NL to OCL via SBVR

School of Computer ScienceUniversity of Birmingham

UK

Translation of Natural Language Specification to OCL

A PhD Research Work PresentationA PhD Research Work Presentation

RSMG 2RSMG 2

Imran Sarwar BajwaPhD Student

Natural Language Processing [email protected]

29 | April | 2010 2 | 16Natural Language Processing Group

School of Computer ScienceUniversity of Birmingham, UK

Presentation Foot Steps

1. Introduction

1.1 Motivation

1.2 Basic Concepts

1.3 Research Problem

1.4 Research Objectives

2. Concluded Work

2.1 State of the Art

2.2 Proposed Solution

3. Conclusions



1.1 Motivation

1.1 Motivation Object Constraint Language (OCL) is used to annotate Unified Modeling Language (UML)

models with constraints.

But OCL is the least used language among the 13 UML languages.

OCL is difficult to write due to its hard syntax.

Creating OCL code is difficult and that results in less

adaptability of OCL

Constraints specification is easy to write in natural language e.g. English

A Natural language interface can be useful in automatic creation of OCL



1.2 Background

Natural Language (NL)Natural Language (NL)

Natural languages (NL) e.g. English, Arabic, French, etc are basic source of communication for humans.

Computational processing of NL is a long standing challenge.

Basic steps involved in translation of Natural Language Text are following

NL Text(Source String)

Lexical Analysis

(Tokenizing)

g

Syntactic Analysis(Parsing)

Semantic Analysis(Meaning

Extraction)

Output(Target String)

Translation

Basic steps of Natural language Translation



1.2 Background

Object Constraint LanguageObject Constraint Language

An OCL constraint defines a restriction on state or behaviour of an entity in a UML model.

a. Invariants: The invariants are conditions those have to be TRUE always.

context driver

inv: self.age >= 18

b. Precondition: This constraint that should be TRUE always before the execution of a method starts.

context driver :: isAdult(dDOB: Integer): Booleanpre: dDOB >= 1990

b. Postcondition: This constraint should be TRUE always after the execution of a method has finished.

context driver :: isAdult(dDOB: Integer): Boolean

post: result >= 18



1.2 Background

context Flight

inv self.maxNrPassengers <= 500

Airport

Flight

*

*

departTime: TimearrivalTime: Timeduration : IntervalmaxNrPassengers: Integer

origin

desti-nation

name: String

arrivingFlights

departingFlights

1

1

Flight Capacity Constraint:Flight Capacity Constraint: The maximum number of passengers that can be on a

flight must be less than or equal to 500.

OCL Simple ExampleOCL Simple Example



1.2 Background

context Department

inv MaxSalary: Department.allInstances() -> forAll(d|not d.employee -> select(e|e.age<25) -> exists(e|e.salary > d.maxJuniorSal)).

Junior Employee Salary Constraint:Junior Employee Salary Constraint: The maximum salary of a junior employee

those with an age lower than 25 can not earn more than the maximum junior salary value

defined for their department.

OCL Hard ExampleOCL Hard Example



1.2 Background

Junior Employee Salary Constraint:Junior Employee Salary Constraint: In the enroll() operation of LoyaltyProgram, the

Postcondition includes that there is no one more customer than before and that the new

customer’s loyalty account has no points and no transactions.

OCL Very Hard ExampleOCL Very Hard Example

context LoyaltyProgram::enroll(int c:Customer)

inv pre: not customer -> includes(c)

post: customer = customer @ pre -> including(c)

post: membership -> select(customer=c) -> forAll(

loyaltyAccount -> notEmpty() and

loyaltyAccount.points = 0 and

loyaltyAccount.transations -> isEmpty)



1.2 Background

Context LineFinder::rows : Set(Set(Integer))

derive: Set{ Set{0, 3, 6}, Set{1, 4, 7}, Set{2, 5, 8}, Set{0, 1, 2}, Set{3, 4, 5}, Set{6, 7, 8}, Set{0, 4, 8}, Set{6, 4, 2}}

Context LineFinder::findWinningLines(board:Sequence(Piece)) :

Set(Line)

body: let winners : Set(Set(Integer)) = LineFinder::rows -> select(row | row -> forAll(a, b : Integer | board -> at(a) = board -> at(b) and board -> at(a) <> Piece::Empty) ) in winners -> iterate(row : Set(Integer); lines : Set(Line) = Set{} | lines -> including(LineFinder::newLine(board -> at(row -> any(true)), row)))

Another OCL very Hard ExampleAnother OCL very Hard Example

TIC-TAC-TOE Winning Line Constraint:TIC-TAC-TOE Winning Line Constraint: Finds all winning lines where it's possible

to have more than one line, but if the other rules are enforced and all winning lines

should be for the same piece.



1.2 Background

Semantic Business Vocabulary and Rules (SBVR)Semantic Business Vocabulary and Rules (SBVR)

SBVR has two components

SBVR vocabulary

SBVR vocabulary is made up of two elements: concepts and fact types.

- A concept can be a noun Concept or individual Concept.

- A fact type is a verb that specifies the relationship among different concepts in a business rule.

SBVR rules

SBVR rules are of two types: Definitional Rules and Behavioural Rules.

a. Definitional Rules:

Definitional Rules or structural rules are used to define an organization’s setup e.g . It is necessary that each customer has at least one bank account.

b. Behavioural Rules:

Behavioural Rules or operative rules express the conduct of an entity e.g. It is obligatory that each customer

can withdraw at most GBP 200 per day.



1.3 Problem Description

1.3. Problem Description

OCL is hard to write due to a number of reasons. Major reasons are OCL syntax is difficult to grasp.

Manually creation of OCL code results in incorrect and erroneous OCL constraints.

So many OCL tools but they provide only type-checking facilities.

OCL is not adaptable in its current interface.

English is difficult to process due to following reasons

English is vast and has no domain boundaries

English sentence structure is ambiguous

Most English words have multiple senses

A single meaning can be represented in multiple ways



1.4 Research Objectives

1.4. Objectives of Research

Major objectives of the research are

To make OCL more adoptable through translation from natural languages.

Overcoming limitations of natural languages with the help of SBVR

Presenting a framework that can be used to automatically generate OCL code from

natural language specification .

Creating a tool for creating OCL from NL by implementing the proposed framework.



2.1 Related Work

2.1 State of the Art

NL Specification to Object Oriented Analysis (Moreno, 1997, Kop, 2002; Lee,

2002; Perez-Gonzalez, 2002; Mich, 1996; Li, 2004; Mala, 2006)

NL Specification to UML Diagrams (Bryant, 2003; Harmain, 2003; Clavel, 2007;

Seco, 2004; Oliviera, 2004; Ilieva, 2005; Whittle, 2009; Kovacs, 2009)

No work done for NL to OCL translation

SBVR based Controlled Natural Languages (Kliener, 2009; Silvie, 2009;

Spreeuwenburg, 2009)

SBVR specification to UML model (Raj, 2008)



2.2 Proposed Solution

Natural Language Specifications

Structured English based SBVR

SBVR to OCL Translation

Mapping NL to SBVR Rules

Validated OCL code

Designer

UML Model

SBVR Rules

OCL Expression

Application Scenario of Natural language to OCL transformation

context driverinv: self.age >= 18

It is necessary that each driver must be at least 18 years old.

A driver should be 18 years old.



3. Conclusion

Finally!!

OCL can be made more adaptable and

usable by providing a natural language e.g.

English based user interface.



Questions

?

NL to OCL via SBVR

Education

Transcript of NL to OCL via SBVR