Mining mobile apps reviews to support release planning

Resumen

The
mobile
apps
market
is
a
tremendous
success,
with
millions
of
apps
downloaded
and
used
every
day
by
users
spread
all
around
the
world.
For
apps’
developers,
having
their
apps
published
on
one
of
the
major
app
stores
(e.g.
Google
Play
market)
is
just
the
beginning
of
the
apps
lifecycle.
Indeed,
in
order
to
successfully
compete
with
the
other
apps
in
the
market,
an
app
has
to
be
updated
frequently
by
adding
new
attractive
features
and
by
fixing
existing
bugs.
Clearly,
any
developer
interested
in
increasing
the
success
of
her
app
should
try
to
implement
features
desired
by
the
app’s
users
and
to
fix
bugs
affecting
the
user
experience
of
many
of
them.
A
precious
source
of
information
to
decide
how
to
collect
users’
opinions
and
wishes
is
represented
by
the
reviews
left
by
users
on
the
store
from
which
they
downloaded
the
app.
However,
to
exploit
such
information
the
app’s
developer
should
manually
read
each
user
review
and
verify
if
it
contains
useful
information
(e.g.
suggestions
for
new
features).
This
is
something
not
doable
if
the
app
receives
hundreds
of
reviews
per
day,
as
happens
for
the
very
popular
apps
on
the
market.
In
this
work,
our
aim
is
to
provide
support
to
mobile
apps
developers
by
proposing
a
novel
approach
exploiting
data
mining,
natural
language
processing,
machine
learning,
and
clustering
techniques
in
order
to
classify
the
user
reviews
on
the
basis
of
the
information
they
contain
(e.g.
useless,
suggestion
for
new
features,
bugs
reporting).
Such
an
approach
has
been
empirically
evaluated
and
made
available
in
a
web-­‐based
tool
publicly
available
to
all
apps’
developers.
The
achieved
results
showed
that
the
developed
tool:
(i)
is
able
to
correctly
categorise
user
reviews
on
the
basis
of
their
content
(e.g.
isolating
those
reporting
bugs)
with
78%
of
accuracy,
(ii)
produces
clusters
of
reviews
(e.g.
groups
together
reviews
indicating
exactly
the
same
bug
to
be
fixed)
that
are
meaningful
from
a
developer’s
point-­‐of-­‐view,
and
(iii)
is
considered
useful
by
a
software
company
working
in
the
mobile
apps’
development
market.