GOOAL AUTOMATIC DESIGN TOOL
A Role Posets based Tool to Produce Object Models from Problem Descriptions
Hector G. Perez-Gonzalez, Sandra Nava-Muñoz, Alberto Nuñez-Varela
Facultad de Ingenieria,Universidad Autonoma San Luis Potosi, Dr. Manuel Nava # 8, San Luis Potosi, Mexico
Jugal Kalita
University of colorado at Colorado Springs, 1420 Auston Bluffs Pkwy, Colorado Springs, CO, U.S.A.
Keywords: Object Oriented Analysis, Object Oriented Design, Software Engineering Education, Natural language
Processing.
Abstract: A number of software analysts may produce different, perhaps all of them correct, solutions from one
specific software requirement document. This is because natural language understanding is complex and
because each analyst has distinct design experience. A methodology and approach that can be automated
and that uses a proposed semi-natural language called 4WL used to accelerate the production of reliable
accords between different stakeholders. The supporting software tool called GOOAL, Graphic Object
Oriented Analysis Laboratory automatically produces simple object models (UML diagrams) from English
or Spanish statements with minimal user participation. These statements, faithfully describe the original
problem description sentences. The models are generated analyzing each sentence of the intermediate 4W
language version of the original sentence set. With this methodology and supporting software tool, students
of Object Oriented technology can visualize the design decisions being made by the system. This
methodology and software tool has been used to support the learning process in object Oriented analysis and
design courses. The original tool was developed to “understand” English and it was validated with design
artefacts produced by several experts of the University of Colorado. The main results reported by the
students, are related with the use of good design practices, a better understanding of UML language and a
major interest in the pre programming process. Its technical contribution is the role posets technique.
1 INTRODUCTION
Although Natural Language (NL) processing is a
very complex task (Allen 1995; McDonald 1992), it
is possible to extract sufficient meaning from NL
sentences to produce reliable models. This research
proposes the use of a tool-supported methodology
that helps in object oriented design (OOD) from
English or Spanish problem sentences.
Our goal is to take a problem description given
from the problem domain experts such as the one
given below and produce the UML (Booch 1997)
models for it. The following sentences describe a
problem known as the dining philosophers and the
second describe the towers of Hanoi problem both as
shown in Rumbaugh’s book (Rumbaugh et al. 1996).
Problem # 1: English Version
There are five philosophers and five forks around a
circular table.
Each philosopher can take two forks on either side
of him.
Each fork may be either on the table or used by one
philosopher.
A philosopher must take two forks to eat.
Problem # 2: English Version
The towers of Hanoi is a game. There is a player
that moves a stack of disks from one of three long
pegs.
The player uses the third peg for manoeuvring.
Each disk has a different size.
Disk may be moved from the top of a stack on a
peg to the top of the stack on other peg.
200
G. Perez-Gonzalez H., Nava-Muñoz S., Nuñez-Varela A. and Kalita J. (2008).
GOOAL AUTOMATIC DESIGN TOOL - A Role Posets based Tool to Produce Object Models from Problem Descriptions.
In Proceedings of the Third International Conference on Software and Data Technologies - SE/GSDCA/MUSE, pages 200-205
DOI: 10.5220/0001887002000205
Copyright
c
 SciTePress

A disk is placed on another disk that is bigger
than itself.
The problem description sentences in the
examples have four and six English sentences
respectively. We use the first as a running example
in this paper. The verb forms used in the first
example are are, can take, may be, used, must take
and eat. The nouns are philosopher(s), table, fork(s)
and side. The classes corresponded to the first
example that could be accepted for modellers to
construct their class diagram are: Application that
works as main class, Philosopher and Fork. Each
class abstracts the knowledge and behaviour of its
corresponding real counterpart. Our challenge in the
research reported in this paper is to obtain
descriptive UML diagrams from NL general
sentences using a systematic methodology and
minimal user participation
2 RELATED WORK
An early paper by Abbot (1983) presented a
systematic procedure to produce design models from
NL sentences. This approach produces static or
structural models obtained with high user
participation. Saeki, Horai and Enemoto (Saeki et al.
