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Graziela S. Tonin¹, Rogério Chaves², Alfredo
                               Goldman¹, Viviane Santos¹.
Instituto de Matemática Estatística e Ciência da Computação – Universidade de São Paulo (USP).
               ² Instituto de Pesquisas Tecnológicas do Estado de São Paulo (IPT)
Agenda
 Motivation
 Research Objectives
 Methodology
 Research Contextualization
 Studied Cases
 Results
 Threats to validity
Motivations
Motivations
 Techinical Debt
                   “Shipping first time code is like going into a debt. A little debt
  speeds development so long as it is paid back promptly with a rewrite. Objects
 make the cost of this transaction tolerable. The dangers occurs when the debt is
    not repaid. Every minute spent on not-quite-right counts as interest on that
                                                                              debt...”
                                                              Cunningham (1992)
 Gartner (2012)
    Technical Debt cost in 2010 was approximately $ 500 billion
    2015 : The cost will reach U$1 trillion
Motivations
 Increased the adoption of Agile Methods in the last 10 years.
 (VersionOne 2012 e Williams 2010).


    Effectiveness and benefits have been significant:
      More productive teams .
      Less Stress.
      Customers more satisfied with the products delivered.
Research Objectives
Research Objectives
 (1) The Technical Debt concept is known in the company?
     If yes, how is considered in project management?


 (2) Strategic Decisions generated Technical Debt?

 (3) What was the impact over time?
Methodology
Methodology
 Exploratory case study
   Selected based on purposive sampling method.
   [Yin, 2010]

 Data collected through interviews.
   Open and close questions.
   Interviews of the 30 minutes.
   Five people interviewed.
   Focus on historical project.
Context Research
Context Research
 Project conducted in a technology company with more
 than 350 developers and 45 Scrum teams.
 Project: Monitoring and automation system,
 developed in java with more than 100,000 lines of
 code.
 Four cases studied.
Studied Cases
Studied Cases
 Case 1
   Upgrade of the JQuery version.
     Fix system bugs.
     New features.
 Case 2
   Persistence method of monitoring events in the
   database.
     Scalability limitation.
       Thousands devices monitored.
     Monitored only status changes.
     Metrics collected in the monitoring agents not persisted.
Studied Cases
 Case 3
   Use of MON (https://mon.wiki.kernel.org/) as
   monitoring agent.
     Monitor service availability.
 Case 4
   CMDB (Configuration Management Database) as
   mandatory data insertion in the system.
     System for configuration management.
Classification
 • According Cunnignham properties(1996)
Characterization and
conceptualization – Case 1
Characterization and
conceptualization – Case 2
Characterization and
conceptualization – Case 3
Characterization and
conceptualization – Case 4
Results
Resulting Model: characterization
and e conceptualization of cases
Results
(1) The Technical Debt concept is known in the
company? If yes, how is considered in project
management?

    The company had knowledge.
     But not considered in project management.
Results
(2) Strategic Decisions generated Technical Debt?
(3) What was the impact over time?
Analysis of impacts in the Cases
  Case 2, Case 3 and Case 4
    Process Adjustment.
    Business adapting to the system.


  All cases
    Rewrite code.
    Team division:
      Maintain an old system.
      Develop new solution.
    High level of stress – customer and project team
    Customer dissatisfied.
Impacts
                           Delay in Delivery/Day

    120


    100


    80


    60


    40


    20


     0
          Upgrade JQuery       Persistence   MON   CMDB
Contributions
Contributions
 Confirms the importance of not ignoring Technical
 Debt.
 Highlights the importance of communication between
 technical and business team.
 General Model that may help to show:
   Where Techincal Debt arise;
   What are your influences and motivations;
   Can be considered at the moment of decision-making.
   Can be replicated in other projects.
Threats to validity
Threats to validity
 Data collected only through interviews.
   Data triangulation.
 Don’t have a detailed calculation of impact / cost.
   No data access as:
     Man/hour value.
     Cost of maintaining the old system while the new version was
     made.
Questions?

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Agile Brazil -Wbma 2014

  • 1. Graziela S. Tonin¹, Rogério Chaves², Alfredo Goldman¹, Viviane Santos¹. Instituto de Matemática Estatística e Ciência da Computação – Universidade de São Paulo (USP). ² Instituto de Pesquisas Tecnológicas do Estado de São Paulo (IPT)
  • 2. Agenda Motivation Research Objectives Methodology Research Contextualization Studied Cases Results Threats to validity
  • 4. Motivations Techinical Debt “Shipping first time code is like going into a debt. A little debt speeds development so long as it is paid back promptly with a rewrite. Objects make the cost of this transaction tolerable. The dangers occurs when the debt is not repaid. Every minute spent on not-quite-right counts as interest on that debt...” Cunningham (1992) Gartner (2012) Technical Debt cost in 2010 was approximately $ 500 billion 2015 : The cost will reach U$1 trillion
  • 5. Motivations Increased the adoption of Agile Methods in the last 10 years. (VersionOne 2012 e Williams 2010). Effectiveness and benefits have been significant: More productive teams . Less Stress. Customers more satisfied with the products delivered.
  • 7. Research Objectives (1) The Technical Debt concept is known in the company? If yes, how is considered in project management? (2) Strategic Decisions generated Technical Debt? (3) What was the impact over time?
  • 9. Methodology Exploratory case study Selected based on purposive sampling method. [Yin, 2010] Data collected through interviews. Open and close questions. Interviews of the 30 minutes. Five people interviewed. Focus on historical project.
  • 11. Context Research Project conducted in a technology company with more than 350 developers and 45 Scrum teams. Project: Monitoring and automation system, developed in java with more than 100,000 lines of code. Four cases studied.
  • 13. Studied Cases Case 1 Upgrade of the JQuery version. Fix system bugs. New features. Case 2 Persistence method of monitoring events in the database. Scalability limitation. Thousands devices monitored. Monitored only status changes. Metrics collected in the monitoring agents not persisted.
  • 14. Studied Cases Case 3 Use of MON (https://mon.wiki.kernel.org/) as monitoring agent. Monitor service availability. Case 4 CMDB (Configuration Management Database) as mandatory data insertion in the system. System for configuration management.
  • 15. Classification • According Cunnignham properties(1996)
  • 21. Resulting Model: characterization and e conceptualization of cases
  • 22. Results (1) The Technical Debt concept is known in the company? If yes, how is considered in project management? The company had knowledge. But not considered in project management.
  • 23. Results (2) Strategic Decisions generated Technical Debt? (3) What was the impact over time?
  • 24. Analysis of impacts in the Cases Case 2, Case 3 and Case 4 Process Adjustment. Business adapting to the system. All cases Rewrite code. Team division: Maintain an old system. Develop new solution. High level of stress – customer and project team Customer dissatisfied.
  • 25. Impacts Delay in Delivery/Day 120 100 80 60 40 20 0 Upgrade JQuery Persistence MON CMDB
  • 27. Contributions Confirms the importance of not ignoring Technical Debt. Highlights the importance of communication between technical and business team. General Model that may help to show: Where Techincal Debt arise; What are your influences and motivations; Can be considered at the moment of decision-making. Can be replicated in other projects.
  • 29. Threats to validity Data collected only through interviews. Data triangulation. Don’t have a detailed calculation of impact / cost. No data access as: Man/hour value. Cost of maintaining the old system while the new version was made.