Machine learning-based test smell detection
Empirical Software Engineering,
Journal Year:
2024,
Volume and Issue:
29(2)
Published: March 1, 2024
Abstract
Test
smells
are
symptoms
of
sub-optimal
design
choices
adopted
when
developing
test
cases.
Previous
studies
have
proved
their
harmfulness
for
code
maintainability
and
effectiveness.
Therefore,
researchers
been
proposing
automated,
heuristic-based
techniques
to
detect
them.
However,
the
performance
these
detectors
is
still
limited
dependent
on
tunable
thresholds.
We
experiment
with
a
novel
smell
detection
approach
based
machine
learning
four
smells.
First,
we
develop
largest
dataset
manually-validated
enable
experimentation.
Afterward,
train
six
learners
assess
capabilities
in
within-
cross-project
scenarios.
Finally,
compare
ML-based
state-of-the-art
techniques.
The
key
findings
study
report
negative
result.
learning-based
detector
significantly
better
than
techniques,
but
none
able
overcome
an
average
F-Measure
51%.
further
elaborate
discuss
reasons
behind
this
result
through
qualitative
investigation
into
current
issues
challenges
that
prevent
appropriate
smells,
which
allowed
us
catalog
next
steps
research
community
may
pursue
improve
Language: Английский
Assertions in software testing: survey, landscape, and trends
International Journal on Software Tools for Technology Transfer,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 22, 2025
Language: Английский
Test smell: A parasitic energy consumer in software testing
Information and Software Technology,
Journal Year:
2025,
Volume and Issue:
unknown, P. 107671 - 107671
Published: Feb. 1, 2025
Language: Английский
A proposal and assessment of an improved heuristic for the Eager Test smell detection
Journal of Systems and Software,
Journal Year:
2025,
Volume and Issue:
unknown, P. 112438 - 112438
Published: March 1, 2025
Language: Английский
An empirical investigation into the capabilities of anomaly detection approaches for test smell detection
Journal of Systems and Software,
Journal Year:
2024,
Volume and Issue:
unknown, P. 112320 - 112320
Published: Dec. 1, 2024
Language: Английский
A Multimethod Study of Test Smells: Cataloging Removal and New Types
Published: Nov. 5, 2024
Language: Английский
Do you see any problem? On the Developers Perceptions in Test Smells Detection
Published: Nov. 7, 2023
Developers
are
continuously
implementing
changes
to
meet
demands
coming
from
users.
In
the
context
of
test-driven
development,
before
any
new
code
is
added,
a
test
case
should
be
written
make
sure
do
not
introduce
bugs.
During
this
process,
developers
and
testers
might
adopt
bad
design
choices,
which
may
lead
introduction
so-called
Test
Smells
in
code.
solutions
for
or
designing
We
perform
broader
study
investigate
participants'
perceptions
about
presence
Smells.
analyze
whether
certain
factors
related
participant'
profiles
concerning
background
experience
influence
their
perception
Also,
we
if
heuristics
adopted
by
existence
commits
open
source
projects
identify
Then,
conduct
an
empirical
with
25
participants
that
evaluate
instances
10
different
smell
types.
For
each
Smell
type,
agreement
among
participants,
assess
on
evaluations.
Altogether,
more
than
1250
evaluations
were
made.
The
results
indicate
present
low
detecting
all
types
analyzed
our
study.
also
suggest
have
consistent
effect
participants.
On
other
hand,
consistently
influenced
specific
employed
Our
findings
reveal
detect
significantly
ways.
As
consequence,
these
some
questions
previous
studies
consider
Language: Английский