Object-Centric Reflection - ESUG 2012

Object-Centric
Reﬂection
Jorge Ressia
Software Composition Group

Proﬁling:
Is the activity of analyzing a program
execution.

Domain-Sp

CPU time profiling
Mondrian [9] is an open and agile visualization engine.
visualization using a graph of (possibly nested) nodes an

Profile
a serious performance issue was raised1 . Tracking down
performance was not trivial. We first used a standard sam
Execution sampling approximates the time spent in an
by periodically stopping a program and recording the cu
under executions. Such a profiling technique is relatively
little impact on the overall execution. This sampling techn
all mainstream profilers, such as JProfiler, YourKit, xprof
MessageTally, the standard sampling-based profiler in P
tually describes the execution in terms of CPU consumpt
each method of Mondrian:
54.8% {11501ms} MOCanvas>>drawOn:
54.8% {11501ms} MORoot(MONode)>>displayOn:
30.9% {6485ms} MONode>>displayOn:
{ { | 18.1% {3799ms} MOEdge>>displayOn:
{ { ...
} | 8.4% {1763ms} MOEdge>>displayOn:
}
}
| | 8.0% {1679ms} MOStraightLineShape>>display:on:
| | 2.6% {546ms} FormCanvas>>line:to:width:color:
} { } ...
23.4% {4911ms} MOEdge>>displayOn:
...

We can observe that the virtual machine spent abou
the method displayOn: defined in the class MORoot. A ro
nested node that contains all the nodes of the edges of t
general profiling information says that rendering nodes a
great share of the CPU time, but it does not help in pin
and edges are responsible for the time spent. Not all grap
consume resources.
Traditional execution sampling profilers center their r
the execution stack and completely ignore the identity of th
the method call and its arguments. As a consequence, it
which objects cause the slowdown. For the example above,
says that we spent 30.9% in MONode>>displayOn: withou
were actually refreshed too often.

Coverage
PetitParser is a parsing framework combining ideas from
parser combinators, parsing expression grammars and pac
grammars and parsers as objects that can be reconfigured

CPU time profiling
Mondrian [9] is an open and agile visualization engine.

Profile
visualization using a graph of (possibly nested) nodes an
a serious performance issue was raised1 . Tracking down
performance was not trivial. We first used a standard sam
Execution sampling approximates the time spent in an
by periodically stopping a program and recording the cu
under executions. Such a profiling technique is relatively
little impact on the overall execution. This sampling techn
all mainstream profilers, such as JProfiler, YourKit, xprof
MessageTally, the standard sampling-based profiler in P
tually describes the execution in terms of CPU consumpt
{ { 30.9% {6485ms} MONode>>displayOn:
{ { | 18.1% {3799ms} MOEdge>>displayOn:
} ...
}
} | 8.4% {1763ms} MOEdge>>displayOn:
} { } | | 2.6% {546ms} FormCanvas>>line:to:width:color:
...
...

We can observe that the virtual machine spent abou
the method displayOn: defined in the class MORoot. A ro
nested node that contains all the nodes of the edges of t
general profiling information says that rendering nodes a
great share of the CPU time, but it does not help in pin
and edges are responsible for the time spent. Not all grap
consume resources.
Traditional execution sampling profilers center their r
the execution stack and completely ignore the identity of th
the method call and its arguments. As a consequence, it
which objects cause the slowdown. For the example above,
says that we spent 30.9% in MONode>>displayOn: withou

Domain
Coverage
PetitParser is a parsing framework combining ideas from
parser combinators, parsing expression grammars and pac
grammars and parsers as objects that can be reconfigured
1
http://forum.world.st/Mondrian-is-slow-next-step-tc
a2261116
2
http://www.pharo-project.org/

C omp lexity
stemand Ducasse 2003
Sy zaLan

little impact on the overall execution. This sampling technique is u
all mainstream profilers, such as JProfiler, YourKit, xprof [10], an
MessageTally, the standard sampling-based profiler in Pharo Sm
tually describes the execution in terms of CPU consumption and i
| 18.1% {3799ms} MOEdge>>displayOn:
...
...
...

We can observe that the virtual machine spent about 54% o
the method displayOn: defined in the class MORoot. A root is the
nested node that contains all the nodes of the edges of the visua
general profiling information says that rendering nodes and edge

Domain-Specific Profiling 3

CPU time profiling

Which is the relationship?
Mondrian [9] is an open and agile visualization engine. Mondrian describes a
visualization using a graph of (possibly nested) nodes and edges. In June 2010
a serious performance issue was raised1 . Tracking down the cause of the poor
performance was not trivial. We first used a standard sample-based profiler.
Execution sampling approximates the time spent in an application’s methods
by periodically stopping a program and recording the current set of methods
under executions. Such a profiling technique is relatively accurate since it has
little impact on the overall execution. This sampling technique is used by almost
all mainstream profilers, such as JProfiler, YourKit, xprof [10], and hprof.
MessageTally, the standard sampling-based profiler in Pharo Smalltalk2 , tex-
tually describes the execution in terms of CPU consumption and invocation for

?
...
...
...