1989) presented a process to derive incrementally a
formal specification from natural language. They
used simple “verb patterns” to identify linguistic
subjects and objects. The user interactively decided
which nouns became classes and which verbs
became methods.
Cockburn (1992) presented a detailed analysis
where he related relational nouns with objects,
adverbs with polymorphism and the roles of objects.
In a high level abstraction, Obsborne and MacNish
(1996) eliminated ambiguity in NL requirements by
the use of a Controlled language (CL) called
Newspeak. Da Silva (1996) suggested an object
oriented model and presented a formal diagrammatic
notation (Object-Z) and a set of rules to transform
semiformal sentences into formal. Burg and Van de
Riet (1996) tried to minimize the participation of the
user in the job of class and relationships extraction
from the text. They used a very large lexicon to aid
in semantic model validation and provide a new
modelling language to illustrate results.
Hars and Marchewka (1997) presented an effort
to produce dynamic analysis using a dictionary of
about 23,000 single root words. Each word belonged
to a concept category such as event, person, and
location. The dictionary also contained word
frequency information computed from a year’s
volume of Time magazine. The process transformed
the textual sentences into an internal representation,
and constructed a tree structure. A separate
algorithm resolved compound words such as chicken
nuggets. This technique asked the user to resolve
ambiguities not just at syntactic level but at semantic
level also. Boyd (1999) discussed the linguistic
metaphors in software design and analyzed
prepositions, articles and interjections in addition to
nouns and verbs. He used a process of syntactic
normalization to produce a more precise sentence
through two manual steps: syntax normalization and
semantic exploration.
Borstler, Cordes and Carver (Borstler el al. 1992)
presented a prototype that accepted well formed NL
textual use cases and produces, with minimal user
participation, UML static diagrams: classes, objects
and simple relationships. A valuable feature of their
work is the final traceability supported by hypertext
technology. Overmyer, Lavoie and Rambow
(Overmyer et al. 2001) presented a complete
interactive methodology and a prototype tool that
produced a subset of UML. However, the text
analysis remained in good part a manual process.
We presented our seminal work at OOPSLA
2002 (Pérez-González & Kalita 2002) showing our
initial results with just two simple examples and
using just an English version tool. At OOPSLA 2005
(Pérez-González et al. 2005) we showed our Spanish
version tool with an educational approach. Polajnar
(Polajnar et al. 2006) presented CLIE (controlled
language for information extraction) based in
predicate logic. Zapata (Zapata et al. 2006)
presented UN-Lencep a controlled language using
the notion of pre-conceptual schema (Zapata et al.
2006b). They proposed a simple framework used by
their automatic tool to produce classes,
communication and state machine diagrams.
Our methodology accelerates the early software
development process through the use of a software
tool. The supporting software tool called Graphic
Object Oriented Analysis Laboratory produces an
intermediate representation of the original Natural
Language sentences describing customers’ problem
description sentences. It then analyzes them and
proposes object oriented (OO) models (Booch 1997)
thorough an iterative process. The value of our
methodology consist of the use of a systematic
process, and the value of our tool and its underlying
techniques is the production of diagrams non just
from a controlled language but from a free version
of the problem sentences. That can be achieved
because of the iterative participation of stakeholders
even thinking that those interventions are not
intensive. Particular difference of our techniques is
the prediction of the probabilities every element has
to become a class depending on the discourse.
GOOAL AUTOMATIC DESIGN TOOL - A Role Posets based Tool to Produce Object Models from Problem
Descriptions
201

3 METHODOLOGY
In order to build successful software products in a
professional Software Engineering environment we
have to define what success is. A software
development project or product is considered
successful if it meets the solicitor’s requirements
respecting the original schedule and budget.
