Journal of Software Engineering and Applications, 2012, 5, 695-710 Published Online September 2012 (
Notification Oriented and Object Oriented Paradigms
Comparison via Sale System
Jean M. Simão1,2, Danillo L. Belmonte1, Adriano F. Ronszcka2, Robson R. Linhares1,2,
Glauber Z. Valença2, Roni F. Banaszewski1, João A. Fabro2, Cesar A. Tacla1,2,
Paulo C. Stadzisz1,2, Márcio V. Batista2
1Graduate School in Electrical Engineering & Industrial Computer Science (CPGEI), Federal University of Technology-Paraná, Cu-
ritiba, Brazil; 2Graduate School of Applied Computing (PPGCA), Federal University of Technology-Paraná, Curitiba, Brazil.
Received July 8th, 2012; revised August 11th, 2012; accepted August 23rd, 2012
This paper presents a new programming paradigm named Notification-Oriented Paradigm (NOP) and analyses the per-
formance aspects of NOP programs by means of an experiment. NOP provides a new manner to conceive, structure, and
execute software, which would allow causal-knowledge organization and decoupling better than standard solutions
based upon current paradigms. These paradigms are essentially Imperative Paradigm (IP) and Declarative Paradigm
(DP). In short, DP solutions are considered easier to use than IP solutions due to the concept of high-level programming.
However, they are considered slower in execution and lesser flexible in development. Anyway, both paradigms present
similar drawbacks such as redundant causal-evaluation and strongly coupled entities, which decrease software per-
formance and processing distribution feasibility. These problems exist due to an orientation to a monolithic inference
mechanism based on sequential evaluation searching on passive computational entities. NOP proposes another way to
structure software and make its inferences, which is based on small, collaborative, and decoupled computational entities
whose interaction happens through precise notifications. This paper presents a quantitative comparison between two
equivalent implementations of a sale system, one developed according to the principles of Object-Oriented Paradigm
(OOP/IP) in C++ and other developed according to the principles of NOP based on a NOP framework in C++. The re-
sults showed that NOP implementation obtained quite equivalent results with respect to OOP implementation. This
happened because the NOP framework uses considerable expensive data-structures over C++. Thus, it is necessary a
new compiler to NOP in order to actually use its potentiality.
Keywords: Notification Oriented Paradigm; Notification Oriented Inference; NOP and OOP Comparison
1. Introduction
This section mentions the drawbacks of the main pro-
gramming paradigms, introduces a new paradigm, and
presents the paper objectives.
1.1. Review Stage
The computational processing power has grown each
year and the tendency is that technology evolution con-
tributes to the creation of still faster processing technolo-
gies [1,2]. Even if this scenario is positive in terms of
pure technology evolution, in general it does not moti-
vate information-technology professionals to optimize
the use of processing resources when they develop soft-
ware [1,3].
This behavior has been tolerated in standard software
development where there is no need of intensive proc-
essing or processing constraints. However, it is not ac-
ceptable to certain software classes, such as software for
embedded systems. Such systems normally employ
less-powerful processors due to factors such as con-
straints on power consumption and system price to a
given market [1,4,5].
Besides, misuse of computational power in software
can also cause overuse of a given standard processor,
implying in execution delays [1,4,6]. Still, in complex
software, this can even exhaust a processor capacity,
demanding faster processor or even some sort of distri-
butions (e.g. dual-core) [1,4,7]. Indeed, an optimiza-
tion-oriented programming could avoid such drawbacks
and related costs [1,4,8].
Thus, suitable engineering tools for software devel-
opment, namely programming languages and their envi-
ronments, should facilitate the development of optimized
Copyright © 2012 SciRes. JSEA
Notification Oriented and Object Oriented Paradigms Comparison via Sale System
and correct code [1,9-12]. Otherwise, the engineering
costs to produce optimized-code could exceed those of
upgrading the processing capacity [1,4,9-11].
Still, suitable tools should also make the development
of distributable code easy once, even with optimized
code, distribution may be actually demanded in some
cases [1,13-16]. However, distribution is itself a problem
once, under different conditions, it could entail a set of
(related) problems, such as complex load balancing,
communication excess, and hard fine-grained distribution
In this context, a problem arises from the fact usual
programming languages (e.g. Pascal, C/C++, and Java)
present no real facilities to develop optimized and really
distributable code, particularly in terms of fine-grained
decoupling of code [1,3,4,17,18]. This happens due to the
structure and execution nature imposed by their para-
digms [1,7,9].
1.2. Imperative and Declarative Programming
Usual programming languages are based on the Impera-
tive Paradigm, which cover sub-paradigms such as Pro-
cedural and Object Oriented ones [1,10,19,20]. Besides,
the latter is normally considered better than the former
due to its richer abstraction mechanism. Anyway, both
present drawbacks due to their imperative nature [1,10,
Essentially, Imperative Paradigm imposes loop-ori-
ented searches over passive elements related to data (e.g.
variables, vectors, and trees) and causal expressions (i.e.
if-then statements or similar) that cause execution re-
dundancies. This leads to the creation of programs as
monolithic entities comprising prolix and coupled code,
generating non-optimized and interdependent code exe-
cution [1,9,21,22].
