Using
different
locking
mechanisms
affects
parallel
programs'
performance
differently,
and
its
impact
on
program
is
difficult
to
assess,
which
hinders
researchers
from
rationally
utilizing
mechanisms.
Moreover,
there
are
few
studies
the
prediction
of
To
address
this
issue,
paper
proposes
a
combination
deep
feedforward
neural
network
(FNN)
Random
Forest
(RF)
method
LockPerf
predict
programs
with
The
predicted
in
execution
time
program.
In
paper,
extracting
static
characteristics
first,
then
sets
variables
such
as
number
threads,
lock
type,
read/write
ratio
by
switch
statement,
finally
runs
collect
multiple
samples
construct
configurable
data
set.
A
total
9
projects
employed
evaluate
effectiveness
Experimental
results
show
that
average
mean
relative
errors
5.47,
standard
95%
confidence
intervals
0.13.
experiments
effectively
predicts
Scientific Reports,
Journal Year:
2023,
Volume and Issue:
13(1)
Published: Sept. 27, 2023
Abstract
Detecting
code
smells
may
be
highly
helpful
for
reducing
maintenance
costs
and
raising
source
quality.
Code
facilitate
developers
or
researchers
to
understand
several
types
of
design
flaws.
with
high
severity
can
cause
significant
problems
the
software
challenges
system's
maintainability.
It
is
quite
essential
assess
detected
in
software,
as
it
prioritizes
refactoring
efforts.
The
class
imbalance
problem
also
further
enhances
difficulties
smell
detection.
In
this
study,
four
datasets
(Data
class,
God
Feature
envy,
Long
method)
are
selected
detect
severity.
work,
an
effort
made
address
issue
imbalance,
which,
Synthetic
Minority
Oversampling
Technique
(SMOTE)
balancing
technique
applied.
Each
dataset's
relevant
features
chosen
using
a
feature
selection
based
on
principal
component
analysis.
determined
five
machine
learning
techniques:
K-nearest
neighbor,
Random
forest,
Decision
tree,
Multi-layer
Perceptron,
Logistic
Regression.
This
study
obtained
0.99
accuracy
score
forest
tree
approach
method
smell.
model's
performance
compared
its
three
other
measurements
(Precision,
Recall,
F-measure)
estimate
classification
models.
impact
presented
without
applying
SMOTE.
results
promising
beneficial
paving
way
studies
area.
Software Practice and Experience,
Journal Year:
2023,
Volume and Issue:
53(10), P. 1902 - 1927
Published: June 26, 2023
Summary
Machine
learning‐based
code
smell
detection
(CSD)
has
been
demonstrated
to
be
a
valuable
approach
for
improving
software
quality
and
enabling
developers
identify
problematic
patterns
in
code.
However,
previous
researches
have
shown
that
the
datasets
commonly
used
train
these
models
are
heavily
imbalanced.
While
some
recent
studies
explored
use
of
imbalanced
learning
techniques
CSD,
they
only
evaluated
limited
number
thus
their
conclusions
about
most
effective
methods
may
biased
inconclusive.
To
thoroughly
evaluate
effect
machine
we
examine
31
with
seven
classifiers
build
CSD
on
four
data
sets.
We
employ
evaluation
metrics
assess
performance
Wilcoxon
signed‐rank
test
Cliff's
.
The
results
show
(1)
Not
all
significantly
improve
performance,
but
deep
forest
outperforms
other
(2)
SMOTE
(Synthetic
Minority
Over‐sampling
TEchnique)
is
not
technique
resampling
(3)
best‐performing
top‐3
little
time
cost
detection.
Therefore,
provide
practical
guidelines.
First,
researchers
practitioners
should
select
appropriate
(e.g.,
forest)
ameliorate
class
imbalance
problem.
In
contrast,
blind
application
could
harmful.
Then,
better
than
selected
preprocess
Artificial
intelligence
(AI)
has
witnessed
an
exponential
increase
in
its
use
various
applications.
Recently,
the
academic
community
started
to
research
and
inject
new
AI-based
approaches
provide
solutions
traditional
software
engineering
problems.
However,
a
comprehensive
holistic
understanding
of
current
status
is
missing.
To
close
above
gap,
synthetic
knowledge
synthesis
was
used
induce
landscape
contemporary
literature
on
AI
engineering.
The
resulted
15
categories
five
themes,
namely
natural
language
processing
engineering,
artificial
management
development
life
cycle,
machine
learning
fault/defect
prediction
effort
estimation,
employment
deep
intelligent
code
management,
mining
repositories
improve
quality.
most
productive
country
China
(n=2042),
followed
by
United
States
(n=1193),
India
(n=934),
Germany
(n=445),
Canada
(n=381).
A
high
percentage
(n=47.4%)
papers
were
funded,
showing
strong
interest
this
topic.
convergence
can
significantly
reduce
needed
resources,
quality,
user
experience,
well-being
developers.
Journal of Systems and Software,
Journal Year:
2023,
Volume and Issue:
209, P. 111934 - 111934
Published: Dec. 19, 2023
The
advancements
in
machine
learning
techniques
have
encouraged
researchers
to
apply
these
a
myriad
of
software
engineering
tasks
that
use
source
code
analysis,
such
as
testing
and
vulnerability
detection.
Such
large
number
studies
hinders
the
community
from
understanding
current
research
landscape.
This
paper
aims
summarize
knowledge
applied
for
analysis.
We
review
belonging
twelve
categories
corresponding
techniques,
tools,
datasets
been
solve
them.
To
do
so,
we
conducted
an
extensive
literature
search
identified
494
studies.
our
observations
findings
with
help
Our
suggest
analysis
is
consistently
increasing.
synthesize
commonly
used
steps
overall
workflow
each
task
employed.
identify
comprehensive
list
available
tools
useable
this
context.
Finally,
discusses
perceived
challenges
area,
including
availability
standard
datasets,
reproducibility
replicability,
hardware
resources.
Editor's
note:
Open
Science
material
was
validated
by
Journal
Systems
Software
Board.
