Requirements modeling has been applied in CASE technologies to formalize knowledge needed for constructing models of information systems. The problem is to acquire knowledge from requirements texts and represent it as intermediate requirements model for entity-relationships or object oriented modeling. Proposed approach is based on formalization of entities and their attributes as formal contexts. It is shown that formal contexts created on the set of conceptual graphs extracted from requirements text may serve as data source for requirements models have been applied in real CASE technologies.
In one of early works of John Sowa [
Requirements Modeling has been applied in Requirements Engineering [
A challenging problem in Requirements Modeling is the problem of creating requirements model from natural language text of requirements.
Significant numbers of works in the area of Requirements Modeling have been
devoted to this problem. The most of them treat it as direct mapping text to
CASEmodels and a requirement considered as a text. All such works can be divided into
two sets: one set of works is devoted to derive a family of Entity Relationship models
(plain or extended ERD) from natural language texts ([
In spite of many existing results here including ones oriented on grammars of concrete languages, full automation of CASE-models design from requirements texts is fundamentally impossible. The text of requirements actually contains more or less portion of information needed for creating a CASE-model and textual data could not be mapped exactly to the data of CASE-model.
Therefore the central Requirements Modeling problem needs to be formulated in its natural form – as a problem of creating requirements model from natural language text. This requirements model has to be treated as separate intermediate model between requirements texts and CASE-models.
This paper is based namely on that approach to Requirements Modeling. It is shown that formal contexts created on the set of conceptual graphs extracted from requirements text may serve as data source for requirements models have been applied in real CASE technologies.
The term Conceptual Requirements Modeling is appropriate to denote the fact of
applying Conceptual Structures in Requirements Modeling. Domain of Conceptual
Structures combines conceptual graphs [
Both Entity Relationships and Object Oriented CASE-models use objects and attributes. Attributes belong to entities in ERD and to objects in Object Oriented models (OOM).
An entity is an object from real world having finite set of attributes. Entity name denotes this set of attributes, for instance, Student{Name, Date _ Birth, ...} . One of the crucial principles of Entity Relationship modeling claims that every entity has only generic attributes i.e. attributes which characterize only entity itself. Describe this by the following way. Consider the set of data types T = {D1 , D2 ,..., Dn } consisted of domains . Every domain is ordered set of data of certain type, for example character, numeric, date, etc. The set of attributes A = {ai }, ai ∈ D j is multiset, which contains examples of domain elements. We denote every i - th example of entity as ei = { Ai }, Ai ⊂ A . All examples constitute an entity type
, which attributes are the same for all examples of entities. Then the generic feature of attributes is described by condition: Very often this for Ei ≠ E
j Ai Aj = ∅ demand is not
(1) met in practice.
For
example:
E1 = Student{Name, Date _ Birth, ...} and E2 = Teacher{Name, Date _ Birth, ...}have
similar subsets of attributes. To hold the condition (1) in CASE-technology one must
rename attributes of entity in the example above. Note that the mapping sets of
attributes to entities (or vice versa) could not be rigorously formalized as a function
it is a relation. An appropriate way of expressing it is formal context [
will be completely filled by units Sub matrices
may represent formal concepts [
Formal concept on the context ( E , A , R) is a pair of subsets
X ⊆ E , Y ⊆ A
together with pair of mappings ϕ : E → A , ψ : A → E realizing so called Galois
connection [
Considered conceptual structures – formal context and formal concepts – may serve as an instrument for constructing requirements models. They unite objects and attributes by relations and have important property of completeness: as formal context as formal concepts are complete objects with certain informational content extracted from the text of requirements. Apparently this content must be represented in CASEmodels so the context and formal concepts constitute a kind of requirements. They may be considered as Conceptual Requirements Model.
To extract objects and their attributes from requirements text, the approaches
mentioned in the Introduction section may be applied. Conceptual graphs are
appropriate for it due to the following reasons:
• if successfully acquired from text, conceptual graphs represent compact
model for discovering objects and their attributes - there may be a set of
conceptual relations in a graph which depict connection between objects
and their attributes;
• conceptual graphs naturally belong to Formal Concept Analysis paradigm
and have been successfully applied for constructing formal contexts [
We use our software for conceptual graphs acquisition from natural language texts
[
Conceptual graphs are acquired from subtitles of requirements text and from text
sections. It is interesting to find similarities between graphs acquired from subtitles
and graphs acquired from text sections since some terms (objects) declared in a
subtitle may be mentioned and concretized in a section text. We apply measures of
similarity of conceptual graphs which we used in our experiments of conceptual
graphs clustering [
All acquired correct conceptual graphs are processed to extract objects and their attributes. The way of extraction is based on fixing certain set of conceptual relations presented in derived graph. There are trivial and non trivial patterns of concepts and corresponding relations which may exist in a graph. If standardized text of requirements is a source for graph acquisition then it is possible to create special templates for graph acquisition algorithm.