The Declarative Paradigm is the alternative to the Im-
perative Paradigm. Essentially, it enables a higher level
of abstraction and easier programming [1,20,21]. Also,
some declarative solutions avoid many execution redun-
dancies in order to optimize execution, such as Rule
Based System (RBS) based on Rete or Hal algorithms
[1,23-26]. However, programs constructed using usual
languages from Declarative Paradigm (e.g. LISP, PRO-
LOG, and RBS in general) or even using optimized solu-
tion (e.g. Rete-driven RBS) also present drawbacks [1,
Declarative Paradigm solutions use computationally
expensive high-level data structures causing considerable
processing overheads. Thus, even with redundant code,
Imperative Paradigm solutions are normally better in
performance than Declarative solutions [1,10,27]. Fur-
thermore, similarly to the Imperative Paradigm pro-
gramming, the Declarative one also generates code cou-
pling due to similar search-based inference process [1,4,
21]. Still, other approaches, such as event-driven and
functional programming, do not solve these problems
even if they may reduce some problems, like redundan-
cies [1,22,27].
1.3. Development Issues & Solution Perspective
As a matter of fact, there are software development is-
sues in terms of ease composition of optimized and dis-
tributable code. Therefore, this prompts for new solutions
to make simpler the task of building better software. In
this context, a new programming paradigm, called Noti-
fication Oriented Paradigm (NOP), was proposed re-
garding some of the highlighted problems [1,4,8,9].
The NOP basis was initially proposed by J. M. Simão
as a manufacturing discrete-control solution [28,29]. This
solution was evolved as general discrete-control solution
and then as a new inference-engine solution [4], attaining
finally the form of a new programming paradigm [8-10].
Since then, efforts have been produced to contribute to
the establishment of this paradigm [1,30-35].
The essence of NOP is its inference process based on
small, smart, and decoupled collaborative entities that
interact by means of precise notifications [1,4]. This
solves redundancies and centralization problems of the
current causal-logical processing, thereby solving proc-
essing misuse and coupling issues of current paradigms
1.4. Paper Context and Objective
This paper discusses NOP as a solution to certain current
paradigm deficiencies. Particularly, the paper presents a
performance study, in a mono-processed case, related to a
program based on NOP compared against an equivalent
program based on Imperative/Object-Oriented Paradigm.
The NOP program is elaborated in the current NOP
framework over C++, whereas the OOP program is
elaborated in C++. Thus, an objective of the paper is
evaluated the current NOP materialization in terms of
Furthermore, this paper presents the results from two
implementations of the NOP Framework. The first is the
original one, designed and presented in [10]. The second
is an optimized version presented in [34].
Thus, another objective is demonstrate how relevant
and appropriated are some refactoring and optimizations
in the NOP framework, thereby realizing if is or not nec-
essary to built a NOP compiler.
2. Background
This section explores programming paradigm drawbacks.
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Notification Oriented and Object Oriented Paradigms Comparison via Sale System 697
2.1. Imperative Programming Issues
The main (related) drawbacks of Imperative Program-
ming are concerned to redundancies and code coupling.
The 1st mainly affects the processing time and the 2nd
the processing distribution, as detailed in the next sub-
sections [1,4].
2.1.1. Imperative Programming Redundancy
In Imperative Programming, like procedural or object
oriented programming, a number of code redundancies
and interdependences comes from the manner the causal
expressions are evaluated. This is exemplified in the
pseudo-code in Figure 1 that represents a usual code
elaborated without strong technical and intellectual ef-
forts. This means that the pseudo-code was elaborated in
a non-complicated manner, as software elaboration should
ideally be [1,8,10].
In the example, each causal expression has three logi-
cal premises and a loop forces the sequential evaluation
of all causal expressions. However, most evaluations are
unnecessary because usually just few attributes of objects
(i.e. variables) have their values changed at each iteration.
This type of code causes the problem called, in the com-
puter science, temporal and structural redundancy [1,
The temporal redundancy is the repetitive unnecessary
evaluation of causal expressions in the presence of ele-
ment states (e.g. attribute or variable states) already
evaluated and unchanged. For instance, this occurs in the
considered loop-oriented code example. The structural
redundancy, in turn, is the recurrence of a given logical
expression evaluation in two or more causal expressions.
For instance, the logical expression (object_1.attribute_1
= 1) is replicated in several causal expressions (if-then
statements) [1,4,8].
These redundancies can be considered unimportant in
this didactic code example, mainly if the number (n) of
Figure 1. Example of imperative code [1].
causal expressions is small. However, if more complex
examples were considered, integrating many (remaining)
redundancies, there would be a tendency to performance
degradation and increasing of development complexity
inclusively to avoid that degradation [1,8,10].
The code redundancies may result, for example, in the
need of a more powerful processor than it is really re-
quired [1,4,7]. Also, they may result in the need for code
distribution to processors, thereby implying in other
problems such as module splitting and synchronization.
These problems, even if solvable, are additional issues in
the software development whose complexity increases as
much as the fine-grained code distribution is demanded,
particularly in terms of logical-causal (“if-then”) calcula-
tion [1,4,7,9].
2.1.2. Imperative Programming Coupling
Besides the usual repetitive and unnecessary evaluations
in the imperative code, the evaluated elements and causal
expressions are passive in the program decisional execu-
tion, although they are essential elements in this process.
