PRODUCTIVITY OF AGILE TEAMS: AN EMPIRICAL EVALUATION OF FACTORS AND MONITORING PROCESSES CSBC 2014 – XXVII CTD Dr. Claudia Melo Advisor: Prof. Dr. Fabio Kon Department of Computer Science, IME-USP
Sep 08, 2014
PRODUCTIVITY OF AGILE TEAMS: AN EMPIRICAL EVALUATION OF
FACTORS AND MONITORING PROCESSES CSBC 2014 – XXVII CTD
Dr. Claudia Melo
Advisor: Prof. Dr. Fabio Kon
Department of Computer Science, IME-USP
OUTLINE
Context & Definitions
Problem statement
Research questions
Research methods & Overview of all studies
Results & Implications for research and industry
Other contributions
3
GRAND CHALLENGE – AN INSTANCE
UK government case Savage, M.: Labour’s Computer Blunders cost 26bn. The Independent, Tuesday 19 January, London (2010)
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
6
SOFTWARE PRODUCTIVITY IS A GENERAL STRATEGIC CONCERN IN SEVERAL
INDUSTRIAL SECTORS
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
“THE MOST VALUABLE ASSET OF A 21ST CENTURY INSTITUTION WILL BE ITS KNOWLEDGE WORKERS AND THEIR PRODUCTIVITY” PETER DRUCKER, 1999
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
Agile Manifesto: values and principles Scrum, XP, Lean software development, Feature Driven Development, DSDM,
Crystal etc. 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
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
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.
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
OVERVIEW OF ALL STUDIES
Multiple-case studies on Agile team productivity definitions and
agile team productivity factors2010-2011
Surveyon Agile productivity
expectationsand benefits
2011-2012
Action research forexploring and assessing
productivity monitoring and measurement in agile teams
2012-2013
Warm-upstudies on Agile
methods impact on productivity
2009-2010
Study of software productivity
definitions, factors and metrics2009-2010
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
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
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
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)
18 1 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
19 19 19
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
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
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11 FINDINGS 21
22
KEY FINDING 1: PRODUCTIVITY AS AN IMPORTANT REASON FOR ADOPTING AGILE METHODS
count
Ch
am
pio
n
Developer
Team leader
Project manager
Development manager
CIO/CTO
President/CEO
Development director
26.3%
23.6%
13.8%
11.5%
10.4%
8.3%
6.2%
0 50 100 150
count
Wo
rrie
s
Lack of documentationLack of predictability
Lack of upfront planningLoss of management control
Lack of team trainingDevelopment team opposed to change
Lack of engineering disciplineRegulatory compliance
Reduced software qualityNo concerns
Inability to scale
51%43.5%
41%37.6%
34.8%32.1%
25.7%24.8%
21.2%12.5%11.9%
0 50 100 150 200 250
Percentage
Re
aso
ns
for
ad
op
ting
Ag
ile
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.
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).
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
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
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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.
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).
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.
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.
I1. Group characteristicsTeam design (e.g., team size, collocation, team, diversity)Team member turnoverBeliefs
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
Fig. 1: IPO traditional team framework adapted from Cohen & Bailey [6] and Marks et al. [7]
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
Team
mem
bers
turn
over
Agile team productivity
Sharing of expertiseIntra team coordination
Conflict managementIntra team coordinationCommunication
Group characteristics Team design
Attitudinal outcomes Commitment
Team
des
ign
choi
ces
Small teams
Diversity (mixed teams)
Full-time allocation
CollocationCommunicationCohesionPlanning/RE negotiation(work procedures)
Intra team coordination
Agile team productivity
Nature of task Team autonomy/ interdependency
Attitudinal outcomes (lack of) CommitmentIn
ter t
eam
coo
rdin
atio
n
Inter team coordination
Agile practices establisment(work procedures)
Inte
r tea
m m
anag
emen
tIn
tra
team
man
agem
ent
MEL
O, C
. O. ;
CRU
ZES,
D. S
. ; K
ON
, F. ;
CO
NRA
DI,
R. In
terp
reta
tive
Case
Stu
dies
on
Agile
Tea
m
Prod
uctiv
ity a
nd M
anag
emen
t. In
form
atio
n &
Softw
are
Tech
nolo
gy 5
5(2)
: 412
-427
(201
3).
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
Pla
nnin
g
8. Assess metrics
usefulness:
- Focus groups - Interviews - Self-assessment questionnaire
9. Elicit research
results
releases
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
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
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
37
KEY FINDING 9: PRODUCTIVITY MONITORING INSTRUMENT
37 37
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
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
DiagnosingAction
PlanningAction 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
MEL
O, C
. O. P
rodu
ctiv
ity a
nd a
dapt
abili
ty o
f agi
le te
ams:
leve
ragi
ng th
e pa
rado
x to
war
ds
inno
vatio
n (in
Por
tugu
ese,
to a
ppea
r), In
: Ant
olog
ia T
houg
htW
orks
Bra
sil. C
asa
do C
ódig
o. 2
014.
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 39 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 40 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
WHY SUCH STUDIES ARE IMPORTANT For research community and industry
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.
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
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
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 46 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
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
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
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
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51 51 51
ACKNOWLEDGMENTS
OBRIGADA Questions?
[email protected] @claudia_melo