Conceptual graph represents semantics of only one sentence. Important information about objects and their attributes may be presented in several various sentences. To collect it we use formal context. A context with associated entities having blockdiagonal structure (2) contains the needed information.
Formal context is created as [
Sybase PowerDesigner CASE technology supports requirements modeling with
natural language texts as its input. Figure 2 illustrates the principle of requirements
modeling in PowerDesigner [
As it is shown on the Figure 2 PowerDesigner processes formatted MS Word textual documents. Requirements model on the Figure 2 is consisted of two elements: requirements which are title and headings of sections and subsections of the document and traceability matrices which represent various connections between requirements and between requirements and elements of created CASE-models. Title and headings are treated as elements of requirements model. The text between two headings is treated as the requirements object’s comment. Detecting requirements as title and headings of sections and subsections of a text is the only current function of natural language processing in PowerDesigner. Having such requirements, user manually applies them in constructing CASE-models by setting entities and relationships, objects and their attributes: type, status, priority, risk. Using traceability matrices user creates tools for checking connections between various objects of models.
Additional functionality to this technology is caused by the fact that requirements text between two headings has been also processed to extract objects and attributes united by formal context. It takes place by the following.
1. Conceptual graphs are acquired from the whole text of requirements. Fixed set of conceptual relations (for example genitive and attribute relations) in conceptual graphs is applied to select candidate pairs of objects - attributes to form a context. 2. Context matrix is formed so that only objects with more than one attribute have been included in the matrix. This is the way to select objects significant for constructing CASE-models. 3. Initial sparse context matrix is transformed by using linear algebra methods for block-diagonal decomposition. The problem of keeping order in the sets which form context is actual here. Associated entities may be ordered by hierarchy relation, for example as Student, Teacher, Dean on Figure 1. As usual attributes are less ordered and can be permuted for block-diagonal decomposition. 4. Interaction with user by special interface and visualization is very important since the process of creating CASE-models still remains closely depended on developer’s skill. In the current experimental version of the system there is user interface to show every subset of object and its attributes obtained from conceptual graphs. User can correct this set.
Figure 3 illustrates this technology on the fragment of CyberFridge project included in PowerDesigner as an example of requirements modelling. On the figure we combined two windows: on the top of the figure there is PowerDesigner interface window showing how requirements are represented, below there is interface window of our system visualizing conceptual graph corresponded to the first sentence highlighted in the top window. Only attribute relation was processed here and candidate pairs of objects – attributes are shown. Later, analyzing other graphs and the context created on the whole set of candidate pairs we keep only refrigerator and cyberfridge entities as requirements. .
On the standard way of creating requirements model in PowerDesigner user takes only headlines of requirements text (“Project Description of Target System” on Figure 3) as requirements objects and treats remaining text as comments. Conceptual Requirements Modeling System extends functionality of requirements modeling in PowerDesigner realizing more complete text processing.
The first version of the system of Conceptual Requirements Modeling was tested
on various Russian texts of requirements being structured according to the standard
[
First results obtained from experiments demonstrate the following.
1. Conceptual graphs are valid for extracting objects and attributes from natural language texts of requirements and can deliver specific new information for CASEmodels developer.
2. Formal context serves as a tool for collecting entities and their attributes for Entity Relationship and Object Oriented Modeling and selects objects significant for constructing CASE-models.
The way of developing proposed approach is mostly experimental and its final effectiveness can be confirmed after series of additional experiments and corresponded changes in the algorithm.
Future work is planned in the following directions.
1. Extending the set of relations which is applied to select candidate pairs of objects - attributes to form a context. For example the goal relation on Figure 3 is also informative as attribute relation. 2. Discovering the way of implementing formal concepts on formal context in the approach. Specifically, if formal concepts exist on the context do they form additional objects significant for CASE modeling? 3. Deep integration proposed approach with CASE technology, particularly with Sybase PowerDesigner.
We also plan to expand the set of our controllable grammatical templates by including English language grammar to it.