The single biggest cause of software project
failure is deficient requirements definition (Hofman
& Lener 2001); specifically, inconsistencies and
misunderstandings in the early stage of the process
produce unpredictable consequences. The other
important success factor is software architecture and
design decisions. Good design decisions lead to a
more maintainable product. Improving requirement
definitions and software design practices will allow
high-quality Software Engineering to become more
to a reality. This section presents a methodology to
maximize software reliability through automatic
modelling techniques from natural language problem
statements. This tool-supported methodology aims
to facilitate stakeholders’ communication by
reducing early misunderstandings and promoting
good design practices using the supporting tool for
software design practice.
The analysis/design step generally takes a
problem statement written in English, follows an
object oriented methodology and produces a set of
UML diagrams representing the proposed solution.
A number n of software (SW) analysts may
produce n different (perhaps all of them correct)
solutions from one specific SW requirement
document. This happens because Natural Language
(NL) understanding involves syntactic, semantic and
pragmatic issues, (different backgrounds in persons
produce different interpretations) and because of
distinct design experiences.
We are proposing a methodology with a
technique called role posets (Pérez-González &
Kalita 2002) that will enable the early modelling of
SW to be automated and a semi-natural Language
called 4WL (Pérez-González & Kalita 2002) that are
used as the main vehicles of the methodology to
accelerate the production of reliable accords
between different stakeholders. Figure 1 shows this
process.
The supporting software tool (GOOAL: Graphic
Object Oriented Analysis Laboratory) automatically
produces object models from a natural language
(English or Spanish) statements describing software
requirements. Those models are generated by
analyzing sentence-by-sentence an intermediate
language (4W) version of the original sentence set.
3.1 4W Language
A 4WL sentence is composed of four general parts
(Pérez-González & Kalita 2002): (W parts). They try
to respond the questions: Who is the semantic
subject?, What is happening?, Who is the receiver of
the action? and Who is the indirect semantic object?.
The subject is mandatory in every 4WL sentence.
The last two parts are optional. Every 4WL sentence
has an object that corresponds to the action referred
to in the sentence. In linguistic terms, this is the
noun of the subject. The presence of this part is
mandatory in every English sentence we can
process, and in a 4WL sentence also. From the OO
point of view, this object cold be an instance of
some class. When we use the term noun in this
paper, in addition to a single noun, it could be a
group of nouns (chicken nuggets) or a sequence of
adjectives and a final noun (small blue birds).
EXAMPLE: DINNING PHILOSOPHERS
The five dinning philosophers’ problem (Hofman
& Lener 2001) is used here to illustrate the use of
4WL language. The English statements of the
problem are given in the beginning of the paper and
are repeated below. English sentences are
automatically translated to 4WL. The English
sentences in the specification are shown first in bold.
The 4WL sentence(s) that correspond(s) to a single
English sentence follow(s) in italics. Note that one
English sentence can result in one or more 4WL
sentences.
There are Five Philosophers and Five Forks
around a Circular Table.
1.-Five forks are around a circular table.
2.-Five philosophers are around a circular table.
Each Philosopher can take Two Forks on either
Side of Him.
3.-Each philosopher can take two forks on the side
of each philosopher.
4.-A philosopher has side.
Each Fork may be either on the Table or used by
One Philosopher.
5.-Each fork may be on the table.
6.-One philosopher may use each fork.
A Philosopher must take Two Forks to Eat.
7.-A philosopher must take two forks.
8.-<When preceding sentence> a philosopher eats.
The Software Tool called GOOAL developed to
support this work automatically translates from
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English to 4WL with little user participation The 4W
grammar is not detailed here.
