From Work To Word

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A Talk given at the IEEE Mining Software Repositories conference collocated with ICSE'09
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From Work To Word

  1. 1. From
Work
to
Word:

 How
Do
So/ware
 Developers
Describe

 Their
Work?
 Walid
Maalej,
Technische
Universität
München
 Hans‐Jörg
Happel,
FZI
Research
Center
Karlsruhe

  2. 2. Outline
 1
 MoMvaMon
 2
 Research
SeKng
 3
 Research Results
 4
 Conclusion
and
Next
Steps

 ©
W.
Maalej,
Mai
09
 From
Work
To
Word
 2

  3. 3. WHY
Do
Developers
Describe
Their
Work?
 RaMonale
Capture

 1
 ReflecMon
&
Experience

Capture
 •  Remember
status
when
conGnuing
postponed
work
 •  Log
decisions
and
why
 they
have
been
taken
 •  Capture
experiences
on
problems
and
their
soluGons
 in
this
parGcular
way
 •  Reason
about
previous
 2
 decisions
by
reading
 Purposes
of
Work

 Status
Awareness
 the
work
descripGons
 DescripMon
 •  Help
collaborators

in
 4
 distributed
projects
to
 know
about
the
status
 of
work
 Controlling
 •  Provide

a
short,
human
 •  Report
work
done
in
a
period
of
Gme
 readable
summary
of
 •  Control
effort
and
cost

(e.g.
for
adding
one
feature)
 3
 changes
 ©
W.
Maalej,
Mai
09
 From
Work
To
Word
 3

  4. 4. WHERE
Do
Developers
Describe
Their
Work?
 Personal
note
 Commit
message
 ArMfacts
 Issue
report
comments
 including

 work
 descripMons
 Social
media
 Time
sheet
 But:
How do
developers
describe
work?
 This
is
the
goal
of
our
exploraMve
study
 ©
W.
Maalej,
Mai
09
 From
Work
To
Word
 4
  5. 5. Outline
 1
 MoMvaMon
 2
 Research
SeKng
 3
 Research Results
 4
 Conclusion
and
Next
Steps

 ©
W.
Maalej,
Mai
09
 From
Work
To
Word
 5

  6. 6. Our
Research
QuesMons
On
Work
DescripMon
 Content Analysis Time Metadata   Vocabulary
usage,
   Session
duraMon
and
 similariGes
and
terms
 descripGon
frequency
 frequency

   Logging
day
Mme
and
   Work
categories
and
 relaGon
to
descripGon
 proporGon
of
acGviGes
 quality
   DescripGon
pa_erns
and

   Pseudo
descripGons,
 common
templates
 only
Gme
metadata
   Referenced
arGfacts
   Required
effort
 ©
W.
Maalej,
Mai
09
 From
Work
To
Word
 6
  7. 7. Data
Sets
Collected
in
Different
Contexts
 Data
set
 Summary
 Number
of
 Number
of
 Represented
 developers
 descripMons
 period
 Developers’
Gme
 8
years
 MyComp
 cards
in
a
German
 25
 38,045
 (2001
–
2009)
 soWware
company
 Commit
messages
of
 15
years
 Apache
 1,949
 747,403
 all
Apache
projects
 (1994
–
2009)
 Subjects‘
personal
 notes
in
a
field
study
 10
days
 Eureka
 21
 115
 with
5
European
 (2008)
 companies
 ©
W.
Maalej,
Mai
09
 From
Work
To
Word
 7
  8. 8. Outline
 1
 MoMvaMon
 2
 3
 Research Results
 4
 Conclusion
and
Next
Steps

 ©
W.
Maalej,
Mai
09
 From
Work
To
Word
 8

  9. 9. Vocabulary
Usage
and
Term
Frequency
 Rank Mycomp German Apache Eureka English Term Not 2 17 4 1 29 For/ Since 7 184 23 212 906 Change 3 911 26 214 333 Review 1 >10,000 13 - 2,275 Problem 24 461 10 9 239 Now 12 576 14 58 808 Done 17 4,454 300 59 606 Work
descripMons
extensively
include:
 •  NegaMve
formulaMons
 •  JusMficaMons
and
argumentaMons
 •  Problems
that
caused
the
work
 ©
W.
Maalej,
Mai
09
 From
Work
To
Word
 9
  10. 10. Described
Categories
of
Work
 •  Not
only
acMviMes
“with
work
products”
are
described
 •  Granularity
is
more
“ediMng‐”
rather
than
acMvity‐oriented
 ©
W.
Maalej,
Mai
09
 From
Work
To
Word
 10
  11. 11. Work
DescripMon
Pa_erns
 1.
ArgumentaMon
Pa_erns
 Pa#ern ::= <Ac-on> 'concerning' | 'performed on' <Ar-fact>  'for'|'since'|'because' <Cause descrip-on>|<Reference to cause>  2.
Status
Pa_erns
 Pa#ern ::= <Ar-fact> 'works now'  Pa#ern ::= 'Problem'|'Bug' <Reference to issue>|<Problem descrip-on>  'fixed'|'solved' <Solu-on> ['reviewed with' <Colleague>]  3.
Experience
Sharing
Pa_erns
 Pa#ern ::= 'If' <Context> 'then' <Experience>  ©
W.
Maalej,
Mai
09
 From
Work
To
Word
 11

  12. 12. CreaMon
Time
and
Session
DuraMon
 •  Delayed
descripMons
are
shorter
than
immediate
ones
 •  The
mean
of
session
duraMons
is
between
30
and
90
min.
 •  Developers
entered
between
8
and
12
descripMons
per
work
day
 ©
W.
Maalej,
Mai
09
 From
Work
To
Word
 12
  13. 13. Work
DescripMon
Quality
and
Effort
 10%
of
pseudo
descripMons •  Developers
don’t
have
Mme
or
moMvaMon
to
describe
10%
of
the
sessions

 •  3
–
6%
of
developer
Mme
is
spent
for
describing
work
(30
min.
/
day
)


 ©
W.
Maalej,
Mai
09
 From
Work
To
Word
 13
  14. 14. Outline
 1
 MoMvaMon
 2
 Research
SeKng
 3
 Research Results
 4
 Conclusion
and
Next
Steps

 ©
W.
Maalej,
Mai
09
 From
Work
To
Word
 14

  15. 15. Summary
of
the
Talk
 1
 We
conducted
an
exploratory
study
on
how
developers
describe 
their
work
using
real
world
data
 2
 A
considerable
amount
of
effort
is
spent
to
describe
work,
with 
oWen
empty
or
pseudo‐descripGons:
automaGon
would
pay
off
 3
 We
found
similariGes
in
descripGon
contents:
part
of
the 
descripGon
can
be
automated
by
observing
interacMons
 4
 We
found
similariGes
in
the
Mme
metadata:
the
work
day
can
be 
sessionized
automaGcally
 ©
W.
Maalej,
Mai
09
 PotenMals
and
Challenges
of
RS
in
SD
 15

  16. 16. Open
Source
Plaqorm:
TeamWeaver

 www.teamweaver.org
 ©
W.
Maalej,
Mai
09
 PotenMals
and
Challenges
of
RS
in
SD
 16

  17. 17. ©
W.
Maalej,
Mai
09
 From
Work
To
Word
 17
  18. 18. You
are
welcome
to
join!

 Contact:
 Walid
Maalej

 Hans‐Jörg
Happel

 TUM
 FZI
 maalejw@cs.tum.edu
 happel@fzi.de

 ©
W.
Maalej,
Mai
09
 From
Work
To
Word
 18

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