; Silveira, Milene Selbach; O. Melo, Claudia; Parzianello, Luiz Claudio. Understanding the UX
Designer s Role within Agile Teams. Lecture Notes in Computer Science, 2013, v. , p. 599-609.
47

47. CONTRIBUTIONS TO THE COMMUNITY
Graduate course “MAC5779 Engenharia de Software Experimental”,
with Professor Marco Aurélio Gerosa
§ Course proposal
§ Course design and content
§ Course lecturing
Our dataset was used to support a Master Thesis (Eng. Produção,
Poli-USP)
§ Student José Henrique Dell'Osso Cordeiro, Advisor Prof. Afonso Carlos Correa Fleury, Title: “Ambidestria
em empresas desenvolvedoras de software: barreiras para adoção de metodologias ágeis e seu impacto
na escolha do modelo organizacional”. Defended on June/2014.
Invited to be part of the “Supporting Agile Adoption: It's About
Change” group, supported by the Agile Alliance.
■ Only participant from the Global South
■ Published content available http://www.agilealliance.org/programs/supporting-agile-adoption-it-is-about-
change/
48

48. CONTRIBUTIONS TO THE COMMUNITY (2)
Presentations
• MELO, C. O. . Agilidade no Brasil: Fatos e Mitos. Agile Trends 2013. São Paulo
• MELO, C. O. . O segredo é a confiança: criando melhores times, com ou sem distância.
Agile Brazil 2013. Brasília. MELO, C. O. . Introdução a Métodos Ágeis de
Desenvolvimento de Software. Caixa Econômica Federal. 2012.
• KATAYAMA, E. ; GOLDMAN, A. ; MELO, C. O. Uma introdução ao Desenvolvimento de
Software Lean. Invited short course. SBQS 2012.
• MELO, C. O. Lean Lego Game. Invited workshop. SBQS 2012.
• SOUSA, F. ; MELO, C. O. ; COLUCCI, T. ; CUKIER, D. Lean startups - Curso de Verão no
IME-USP. 2012.
• MELO, C. O. ; SANTANA, C. ; GOLDMAN, A. ; KON, F. A Primeira Década com Métodos
Ágeis: desafios atuais e evidências encontradas. CBSOFT 2011.
49

49. Number of possible studies in different areas (Computer
Science, Organizational & Management Science, Social
Science)
§ Testing the agile team productivity factors framework through
confirmatory studies. Replication.
§ Explaining the role of adaptability on team productivity factors.
§ Further exploring metrics driving learning and change.
§ Exploring productivity drivers in agile companies.
Possible partnership with Programa Brasileiro da Qualidade
e Produtividade em Software
§ Just 1-2 Brazilian studies cited.
FUTURE RESEARCH
50

50. 51
5151
ACKNOWLEDGMENTS

51. OBRIGADA
Questions?
claudia@ime.usp.br
@claudia_melo