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PRODUCTIVITY OF AGILE TEAMS: AN EMPIRICAL EVALUATION OF FACTORS AND MONITORING PROCESSES

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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.

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  • DOWNLOAD FULL BOOKS, INTO AVAILABLE FORMAT ......................................................................................................................... ......................................................................................................................... 1.DOWNLOAD FULL. PDF EBOOK here { https://tinyurl.com/y6a5rkg5 } ......................................................................................................................... 1.DOWNLOAD FULL. EPUB Ebook here { https://tinyurl.com/y6a5rkg5 } ......................................................................................................................... 1.DOWNLOAD FULL. doc Ebook here { https://tinyurl.com/y6a5rkg5 } ......................................................................................................................... 1.DOWNLOAD FULL. PDF EBOOK here { https://tinyurl.com/y6a5rkg5 } ......................................................................................................................... 1.DOWNLOAD FULL. EPUB Ebook here { https://tinyurl.com/y6a5rkg5 } ......................................................................................................................... 1.DOWNLOAD FULL. doc Ebook here { https://tinyurl.com/y6a5rkg5 } ......................................................................................................................... ......................................................................................................................... ......................................................................................................................... .............. Browse by Genre Available eBooks ......................................................................................................................... Art, Biography, Business, Chick Lit, Children's, Christian, Classics, Comics, Contemporary, Cookbooks, Crime, Ebooks, Fantasy, Fiction, Graphic Novels, Historical Fiction, History, Horror, Humor And Comedy, Manga, Memoir, Music, Mystery, Non Fiction, Paranormal, Philosophy, Poetry, Psychology, Religion, Romance, Science, Science Fiction, Self Help, Suspense, Spirituality, Sports, Thriller, Travel, Young Adult,
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  • Very nice presentation! I have one question: How do you monitor the goals in slide 36?
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  • Really nice. Useful information and tools. Thanks so much Claudia.
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PRODUCTIVITY OF AGILE TEAMS: AN EMPIRICAL EVALUATION OF FACTORS AND MONITORING PROCESSES

  1. 1. PRODUCTIVITYOFAGILETEAMS: ANEMPIRICALEVALUATIONOF FACTORSANDMONITORINGPROCESSES CSBC 2014 – XXVII CTD Dr. Claudia Melo Advisor: Prof. Dr. Fabio Kon Department of Computer Science, IME-USP
  2. 2. OUTLINE Context & Definitions Problem statement Research questions Research methods & Overview of all studies Results & Implications for research and industry Other contributions 3
  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. 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. 5. 6 SOFTWARE PRODUCTIVITY IS A GENERAL STRATEGIC CONCERN IN SEVERAL INDUSTRIAL SECTORS
  6. 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. 7. “THEMOSTVALUABLEASSETOFA21STCENTURY INSTITUTIONWILLBEITSKNOWLEDGEWORKERS ANDTHEIRPRODUCTIVITY” PETERDRUCKER,1999 8
  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. 9. Agile Manifesto: values and principles Scrum, XP, Lean software development, Feature Driven Development, DSDM, Crystal etc. 10
  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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 20. 11 FINDINGS 21
  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. 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. 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. 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. 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. 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. 27. 28
  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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 41. WHYSUCHSTUDIESARE IMPORTANT For research community and industry 42
  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. 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. 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. 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. 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. 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. 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. 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. 50. 51 5151 ACKNOWLEDGMENTS
  51. 51. OBRIGADA Questions? claudia@ime.usp.br @claudia_melo

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