4 ROLE POSETS
We use the notion of partially ordered set of roles
(Role Posets) (Pérez-González & Kalita 2002) to
emulate the reasoning analysts perform when they
model a problem. Software models are the product
of a sequence of decisions taken. Different
modellers can produce different models for a given
problem depending on the decisions they make. The
decisions can be influenced by different factors such
as the design priorities and previous experience of
the modeller, the nature of the problem itself and the
priorities and expectations of the consumer. Role
Posets are based on the mathematical concept of
poset (Partially ordered set) and the thematic (theta)
roles due to Chomsky (Chomsky 1965) and are used
widely in many linguistics formalisms. We use Role
Posets to construct the structures to model a
problem. The decision that makes a noun becomes a
class depends on the analysis of the complete
problem, the potential future additions to the system
and the perception, experience and motivation of the
analyst. Some automatic methodologies (Borstler el
al. 1992, see also Mich & Garigliano 1997) make
class identification using the frequencies of all the
nouns in the requirements text. We propose a
heuristic algorithm to identify classes considering
also the roles that the nouns play in every sentence:
The probability that a noun becomes a class
varies proportionally to its importance or value in a
text:
probToClass(NounX) = value
t
(NounX) (1)
The importance of a noun in a text is computed
as the sum of the importance of that noun in every
sentence. We write it as:
value
t
(NounX) = ∑
i=m
i=1
value
i
(NounX)
(2)
Where m is the number of sentences in the text
and i is the index of the particular sentence.
The importance of a noun in a sentence S
depends on the role it plays in this sentence. We
write it as:
value
i
(NounX) = role
i
(NounX)
(3)
Where i is the index of the particular sentence.
According to Chomsky (Chomsky 1965): “The
role a noun plays in a sentence depends on the
relative position it has in the sentence and on the
semantics of the main verb of that sentence”. We
express Chomsky’s statement as:
role
i
(NounX)=pos(NounX)
i
+semantics (V)
I
(4)
Where i is the index of the particular sentence.
According to Haegeman (1991) “There is no
agreement about how many such specific thematic
roles (Θ Theory) there are and what their labels are.
Some types are quite generally distinguished”. The
selection of the roles is frequently a semantic
intuition that is difficult to automate. Some types are
quite generally distinguished”. According to
Chomsky’s Government and Binding theory there
are restrictions called the Theta Criterion on the Θ
roles: “Each Argument is assigned one and only one
theta role. Each theta role is assigned to one and
only one argument”. Of course, we follow
Chomsky’s ideas as just guidance in formulating our
algorithms. Details of calculating the significance of
roles for a specific piece of requirements text are not
discussed here. Once the potential classes have been
analyzed, their calculated probabilities are used to
decide which ones deserve the class category. All of
this are based on our proposed role machines and
verb families (Pérez-González et al. 2005) explained
below. We propose a universal partially ordered set
of roles composed by: Agent (Ag), User (Ur),
Modified (Mr), Used (Ud), Whole (Wh), Part (Pt),
General (Gl), Special (Sp), Theme, LSP (Location,
situation or position) and attribute (At).
Our prototype tool internally labels every noun
in the text with the particular role it plays according
to its associated verb and its relation with it.
At the end of this automated analysis, there is a
list of nouns (and adjectivally qualified nouns),
every one of them associated with a list of roles it
plays.
4.1 Results and Evaluation
In our example we have the following nouns:
Na=Philosopher, Nb=Fork, Nc=Table, Nd=Side.
Table 1 shows results of the automatic generation of
roles.
Table 1: Roles results for the five Philosophers problem.
Na = {User, User, Whole, Theme}
Nb = {Used, Used, Theme, Theme}
Nc = {LSP, LSP}
Nd = {Attribute, LSP}
From this, the universal role poset is
reconstructed:
{Agent ( ), User (N1,N1), Modified ( ),
Used (N2,N2), Whole (N1), Part (), General ( ), Special (
), Theme (N1,N2,N2,), LSP (N3,N3,N4), Attribute (N4) }.
The reason because N1, for example is
associated with that particular list of roles, involves
the use of a proposed state machine we called Role
Machine.