For instance, a given if-then statement (i.e. a causal ex-
pression) and concerned variables (i.e. evaluated ele-
ments) do not take part in the decision with respect to the
moment in time they must be evaluated [1,4].
The passivity of causal expressions and concerned
elements is due to the way they are evaluated in the time.
An execution line in each program (or at least in each
program thread) carries out this evaluation, usually
guided by means of a set of loops. As these causal ex-
pressions and concerned elements do not actively con-
duct their own execution (i.e. they are passive), their in-
terdependency is not explicit in each program execution
Thus, at first, causal expressions or evaluated elements
depend on results or states of others. This means that
they are somehow coupled and should be placed together,
at least in the context of each module. This coupling in-
creases code complexity, which complicates, for instance,
an eventual distribution of each single code part in fine-
grained way. This makes each program module, or even
the whole program, a monolithic computational unit [1,
2.1.3. Imperative Programming Distribution
When distribution is intended (process, processor, and
cluster distribution), a code analysis could identify less
dependent code sets to facilitate splitting. However, this
is normally a complex activity due to the code coupling
and complexity caused by imperative programming [1,18,
In this sense, well-designed software composed of
modules as decoupled as possible, using advanced and
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Notification Oriented and Object Oriented Paradigms Comparison via Sale System
quite complicated software engineering concepts like
aspects [13] and axiomatic design [37], can help distri-
bution. Still, middleware such as CORBA and RMI
would be helpful in terms of infrastructure to some types
of module distribution, if there is enough module de-
coupling [1,13,38,39]. In spite of those advances, distri-
bution of single code elements or even code modules is
still a complex activity demanding research efforts [1,
13,14,17,36,40]. It would be necessary additional efforts
to achieve easiness in distribution (e.g. automatic, fast,
and real-time distribution), as well as correctness in dis-
tribution (e.g. balanced and minimal inter-dependent
distribution) [1,4].
Indeed, distribution hardness is an issue once there are
contexts where distribution is actually necessary [1,7,15,
16]. For instance, a given optimized program exceeding
the capacity of a given processor would demand proc-
essing splitting [1,6]. Other instances are programs that
must guaranty error isolation or even robustness by dis-
tributed module redundancy [1,28]. These features can be
found in application such as nuclear-plant control [41],
intelligent manufacturing [29,42,43], and cooperative
controls [44].
Besides, there are other applications that are inherently
distributed and need flexible distribution, such as those
of ubiquitous computing. More precise examples are
sensor networks and some intelligent manufacturing con-
trol [1,43,45]. Moreover, the easy and correct distribution
is an expectation due to the reduction of processor prices
and the communication networks advances as well [1,
2.1.4. Imperative Programming Development
In short, as explained in terms of Imperative and De-
clarative Paradigms, current paradigms do not make easy
to achieve the following qualities together [1]:
Effective (causal) code optimization to be sure about
the eventual need of a faster processor or multiproc-
Easy way to compose correct code (i.e. without er-
Easy code splitting and distribution to processing
This is a problem mainly when the increasing market
demand by software is considered, where development
easiness, code optimization, and processing distribution
are current requirements [1,47-49]. This software devel-
opment “crisis” impels new researches and solutions to
make simpler the task of building better software [1].
In this context, a new programming paradigm called
Notification Oriented Paradigm (NOP) was proposed to
solve some of the highlighted problems. NOP keeps the
main advantages of Declarative Programming/Rule Based
Systems (e.g. higher causal abstraction and organization
by means of fact base and causal base) and Imperative/
Object Oriented Programming (e.g. reusability, flexibility,
and suitable structural abstraction via classes and objects).
Moreover, NOP would evolve some of their concepts and
solve some of their deficiencies [1,4,8,10].
2.2. Declarative Programming Issues
A well-known example of Declarative Programming and
its nature is Rule Based System (RBS) [1,4,50]. A RBS
provides a high-level language in the form of causal-
rules, which prevents the developers from algorithm par-
ticularities [1,50]. RBS is composed of three general
modular entities (Fact Base, Rule Base, and Inference
Engine) with well-distinguished responsibilities, as usual
in declarative language (e.g. LISP, PROLOG, and CLIPS)
In Declarative Programming, the variable states are
dealt in a Fact Base and the causal knowledge in a Causal
Base (Rule Base in RBS), which are automatically
matched by means of an Inference Engine (IE) [1,24,50].
Moreover, some IE algorithms (e.g. RETE [23-25],
TREAT [52,53], LEAPS [54], and HAL [26] algorithms)
avoid most of temporal and structural redundancies
[1,10]. However, the data structures used to solve redun-
dancies in those IEs implies in too much consuming of
processing capacity [1,25].
Actually, the use of Declarative Programming only
compensates when the software under development pre-
sents many redundancies and few data variation. Also, in
general, an IE related to a given declarative language
limits the inventiveness, makes difficult some algorithm
optimizations, and obscures hardware access, which can
be inappropriate in certain contexts [1,10,22,27,55].
A solution to these problems can be the symbiotic use
of Declarative and Imperative Programming [1,19,55].
Such approach has been presented, like CLIPS++, ILOG
Rules, and R++. However, they would not be popular
due to factors like syntax and paradigms mixing or tech-
nical cultural reasons [1,10]. Anyway, even Declarative
Programming being a relevant solution, it does not solve
certain issues [1].
