Object-Centric Reflection - Thesis Defense

Object-Centric
Reﬂection
Unifying Reﬂection and Bringing it Back to
Objects

PhD Defense
Jorge Ressia
Advisor
Oscar Nierstrasz

A reﬂective computational system is
capable of inspecting, manipulating and
altering its representation of itself.

Smith, 1982

Structural
Reﬂection

Behavioral
Reﬂection

Message send
Message

Object

Object-speciﬁc
Reﬂection

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

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

Which is the relationship?
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

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)

Object
Paradox

Reﬂection
Requirements

Object
Paradox

Uniﬁed Reﬂection
Uniform Requirements
Approach

Thesis:

To overcome the object paradox while providing a
unified and uniform solution to the key reflection
requirements we need an object-centric reflective
system which targets specific objects as the central
reflection mechanism through explicit meta-objects.

Object-Centric
Applications MetaSpy
Debugging

Talents Chameleon

Host Language

56

Object-Centric
Debugging

Talents Chameleon

Host Language

57

Object-Centric
Debugging

Talents Chameleon

Host Language

58

Object-Centric
Debugging

Talents Chameleon

Host Language

59

Object-Centric
Debugging

Talents Chameleon

Host Language

60

Object-Centric
Debugging

Talents Chameleon

Host Language

61

Object-Centric
Debugging

Talents Chameleon

Host Language

62

Object-Centric
Debugging

Talents Chameleon

Host Language

63

Object-Centric
Debugging

Talents Chameleon

Host Language

64

Object-Centric
Debugging

Talents Chameleon

Host Language

66

2010
time. Nierstrasz
@run
delsnggli,T. Gîrba and O
Mo e R
J. R essia, L.

Class

Meta-object

Object

Class

Meta-object

Adapted Object

Source
Code
Syntactic
Analysis

Semantic
Analysis

AST Code
Manipulation Generation

Executable
Code

Meta-object
Meta-object

Meta-object
Meta-object

Composed
Meta-object

Any adaptation goes
through a meta-object

Object-Centric Reflection - Thesis Defense