GOOAL AUTOMATIC DESIGN TOOL - A Role Posets based Tool to Produce Object Models from Problem
Descriptions
203

We have designed a role machine for every one
of a group of verb families.
We have identified 13 semantic families of
verbs. Figure 1 shows the role machine
corresponding to the be verb family with the slots
being occupied by the words of our first 4W
sentence: The machine shows the Noun fork (Nb)
playing the role of Theme (Th) two times because
the state the sentence follows denotes the state Th-
meaning that the noun in the left side of the verb is a
theme The verb take used in the second sentence
belongs to the di-transitive modifier family of verbs.
Figure 1: Role Machine for the be verb family using the
sentence: Five forks are around a circular table.
We don’t present more details of this technique
due to lack space. After following the technique, we
obtain the probabilities every noun (N) has to
become a Class. Results of our example are shown
in table 2.
Table 2: Result of analysis of nouns. Five Philosophers
problem.
Noun Noun´s name Probability to be a class
Na Philosopher 100%
Nb Fork 61%
Nc Table 15%
Nd Side 5%
Figure 2 shows a simple class diagram obtained
from our example. The vertical axis shows the
probability a noun element has to become a class.
Figure 2: Final class diagram. Five Philosophers problem.
The tool can produce general static and dynamic
UML diagrams. These diagrams and the 4W
sentences may be used to detect and solve
ambiguity.Class, These diagrams are based on the
analysis of every one of the 4WL sentences.
5 EVALUATION
In order to prove the results obtained by the tool-
supported methodology presented in this paper, we
tested 82 undergraduate software engineering
students. Each student was asked to evaluate both
problems presented in this paper (philosophers and
Towers of Hanoi). The students followed our
methodology to identify the nouns in the text and
assign them a probability that the noun or noun
construction will become a class according to his
level of knowledge and experience.
Table 3 shows
the test results for the Philosophers problem.
Table 3: Philosphers test results.
Students GOOAL
Philosopher 92,10% 100,00%
Fork 75,52% 64,00%
Side 23,42% 15,00%
Table 35,28% 2,00%
Table 4 shows the test results for the Hanoi
problem.
Table 4: Towers of Hanoi test results.
Students GOOAL
Player 83,91% 100,00%
Disk 70,65% 99,00%
Stack 70,21% 67,00%
Top 28,52% 24,00%
Collection 25,00% 10,00%
Size 17,78% 10,00%
Note: the students’ column is the students’ average.
As we can see in the tables, all decisions taken
by the systems are closely related with the ones
taken by the students following the methodology
The results are very promising and we can say that
the tool will help in the use of good design practices
and a better understanding of UML language.
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6 CONCLUSIONS AND FURTHER
WORK
The present paper has described a tool that supports
a methodology that aims to accelerate the production
of analysis and early design models. This
methodology is based on semantic abstraction of
linguistic elements and involves the use of the
semantic role theory and a semi-natural language.
The prototype tools have produced good results with
problems described in no more than eight sentences
and 100 words on an average. Observed advantages
of the use of this tool are formalization, standard
notation, validation, traceability, efficiency and early
identification of misunderstood requirements.
Individuals using it, will see how a group of
sentences describing a problem are handled by
GOOAL. The system takes decisions with minimal
user participation, shows its interpretation in 4WL
and produces model views of the problem. Unique
features of this tool are the underlying methodology
and the production of dynamic models. Although the
tool, methodology and techniques expounded here
are far from perfection, results using simple
sentences are promising. Better results are expected
if a more complete general dictionary is used as well
as finer refinements in semantic classification of
verbs into families. With the comparison of results
between both GOOAL tool and the tests we are
running with the students, it can be conclude that we
can use this program as an educative tool.
REFERENCES
Abbott, R.,1983. Program Design by informal English
Descriptions. In Communications of the ACM, 26(11).