Indeed, beyond processing-overhead, Declarative Pro-
gramming also presents code coupling. Each declarative
program has also an execution or inference policy whose
essence is a monolithic entity (e.g. Inference Engine)
responsible for analyzing every passive data-entity (Fact-
Base) and causal expression (Causal-Base). Thus, the
inference, based upon a search technique (i.e. matching),
implies a strong dependency between facts and rules be-
cause they together constitute the search space [1,4].
2.3. Other Programming Approach Drawbacks
Enhancements in the context of Imperative and Declara-
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Notification Oriented and Object Oriented Paradigms Comparison via Sale System 699
tive Paradigm have been provided to reduce the effects of
recurrent loops or searches, such as event-driven pro-
gramming and functional programming [1,10,51,56].
Event programming and functional programming have
been used to different software such as discrete control,
graphical interfaces, and multi-agent systems [1,10,51,
Essentially, each event (a button pressing, a hardware
interruption or a received message) triggers a given exe-
cution (process, procedure or method execution), usually
in a given sort of module (block, object or even agent),
instead of repeated analysis of the conditions for its exe-
The same principle applies to the called functional
programming whose difference would be function calling
via other function in place of events. Still, function
means procedure, method or similar unity. Besides, func-
tional and event programming used together would be
usual [1].
However, the algorithm in each module process or
procedure is built using Declarative or Imperative pro-
gramming. This implies in the highlighted deficiencies,
namely code redundancy and coupling, even if they are
diminished by events or function calls. Indeed, if each
module has extensible or even considerable causal-logi-
cal calculation, they can be a problem together in terms
of processing misuse and distribution. This may demand
special design effort to achieve optimization and module
decoupling [1].
An alternative programming approach is the Data
Flow Programming that supposedly should allow the
program execution oriented by data instead of an execu-
tion line based on search over data. Therefore, this would
allow decoupling and distribution [1,14]. The distribution
in Data Flow Programming is achieved in arithmetical
processing, however it is not really achieved in logi-
cal-causal calculation [1,14,17]. This calculation happens
by current advanced inference engines, namely Rete
The fact is current inference engines attempt to
achieve a data-driven approach. However, the inference
process is still based on searches even if they use data
from (some sort of) object-oriented tree to speed up the
inference cycle or searches. Thus, the highlighted prob-
lems remain [1].
2.4. Enhancement in Programming
In short, as explained in terms of Imperative and De-
clarative Paradigms, current paradigms do not make easy
to achieve the following qualities together [1]:
Effective (causal) code optimization to be sure about
the eventual need of a faster processor or multiproc-
Easy way to compose correct code (i.e. without er-
Easy code splitting and distribution to processing
This is a problem mainly when the increasing market
demand by software is considered, where development
easiness, code optimization, and processing distribution
are current requirements [1,47-49]. This software devel-
opment “crisis” impels new researches and solutions to
make simpler the task of building better software [1].
In this context, a new programming paradigm called
Notification Oriented Paradigm (NOP) was proposed to
solve some of the highlighted problems. NOP keeps the
main advantages of Declarative Programming/Rule Based
Systems (e.g. higher causal abstraction and organization
by means of fact base and causal base) and Impera-
tive/Object Oriented Programming (e.g. reusability, flexi-
bility, and suitable structural abstraction via classes and
objects). Moreover, NOP would evolve some of their
concepts and solve some of their deficiencies [1,4,8,10].
3. Notification Oriented Paradigm (NOP)
The Notification Oriented Paradigm (NOP) introduces a
new concept to conceive, construct, and execute software.
NOP is based upon the concept of small, smart, and de-
coupled entities that collaborate by means of precise no-
tifications to carry out the software inference [1,4,8].
This would allow enhancing software applications per-
formance and potentially makes easier to compose soft-
ware, both non-distributed and distributed ones [1,10].
3.1. NOP Structural View
NOP causal expressions are represented by common
rules, which are naturally understood by programmers of
current paradigms. However, each rule is technically
enclosed in a computational-entity called “Rule” [1,8]. In
Figure 2, there is a Rule content example, which would
be related Sale System.
Structurally, a Rule has two parts, namely a “Condi-
tion” and an “Action”, as shown by means of the UML
class diagram in Figure 3. Both are entities that work
together to handle the causal knowledge of the Rule
computational-entity. The Condition is the decisional
part, whereas the Action is the execution part of the Rule.
Both make reference to factual elements of the system
NOP factual elements are represented by means of a
special type of entity called “Fact_Base_Element” (FBE).
A FBE includes a set of attributes. Each attribute is rep-
resented by another special type of entity called “Attrib-
ute” [1,8,10].
Attributes states are evaluated in the Conditions of
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Notification Oriented and Object Oriented Paradigms Comparison via Sale System
Copyright © 2012 SciRes. JSEA
Change State*
Notify State
Condit ion
Notify State0..*
I nst igat ion
Instig ate
<<NOP>> <<NOP>>
Change State*
Notify State
Condit ion
Notify State0..*
I nst igat ion
Instig ate
<<NOP>> <<NOP>>
have their inference carried out by active collaboration of
its notifier entities [4]. In short, the collaboration happens
as follow: for each change in an Attribute state of a FBE,
the state evaluation occurs only in the related Premises
and then only in related and pertinent Conditions of
Rules via punctual notifications between the collabora-
tors [1,4,8].
