Recommendation Systems for Software Engineering

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Introduction and wrap-up of the International Workshop on Recommendation Systems for Software Engineering (RSSE 2008) organized by Martin Robillard, Rob Walker, and Thomas Zimmermann

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Recommendation Systems for Software Engineering

  1. 1. Welcome
to
 RSSE
2008
 Interna'onal
Workshop
on
 Recommenda'on
Systems
 for
So7ware
Engineering
 Mar2n
Robillard



Rob
Walker



Tom
Zimmermann

  2. 2. Recommenda2on
Systems...
 •  Emerged
in
the
early
1990s
 •  To
help
users
“make
choice
without
sufficient
 […]
experience”
[Resnick
and
Varian
1997]
 •  For:
 –  Web
resources
(PHOAKS,
Fab,
Siteseer)
 –  Books,
movies,
music
(e.g.,
Amazon)
 –  Hotels
(e.g.,
TripAdvisor)
 •  A.k.a
“Collabora2ve
Filtering”

  3. 3. …for
Soware
Engineering
 •  A
21st‐century
phenomenon?
 •  2002
 –  CodeBroker
[Ye
and
Fisher]
 –  Exper2se
Browser
[Mockus
and
Herbsleb]
 •  What’s
happening?
 –  Masses
of
available
data

 –  Increasingly
rich
programming
domains

  4. 4. Long‐Standing
Ques2ons
 •  What
is
a
recommenda2on
system?
 •  How
do
you
“declare
your
interest”
in
specific
 recommenda2ons
 •  What
is
the
“cost
structure”
of
choice?
 •  What
is
the
incen2ve
structure
for
 contribu2ng
data?

  5. 5. Emerging
Ques2ons
 •  What
is
special
about
recommenda2ons
 systems
for
soware
engineering?
 •  What
are
the
different
ways
to
integrate
 recommenda2ons
into
developer
workflows?
 •  How
can
we
evaluate
recommenda2ons?

  6. 6. Introduc2ons
 Name
+
Affilia2on

  7. 7. Goals
and
Desired
Outcomes
 •  New
themes
that
have
emerged
 •  Stable
results
we
are
ready
to
accept
as
part
 of
the
recommenda2on
system
corpus
 •  Shortcomings
of
current
state
of
the
art
 •  Future
direc2ons

  8. 8. Format
 •  Talks
+
Ques2ons
+
Discussions
 •  Special
Poster
Session
with
Blitz
Talks
 •  5‐minute
Madness
 •  And
another
thing…
 –  Sign
up
on
the
signup
sheet
 –  Consider
pos2ng
your
slides
 –  Follow
up
might
include
a
special
issue

  9. 9. 5‐minute
madness
 •  Gail:


 –  “The
recommenda2on
conundrum”:
what
do
people
need
 help
with,
how
to
deal
with
compe2ng
recommenders
 –  recommenda2ons
work
well
when
task‐first
rather
than
 tool‐first
 •  Adrian:
 –  Interac2on
effects
with
other
recommenders,
either
 synchronously
or
sequen2al
 •  Rob:
 –  announcement
of
a
data
mining
summer
school
 (rob.deline@microso.com)

  10. 10. WRAP‐UP

  11. 11. Current
shortcomings
 •  Missing
data
impedes
analyses
(e.g.,
method
 bindings)
 •  Too
many
(false‐posi2ve)
recommenda2ons
 •  Ra2onales
are
oen
absent
 –  Apparent
tradeoff:
usefulness/ease
of
provision
 •  User‐specific
needs
and
staleness
of
those
needs
 •  Lack
of
feedback
capture

 •  Screen
real
estate
needed

  12. 12. Themes
 •  Thoughts
on
what
recommenda2on
systems
 generally
involve
 •  Features
of
recommenda2on
systems
 –  recommenda2on
filtering
 –  ranking/ordering
 –  ra2onale
 –  means
of
presen2ng
the
results
 •  Cost
model

 •  Level
of
granularity

  13. 13. Themes
 •  Modes
of
use
(batch
vs.
in‐context)
 •  Tasks
being
supported
 •  Degree
of
collabora2on
supported/required
 –  Privacy
issues
 •  Evalua2on
 –  Evalua2on
should
not
s2fle
innova2on
 –  “Evalua2on
drives
innova2on”

  14. 14. Stable
results
 •  Recommenda2ons
can
involve
people,
 ar2facts,
or
both
 •  Dimensions
of
recommenda2on
systems
 –  popula2on
set
(what
might
be
recommended)
 –  possibly,
addi2onal
data
(analyzed
to
derive
 results)
 –  result
set
 –  ???
 •  ???

  15. 15. Future
direc2ons
 •  Guidelines
for
what
cons2tutes
good
eval.
(rela.
to
maturity)
 –  user
studies
vs.
simula2ve
studies
 •  strengths/weaknesses
 –  benchmarks
to
avoid
user
studies,
improve
comparability
 •  via
Wizard
of
Oz
studies
 •  real
user
studies
are
necessary
eventually,
though
 •  What
cons2tutes
a
good
ra2onale
 –  Levels
of
abstrac2on
approach
suggested
 •  Understanding
usage
modes:
 –  When
is
it
(not)
appropriate
to
push
informa2on,
and
how
 prominently?
 –  Should
systems
only
provide
batch‐mode
support
or
in‐context‐mode
 support,
or
always
both
 •  “Ungameable”
recommenda2on
systems?
 •  “Unified
theory
of
RSSE”

  16. 16. Stay
tuned
…
 •  Workshop
summary
to
go
to
Soware
 Engineering
Notes
 •  Future
RSSE
workshops
 •  Journal
special
issue
on
RSSE


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