Allen, J.,1995. Natural Language Understanding,
Benjamin/Cummins Publishing Co.
Booch, G.,1997. The unified Modeling Language User
guide. Addison-Wesley.
Borstler, Jurgen, Cordes, Carver. 1992. An Object-Based
Requirements Modeling Method. Journal of the American
Society for Inf. Science 43(1):62-71.
Boyd, N., 1999. Using Natural Language in Software
Development. Journal Of Object Oriented Progr.
Burg, J., Van De Riet, R., 1996. Analyzing Informal
Requirements Specifications: A first Step towards
conceptual modeling. Proceedings of the 2
th
International workshop on applications of natural
language to information systems, Amsterdam, The
Netherlands, IOS Press.
Chomsky, N., 1965. Aspect of the theory of syntax. MIT Pr
Cockburn, A., 1992. Using Natural Language as a
metaphorical Basis for Object Oriented Modeling and
Programming. In IBM Technical Report TR-36.0002.
Da Silva, J.,1996. Metamorphosis: An Integrated Object
Oriented Requirements Analysis and Specification.
Lancaster University.
Haegeman, L., 1991. Introduction to government and
binding theory. Wiley.
Hars, A., Marchewka, J.,1997. The Application of Natural
Language Proc. requirements Analysis. Journal of
Management Information Systems.
Hofmann, H., Lener, F.,2001. Requirements Engineering
as a Success Factor in Software Projects. IEEE Software,
(pp 58)
McDonald, D.,1992. Robust Partial-Parsing Through
Incremental Multi- Algorithm Processing. In Lawrence,
E., Text Based intelligent systems. (pp 83-100).
Associates Publishers.
Mich, L., Garigliano, R., 1997. NL-OOPS A Tool for
Object Oriented Requirements Analysis. In The LOLITA
Project: The First Ten Years, Vol.2 Applications,
Springer-Verlag.
Osborne, M., MacNish, K., 1996. Processing Natural
Language Software Requirement Specifications IEEE
Computer Society DL. (pp 229-237).
Overmyer, S., Lavoie, V., Rambow, O., 2001. Conceptual
Modeling through Linguistics Analysis Using LIDA. 23rd
International Conference on Software engineering.
Pérez-González, H., Kalita, J., 2002. Automatically
Generating Object Models from Natural Language
Analysis, Companion OOPSLA 2002.
Pérez-González, H., Kalita, J., Nunez-Varela A. Wiener,
R., 2005. GOOAL: An educational Object Oriented
analysis Laboratory, Companion OOPSLA 2005.
Polajnar, T., Cunningham, H., Tablan, V., Bontcheva,
K.,2006. Controlled language IE Components Version 1.
EU-IST Integrated Project (IP) IST-2003-506826
SEKT, D2.2.1 Report Sheffield.
Rumbaugh, J., Blaha, M., Lorensen, W., Eddy, F.,
Premerlani, W. ,1996. Object Oriented Modeling and
Design. Prentice Hall.
Saeki, Horai, M., Enemoto, H.,1989. Software
Development Process from Natural Language
Specification. In Proceedings of the 11
th
International
conference on SW Engineering IEEE, Computer
Society Press.
Zapata, C., Gelbukh, A., Arango, F.,2006. UN-Lencep
Obtencion Automatica de Diagramas UML a partir de un
leguaje controlado. Avances en ciencias de la
computación VII Encuentro Internacional de
Computacion ENC 2006, ISBN 968-5733-06-6.
Zapata, C., Gelbukh, A., Arango, F., 2006. Pre-conceptual
schema: a UML isomorphism for automatically Obtaining
UML Conceptual Schemas. Research in computing
Science: Advances in Computer Science and
Engineering. Vol 19, (pp 3-13).
GOOAL AUTOMATIC DESIGN TOOL - A Role Posets based Tool to Produce Object Models from Problem
Descriptions
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