Mood-Based Micro-Blogging in Distributed Software Engineering

Mood-Based Micro-Blogging in Distributed Software Engineering

Talk presented at MSR 2013, the 10th Working Conference on Mining Software Repositories.

Paper: http://aspic.nl/publications/DullemondGamerenStoreyDeursenMSR2013.pdf

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Arie van Deursen

May 19, 2013
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Transcript

  1. 1.

    Mood-­‐Based  Micro-­‐Blogging   Kevin  Dullemond,    Ben  van  Gameren,  

      Margaret-­‐Anne  Storey,  Arie  van  Deursen   Mining  So;ware  Repositories  (MSR),  San  Francisco,  May  2013   1  
  2. 2.

    Micro-­‐Blogs   •  “Short  messages  people  use  to  provide  updates

      on  their  ac5vi5es,  observa5ons  and  interes5ng   content,  directly  or  indirectly  to  others”   •  Use  in  so;ware  engineering:   – Support  awareness?   – Support  distributed  working?   Ehrlich  et  al:   “Microblogging  inside   and  outside  the   workplace”,  ICWSM,  2010   2  
  3. 3.

    IHomer  Creates  WeHomer   WeHomer  @  iHomer:   Miro-­‐Blogging  with

     Moods     >L      L      K      J      >J   3  
  4. 5.

    5   I’m  going  to  be  a  daddy!    :-­‐D

      Will  be  partly  available  today;  Son  is  sick  at  home  >L   Working  on  our  Linux  servers  today  –     some  services  may  experience  outages  J   On  my  way  to  customer  X  for  first  live  test  :-­‐D  
  5. 7.

    Approach   •  Try  to  understand:   – Role  of  micro-­‐blogging

     in   distributed  work  @  home   – Role  of  “mood”   •  Qualitadve  analysis   – Coding  2500  posts     from  20  users   – Interviews  with  5  users   Content Information about a person Health Sentiment Personal Experience Information about technology Technical Knowledge Information about task articulation work Work Planning Work Assignment Supplies Non-Technical Infrastructure Technical Infrastructure Intern Technical Infrastructure Extern Information about customer relations Relation Project Commissioning Information about entrepreneurial tasks Prospects Company Meeting Applicant Invoicing 7  
  6. 8.

    “Nature”:     Most  Posts  Are  Posidve   8  

    Manual  sendment  analysis.   Largely  in  line  with  indicated  “happiness”  
  7. 9.

    “Form”:     9%  of  Posts  is  a  Quesdon  

    9   Ehrlich  et  al  BlueTwit  analysis:  13%  quesdons    
  8. 10.

    “Intendon”:     Personal,  Work,  Coordinadon   10   “Task

     ardculadon”  work   Home  /  work  balance  
  9. 11.

    “Content”:     Intra-­‐  And  Inter-­‐Team  Topics   •  Intra-­‐team

     (4  teams,  2-­‐7  members)   – Directed  messages:  more  within  team   – Planning  and  coordinadon   •  Full  organizadon  (20  people):   – New  employees,    new  prospects,  new  business   direcdons,  social  events   – Planning  and  coordinadon   •  “Glue”  between  people  working  at  home.   11  
  10. 12.

    The  Tuesday  Effect   12   “There  is  no  halfsies

     in  a  distributed  team  …   If  even  one  person  of  the  team  is  remote,  very  single  person  has  to   start  communica5ng  on  line.”  –  Fullerton  /  StackExchange  
  11. 13.

    Discussion   •  Social  interacdons  affect  so;w.  development   – Mining

     research  must  be  aware  of  them!   – To  what  extent  should  we  mine  them?   •  Mood  indicators?   – Issues,  feature  requests,  commits,  pull  requests?   •  Sendment  analysis?  –  Anger  versus  Joy   13  
  12. 14.

    Conclusions   •  Micro-­‐blogging  with  mood  indicators:   – Share  knowledge

     and  emo5ons  like     you  are  co-­‐located   – Create  and  sustain  a  feeling  of  connectedness   (conform  Zhao  et  al,  ICWSM  2009)   – Beyond  individual  projects   •  All  or  nothing  proposidon  (Tuesday  effect)   14