We can observe that the virtual machine spent about 54% of its time in
the method displayOn: defined in the class MORoot. A root is the unique non-
nested node that contains all the nodes of the edges of the visualization. This
general profiling information says that rendering nodes and edges consumes a
great share of the CPU time, but it does not help in pinpointing which nodes
and edges are responsible for the time spent. Not all graphical elements equally
consume resources.
Traditional execution sampling profilers center their result on the frames of
the execution stack and completely ignore the identity of the object that received
the method call and its arguments. As a consequence, it is hard to track down
which objects cause the slowdown. For the example above, the traditional profiler
says that we spent 30.9% in MONode>>displayOn: without saying which nodes

Coverage
PetitParser is a parsing framework combining ideas from scannerless parsing,

Debugging:
Is the process of interacting with a
running software system to test and
understand its current behavior.

When during the execution is this method called? (Q.13)
Where are instances of this class created? (Q.14)
Where is this variable or data structure being accessed?
(Q.15)
What are the values of the argument at runtime? (Q.19)
What data is being modiﬁed in this code? (Q.20)
How are these types or objects related? (Q.22)
How can data be passed to (or accessed at) this point
in the code? (Q.28)
What parts of this data structure are accessed in this
code? (Q.33)

Where is this variable or data structure being accessed?
(Q.15)
How can data be passed to (or accessed at) this point
in the code? (Q.28)
What parts of this data structure are accessed in this
code? (Q.33) llito
Si etal. g softwar
e
ask durin
gr ammers s. 2008
Questi ons pro ution task
evol

Which is the relationship?


?
Where is this variable or data structure being accessed? (Q.15)
How can data be passed to (or accessed at) this point in the code? (Q.28)
What parts of this data structure are accessed in this code? (Q.33)

visualization using a graph of (possibly nested) nodes and edges. In June 2010
a serious performance issue was raised1 . Tracking down the cause of the poor
performance was not trivial. We first used a standard sample-based profiler.
Execution sampling approximates the time spent in an application’s methods
by periodically stopping a program and recording the current set of methods

Profiling
under executions. Such a profiling technique is relatively accurate since it has
little impact on the overall execution. This sampling technique is used by almost
all mainstream profilers, such as JProfiler, YourKit, xprof [10], and hprof.
MessageTally, the standard sampling-based profiler in Pharo Smalltalk2 , tex-
tually describes the execution in terms of CPU consumption and invocation for

?
...
...
...

We can observe that the virtual machine spent about 54% of its time in
the method displayOn: defined in the class MORoot. A root is the unique non-
nested node that contains all the nodes of the edges of the visualization. This
general profiling information says that rendering nodes and edges consumes a
great share of the CPU time, but it does not help in pinpointing which nodes
and edges are responsible for the time spent. Not all graphical elements equally
consume resources.
Traditional execution sampling profilers center their result on the frames of
the execution stack and completely ignore the identity of the object that received
the method call and its arguments. As a consequence, it is hard to track down
which objects cause the slowdown. For the example above, the traditional profiler
says that we spent 30.9% in MONode>>displayOn: without saying which nodes

Coverage

Debugging


?
Where is this variable or data structure being accessed? (Q.15)
How can data be passed to (or accessed at) this point in the code? (Q.28)
What parts of this data structure are accessed in this code? (Q.33)

Class

Meta-object

Object

Class

Meta-object

Evolved Object

Debugging Proﬁling

Execution Structure
Reiﬁcation Evolution

Object-Centric
Debugging
2
201Nierstrasz
ICSE .
rgel and
O
J. Ressi a, A, Be

stack-centric debugging

InstructionStream class>>on:
InstructionStream class>>new
InstructionStream>>initialize
step into, CompiledMethod>>initialPC
step over, InstructionStream>>method:pc:
resume InstructionStream>>nextInstruction
MessageCatcher class>>new
InstructionStream>>interpretNextInstructionFor:
...

object-centric debugging

centered on centered on
the InstructionStream class the InstructionStream object
initialize
on:
next message, next message, method:pc:
new next change nextInstruction ...
next change
interpretNextInstructionFor:
...

Halt on next message
Halt on next message/s named
Halt on state change
Halt on state change named
Halt on next inherited message
Halt on next overloaded message
Halt on object/s in call
Halt on next message from
package

MetaSpy

OLS 2011
TO Ber gel etal.

C omp lexity
stema, Ducasse 2003
Sy anz L

What if we do not
know what to evolve?

Back in time
Debugger

Debu gger
w
Floal. ECOOP 2008
O b ject nhard e
t
Lie

name value
init@t1 null
predecessor

name value
:Person ﬁeld-write@t2 'Doe'
predecessor
name
value
ﬁeld-write@t3 'Smith'
person := Person new t1
...
name := 'Doe' t2
...
name := 'Smith' t3

Talents

scg.unibe.ch/research/talents

Talents
ST 2 011F. Perin and
IW ie rstrasz,
îr ba, O. N gli
J. Re ssia, T. G L. Reng

scg.unibe.ch/research/talents

Dynamically
composable units of
reuse

Object-
Centric Prisma
Debugger

Subjectopia MetaSpy

Talents Chameleon

Alexandre Marcus Stéphane Oscar
Bergel Denker Ducasse Nierstrasz

Lukas Tudor Fabrizio
Renggli Gîrba Perin

Object-Centric Reflection - ESUG 2012