In order to detail this Notification Oriented Inference,
it is firstly necessary to explain the Premise composition.
Each Premise represents a Boolean value about one or
even two Attribute state, which justify its composition: 1)
a reference to a discrete value of an Attribute, called
Reference, which is received by notification; 2) a logical
operator, called Operator, useful to make comparisons;
and 3) another value called Value that can be a constant
or even a discrete value of other referenced Attribute
Figure 2. Rule and Fact_Base_Element (FBE) class diagram
[1,8,10]. A Premise makes a logical calculation when it receives
notification of one or even two Attributes (Refere nce and
even Va lue). This calculation is carried out by comparing
the Reference with the Value, using the Operator. In a
similar way, a Premise collaborates with the causal
evaluation of a Condition. If the Boolean value of a noti-
fied Premise is changed, then it notifies the related Con-
dition set [1,4,8].
Rules by associated entities called “Premises”. In the
example, the Condition of the Rule is associated to three
Premises, which verify the state of FBE Attributes as
follow: 1) Is the product branch perishable? 2) Is the
product valid? 3) Is the product perishable date? [1,8,10].
When each Premise of a Rule Condition is true, which
is concluded via a given inference process, the Rule be-
comes true and can activate its Action composed of spe-
cial-entities called “Instigations”. In the considered Rule,
the Action “has” only one Instigation that makes the
System shows a message that the product is perishable
Thus, each notified Condition calculates their Boolean
value by the conjunction of Premises values. When all
Premises of a Condition are satisfied, a Condition is also
satisfied and notifies the respective Rule to execute [1,4].
The collaboration between NOP entities via notifications
can be observed at the schema illustrated in Figure 4. In
this schema, the flow of notifications is represented by
arrows linked to rectangles that symbolize NOP entities
Instigations are linked to and instigate the execution of
“Methods”, which are another special-entity of FBE.
Each Method allows executing services of its FBE. Gen-
erally, the call of FBE Method changes one or more FBE
Attribute states, thereby feeding the inference process
An important point to clarify about NOP collaborative
entities is that each entity (e.g. Attributes) registers the
entities interested in its state (e.g. Premises) in the mo-
ment that they are created. For example, when a Premise
is created and makes reference to an Attribute, the latter
automatically includes the former in its internal set of
entities to be notified when its state change [1,4,8].
3.2. NOP Inference Process
The NOP inference process is innovative once Rules
Figure 3. The representation of a rule.
Notification Oriented and Object Oriented Paradigms Comparison via Sale System 701
Figure 4. Rule notification chain and their collaborators [1,4,8,10].
3.3. NOP-Redundancy Avoidance—Performance
In NOP, an Attribute state is evaluated by means of a set
of logical expression (Premise) and causal expression
(Condition) in the changing of its state. Thanks to the
cooperation by means of precise notifications, NOP
avoids the two types of aforementioned redundancies
The temporal redundancy is solved in NOP by elimi-
nating searches over passive elements, once some data-
entities (e.g. Attributes) are reactive in relation to their
state updating and can punctually notify only the parts of
a causal expression that are interested in the updated state
(e.g. Premises), avoiding that other parts and even other
causal expressions be unnecessarily (re-)evaluated [1,4,
Indeed, each Attribute notifies just the strictly con-
cerned Premise due to state change and each Premise
notifies just the strictly concerned Condition due to state
change, therefore implicitly avoiding temporal redun-
dancy. Besides, the structural redundancy is also solved
in NOP when Premise collaboration is shared with two or
more causal expressions (i.e. Conditions). Thus, the Pre-
mise carries out logic calculation only once and shares
the logic result with the related Conditions, thereby
avoiding re-evaluations [1,4,8,10].
3.4. NOP—Decoupling and Distribution
Actually, besides solving redundancy and then perform-
ance problems, NOP also is potentially applicable to de-
velop parallel/distributed applications because of the
“decoupling” (or minimal coupling to be precise) of enti-
ties. In inference terms, there is no great difference if an
entity is notified in the same memory region, in the same
computer memory or in the same sub-network [1,4,8,10].
For instance, a notifier entity (e.g. an Attribute) can
execute in one machine or processor whereas a “client”
entity (e.g. a Premise) can execute in another. For the
notifier, it is only necessary to know the address of the
client entity. However, these issues also should be con-
sidered in more technical and experimental details in
future publications once there are current works in this
context [1,4,8,10].
3.5. NOP Originality
At first, NOP entities (Rules and FBEs) may be confused
as just an advance of Rule Based, Object Oriented, and
Event-Driven Systems, including then Data-Flow-like
Programming and Inference Engines. However, NOP is
far than a simple evolution of them. It is a new approach
that proposes Rule and FBE smart-entities composed of
other collaborative punctual-notifier smart-entities, which
provide new type of logical-causal calculation or infer-
ence process [1,4,8,10].
This inference solution, in turn, is not just an applica-
tion of known software notifier patterns, useful to Event-
Driven Systems, such as the observer-pattern. It is the
extrapolation of that once the execution of the NOP
logical-causal calculation via punctual notifications has
not been conceived before. At least, this is the honest
authors’ perception after more than one decade of litera-
ture reviewing [1,4,8,10].
Indeed, this inference innovation changes all the soft-
ware essence with respect to logical-causal reasoning (i.e.
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Notification Oriented and Object Oriented Paradigms Comparison via Sale System
one of its essential parts) and then makes the solution a
new programming paradigm. Moreover, as NOP changes
the form in which software is structured and executed, it
also determines a change in the form that software is
conceived [1,4,8,10].
3.6. NOP Implementation
In order to provide the use of NOP, its entities were ma-
terialized in C++ language in the form of a framework
[10]. Indeed, it is usual to emergent paradigms be mate-
rialized by means of programming languages of current
paradigms, already changing them somehow, before the
conception of a particular language and compiler [10].
Anyway, developed NOP applications have been made
just by instantiating the framework [10,31-33,35]. More-
over, to make easier this process, a prototypal wizard tool
has been proposed to automate this process. It is a tool
that generates NOP smart-entities from rules elaborated
in a graphical interface. In this case, developers “only”
need to implement FBEs with Attributes and Methods,
once other NOP special-entities will be composed and
linked by the tool. This allows using the time to the con-
struction of the causal base (i.e. composition of NOP
rules) without concerns about instantiations of the NOP
4. The Sale Order System
In order to make some comparisons between NOP and
Object-Oriented Paradigm (OOP), this paper presents a
Sale Order System as case of study. This application was
firstly described (in Portuguese and in shorter way) in
[32] and was there first used to some other previous ex-
periments. This system was proposed due to the rele-
vance of its nature.
Indeed, there are many systems of the same domain
built in OOP. Thus, the creation of a NOP version is in-
teresting. Among all the applications designed in NOP so
far, there is none with the same commercial focus and
from the same domain.
4.1. Sale Order System: Requirements
This relevant proposed software system, thought to
compare NOP and OOP implementations, presents the
requirements considered in Tables 1-3.
The software that has been used in all comparisons in
this paper implements the functional requirement number
RF1 from Table 1, the sub-requirements from Table 2
(related to RF1), and all non-functional requirements
from Table 3.
4.2. Sale Order System: Structure
The requirement definition allows modeling the system
Table 1. Functional requirements.
RF1 To provide selling of products.
Table 2. Sub-requirements related to functional require-
SR1.1 To not allow selling products with stock equal zero.
SR1.2 To debit the stock of the sold quantity of product.
SR1.3 To allow selling more than one product for each sale.
SR1.4 To persist the data of the sale.
SR1.5 To not allow selling perishable product expired.
Table 3. Non-functional requirements.
RNF1 To be implemented using C++ language.
RNF2 To be implemented on both paradigms, OOP in C++
and NOP Framework over C++.
RNF3 To run under console (ms-dos) environment.
RNF4 To persist data in text files.
by means of a class diagram shown in Figure 5, which is
useful to both OOP and NOP implementations.
The SalesOrder class is the most important, having
association with Customer, PaymentForm, and SalesOr-
derItensList. Moreover, it is possible to verify that the
Product class is specialized in three different types of
4.3. Sale Order System: Dynamics
The Figure 6 shows an Activity Diagram of the entire
selling process without focusing on any implementation.
4.4. Sale Order System: Execution
The sale starts with customer code, which passes by a
verification to check if it is valid. After that, it is neces-
sary to inform the branch of the product, as well as check
its veracity. The sale process continues requesting prod-
ucts which will compose the sale order.
The system provides validations for product and cus-
tomer existence. Moreover, the system checks if the
product is available in the stock. If the chosen product
belongs to the perishable branch, the system evaluates its
expiration date. If the product life is expired, it cannot be
After the whole cycle of product insertion into the sale
order, the sale can be completed filling out the payment
form. There are only two payment forms available,
which are cash and installment payment. If the payment
chosen is installment, the system checks if the customer
can complete the purchase, omparing his credit limit c
Copyright © 2012 SciRes. JSEA
Notification Oriented and Object Oriented Paradigms Comparison via Sale System
Copyright © 2012 SciRes. JSEA
Figure 5. Class diagram of sale order system.
with the total value of the sale. Actually, the system has
information about the credit limit of the customer.
Still, on the customer’s profile there is a parameter that
provides a classification type. This classification type is
used to provide a special discount during the sale com-
pletion. There is a range of 20 different customer’s clas-
sification types, which can provide discounts from 5% to
5. A Performance Study
This section shows the implementation of OOP (C++)
and in an equivalent manner in NOP (NOP framework in
C++) of the Sale System. Still, the section presents com-
parative tests performed between these two implementa-
5.1. Implementation Details
Both OOP and NOP implementations use same classes,
methods and procedures. The difference between them is
the fact that the principal method of the whole system (i.e.
Sale method) has been rewritten for each version.
In NOP, the sale method has its code written under the
principles of Rules and other collaborator smart-entities.
The Figure 7 shows an example of a code written in
OOP (in C++) and NOP (in NOP Framework over C++).
There are no more if-then causal tests and nested code
in NOP version. The entire sale flow is governed by
Rules and other collaborator smart-entities. For each
NOP Attribute that has its state changed, it will start the
notification process.
All causal tests performed in the OOP version are
handled quite differently in the NOP version. The Figure
7 somehow shows the differences between them. In turn,
the Figure 8 shows the pseudo-code of the process to
create a sale order based on the OOP paradigm.
5.2. First Experiments and Results
The experiments were performed using the original version
of the NOP Framework, i.e. 1st stable version designed
Figure 6. Activity diagram of the sale.
Notification Oriented and Object Oriented Paradigms Comparison via Sale System
Figure 7. Equivalent code in OOP and NOP.
by Banaszewski [9]. Tests performed with optimized
version of the Framework have also been added in order
to verify the performance difference between Frame-
The optimized version was developed by NOP re-
search group [9]. The same set of parameters was used in
all experiments. This set of parameters was implemented
for the version of the system developed purely on the
principles of OOP and also for the version designed in
It has been created 100, 1000, 10,000, and 100,000
sales for the test cases. The experiment was performed
three times to ensure the accuracy of the data. All the
data from the Sale Orders which were used in the fol-
lowing experiments were inserted directly on the code.
This kind of implementation is well known as hard-cod-
ing. The Tables 4-6 summarize the data which each sale
order used during each experiment cycle.
The experiments were performed on a PC with Core 2
Duo processor with 3 Gigabytes of RAM. Tests were
performed on Windows and Linux operating systems.
Both operating systems are preemptive multitasking,
although Linux had better time response of the processor.
For the Windows experiment the Visual Studio 2008 was
used with its C++ native compiler to design the system.
In Linux experiment the GCC compiler was used.
The results of the first test experiments are shown in
Tables 7 and 8. The first column in Table 7 represents
Table 4. First test sale order content.
First Experiment Order
Customer #1
Product #1—Electronic type
Payment Form
Table 5. Second test sale order content.
Second Experiment Order
Customer #20—Type number 20
Product #1—Electronic type
Product #2—Electronic type
Product #3—Electronic type
Product #4—Electronic type
Product #5—Electronic type
Payment Form
95% discount for customers of type 20.
Table 6. Third test sale order conte nt.
Third Experiment Order
Customer #20—Type number 20
Product #6—Perishable type (Shelf date expired)
Product #7—Perishable type
Product #8—Perishable type
Product #9—Perishable type
Product #10—Perishable type
Product #11—Perishable type
Payment Form
Installment (Customer does not have sufficient credit to use this
payment form)
95% discount for customers of type 20.
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Notification Oriented and Object Oriented Paradigms Comparison via Sale System 705
Figure 8. OOP pseudo code of the selling function.
Copyright © 2012 SciRes. JSEA
Notification Oriented and Object Oriented Paradigms Comparison via Sale System
Table 7. First test experiment on Windows environment.
Total of sale
orders OOP NOP
Performance NOP
over OOP
100 208 280 231 11.10%
1000 2028 2974 2302 13.51%
10,000 22,734 30,019 24,025 5.67%
100,000 234,650 338,390 243,226 3.65%
Table 8. First test experiment on Linux environment.
Total of sale
orders OOP NOP
Performance NOP
over OOP
100 98 106 100 2.04%
1000 917 1020 965 5.23%
10,000 8935 9775 9552 6.90%
100,000 90,491 98,668 94,559 4.49%
the numbers of sale orders that were created. The column
OOP shows the results of the elapsed time in millisec-
onds during the tests, as well as the column NOP (origi-
nal) and NOP (optimized). The last column shows the
percentage difference between NOP Optimized and OOP
Besides, a first version of this experiment concerning
Table 7 was done and presented in [32], where it was
considered just OOP and NOP original versions of the
Sale System. Still, the case with 100,000 sales orders was
not taken into account. The same environment was used
and the tests had almost the same results here presented.
The results in Tables 7 and 8 shows that the system
purely written in OOP obtained a little less execution
time when compared with the system version written
purely in NOP. The scenario created for the first experi-
ment did not allow NOP to explore some OOP problems
(i.e. causal and temporal redundancy).
The code organization developed in OOP was con-
structed in the same way of the pseudo code shown on
the Figure 8. However, in the considered experiment, the
OOP code does not activate the temporal redundancy
shown from line 85 to 97. Thus, this code does not per-
form unnecessary tests and iterations.
Moreover, all the sale orders created are composed of
only one product. Many causal evaluations that verify the
product’s shelf life in a sale order were not fully explored
because all the information contained in those sale orders
were valid (i.e. customer, product, payment form).
5.3. Second Experiments and Results
In the second experiment, there are more evaluations
performed. In this experiment, each sale order has a total
of five products sold. Also, there is the indicator of cus-
tomer’s classification type with the range of 20 types.
This indicator provided a special discount when the sale
order was finalized. This verification was implemented
in the OOP version in a nested way (i.e. If-else-if).
In the Figure 9, it is possible to observe the represen-
tation of temporal redundancy in OOP and how NOP
implements the same code using. With OOP version, it
was possible to create a typical temporal redundancy in
order to compare its impact when compared to NOP. In
Tables 9 and 10, it is presented the results of the second
stage of the experiments.
Besides, a similar version of this experiment concern-
ing Table 9 was done and shortly presented in [34],
where it was considered just NOP Original and NOP
Optimized versions of the Sale System. Still, the case
with 100,000 sales orders was not taken into account.
Almost the same environment was used and the tests had
quite similar results to those here presented.
From the results in Tables 9 and 10, it was possible to
verify results of a scenario with temporal redundancy in
OOP implementation. In this context, for each iteration,
it totalizes a total of 19 unnecessary evaluations.
According to the results presented in Tab les 9 and 10,
in general, the system written in OOP obtained an execu-
tion time slightly better than NOP version. However, the
Table 9 shows that in the Windows version, NOP ob-
tained from 7 to 10 percent better results when performed
10,000 and 100,000 iterations.
5.4. Third Experiment and Results
The Tables 11 and 12 express results of third experiment
where the scenario was changed. The causal evaluations
Table 9. Second test experiment on Windows environment.
Total of sale
order OOP NOP
Performance NOP
over OOP
100 780 936 790 1.22%
1000 80039968 8211 2.59%
10,000 103,688122,72593,943 10.37%
100,000 969,8481,297,993900,251 7.73%
Table 10. Second test experiment on Linux environment.
Total of sale
order OOP NOP
NOP over OOP
100 379 405 391 3.16%
1000 3689 3967 3847 4.28%
10,000 36,83639,268 38,401 4.24%
100,000 367,442393,057 384,161 4.55%
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Notification Oriented and Object Oriented Paradigms Comparison via Sale System 707
Figure 9. Temporal redundancy in OOP and Rules PO N.
Table 11. Third test experiment on Windows environment.
Total of sale
order OOP NOP
NOP over OOP
100 894 1121 967 8.16
1000 9645 12,548 10,587 9.76
10,000 115,252 152,789134,420 16.63
100,000 1166,195 1688,3411214,634 4.15
Table 12. Third test experiment on Linux environment.
Total of sale
order OOP NOP
NOP over OOP
100 390 425 405 3.84%
1000 3899 4212 4036 3.51%
10,000 38,652 42,645 40,486 4.74%
100,000 387,716 430,053405,855 4.67%
were increased in order to have more evaluations during
the execution of experiments. It was entered invalid in-
formation (e.g. customer, product, and payment form).
Also, wrong identification codes cause unnecessary eva-
luations that check the validity of information informed.
In Tables 11 and 12, the results show that the system
developed entirely in NOP obtained similarly runtime
results when compared with the system developed in
OOP. Even though it was expected a better runtime per-
formance of the NOP solution when compared to the
OOP solution. Nevertheless, it is possible to believe in
the NOP approach for other systems with more unneces-
sary evaluations and redundancies than those of the con-
sidered experiment.
Still, something that can be noticed in all the compari-
sons which have been made between Windows and
Linux for the same application is that they presented
quite different execution times. This may be due to dif-
ferences in machine code generated by compilers.
5.5. Considerations
Analyzing the difference between the execution time of
the original and optimized version of NOP against the
OOP version shown in Tables 2 and 3, it is possible to
infer that the overhead of the NOP Framework imple-
mentation did not allow NOP exploring its capabilities.
The framework’s core uses native data structures such
as pointers and linked lists, which have the role of storing
elements that will be notified when it is necessary. With
the increase of system elements, more elements should
be notified and this overhead could generate exhaustive
use of the processing power that will result in no changes
as expected.
All the data from the experiments place, in general,
OOP version in a better position than NOP with slightly
better results. However, these findings are not sufficient
to judge OOP system version as superior when compared
to NOP version. This is due particularly to the fact that
NOP is embodied in the form of a Framework/wizard
built over the C ++ programming language.
The experiments performed can conclude that the de-
velopment of systems designed in NOP is plausible. Al-
though the framework used for the realization of the
paradigm needs some improvements, its current state
allows the development of functional systems.
Copyright © 2012 SciRes. JSEA
Notification Oriented and Object Oriented Paradigms Comparison via Sale System
6. Conclusion and Future Works
This section discusses NOP properties and future works.
6.1. Notification Oriented Paradigm Features
NOP would be an instrument to improve applications’
performance in terms of causal calculation, especially of
complex ones such as those that execute permanently and
need excellent resource use and response time. This
would be possible thanks to the notification mechanism,
which allows an innovative causal-evaluation process
with respect to those of current programming paradigms
The notification mechanism is composed of entities
that collaboratively carry out the inference process by
means of notifications, supposedly providing solutions to
deficiencies of current paradigms [1]. In this context, this
paper addressed the performance subject making some
comparisons of NOP and Imperative Programming in-
6.2. NOP Performance
Previous tests implemented in environments with a con-
siderable number of redundancies show that NOP im-
proves performance compared to other approaches, by
means of its innovative notification mechanism [1,10].
This was expected once temporal and structural redun-
dancies are avoided by NOP, thereby guaranteeing suit-
able performance by definition [4].
In this context, the approach shown in this paper does
not present significant number of redundancies, since its
implementation addresses only one method of the entire
system. Therefore, this approach does not explore the full
potential of the Notification-Oriented Paradigm (NOP).
Furthermore, some optimization of NOP implementa-
tion may provide better results than the current results,
namely in terms of performance. Certainly, these op-
timizations are related to the development of a particular
compiler to solve some drawbacks of the actual imple-
mentation of NOP, such as the overhead of using com-
putationally expensive data-structure over an intermedi-
ary language. These advances are under consideration in
other works.
7. Acknowledgements
R. F. Banaszewski’s M.Sc. thesis [10] was supported by
CAPES Foundation (Brazil), as well as R. F. Banas-
zewski’s Ph.D. thesis and A. F. Ronszcka’s M.Sc. thesis
are under CAPES support.
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