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A Formal Descriptive Semantics of UML and Its Applications
Hong ZhuDepartment of Computing and ElectronicsSchool of Technology
Acknowledgement
The work reported here is based on the outcomes of the collaborative research with Dr. Ian Bayley, Oxford Brookes University, UK Ms. Lijun Shan, National University of Defense
Technology, China (visitor to Oxford Brookes University funded by China Scholarship Council)
Mr. Richard Amphlett, Oxford Brookes University, UK (Funded by Reinvention Centre as Undergraduate Research Student Scholarship)
Outline
What is descriptive semantics? The concept and motivation
How to specify descriptive semantics? The formal framework Formal definition of mappings from diagrams to first order
logic (FOPL) Application to UML class, interaction and state machine
diagrams The relationships between descriptive semantics and
functional semantics Why descriptive semantics are important and useful?
Implementation of the semantics and the tool LANBDES Applications in formal reasoning about models in FOPL
What is the meaning of UML diagrams? The system has two classes called
Member and Book, (There are two types of objects called members and books)
There is an association between them, which is called Borrow (Members can borrow books)
The upper limit of borrowing is 10 (Each member can only borrow up to 10 books at any time)
The upper limit for a book to be borrowed is 1 (Each book can only be borrowed by at most 1 member at any time)
Member
Book
Borrow
Library system
0..10
0..1
Instances of the class Member
Instances of the class Book
Semantics of modelling languages
‘A model is a set of statement about some system under study’ [Seidwitz, 2003]
The semantics of a modelling language is the satisfaction relationship between a model and a system.
s m
Software model, and models in all scientific disciplines
A system in the subject domain D, i.e. universe of systems that the language is modelling.
A well-formed model in the modelling language.
s is an instance of the model m, i.e. s satisfies the statements of the model m
Two types of semantics
Descriptive semantics Describe the system based on
a set of basic concepts, such as class, association multiplicity upper bound, etc.
Example: The system has two classes
called Member and Book
Functional semantics Define how the system funct
ions at run time. Example:
There are two types of objects called members and books.
Existing work on UML semantics often occurs as a combination of them.
Example: Each instance of the Book class can only be borrowed by at most 1 member at any time.
Difficulties in the formalisation of UML Interpretation in different subject domains:
One model can be interpreted in several different subject domains Library system model:
• When interpreted on the subject domain of real world systems, the library of Oxford Brookes University is an instance
• When interpreted on the computer software, …
Extension with new concepts: New basic concepts and constructs can be introduced through
extension facilities, such as introduce new stereo types in profile definitions
Uses in different context: One model may have different meanings in different context
Example: Which of the following is the correct
semantics of the model on the left? There are exactly three different classes such that
…; There are at least three different classes such that
…; Which of the following programs can be
considered as satisfied the model?
Member
Staff Student
class Member{ … }class Staff extends Member{…}Class Student extends Member{…}
class MScStudent extends Student{…}
class Member { public enum MemberType {
Staff, Student }public MemberType
TypeOfMember;…
}
UML gives no definition on this issue!
Overview of our approach
This is consistent with the theory of institute proposed by Goguen and Burstall (1992) for formal specification languages.
The formal frameworkDefinition 1. (Semantics of a modelling language)
A formal semantic definition of a modelling language consists of the following elements.
A signature Sig, which defines a formal logic system; A set AxmD of axioms about the descriptive semantics, which is
in the formal logic system defined by Sig; A set AxmF of axioms about the functional semantics, which is
also in the formal logic systems defined by Sig; A mapping T from models to a set of formulas in the formal
logic system defined by Sig. The formulas are the statements for the descriptive semantics of the model;
A mapping H from models to a set of formulas in the formal logic system defined by Sig. The formulas represent the hypothesis about the context in which the descriptive semantics is interpreted.
Definition 2. (Semantics of a model)
Given a semantics definition of a modelling language as in Definition 1, the semantics of a model M under the hypothesis H, written SemH(M), is defined as follows.
SemH(M) = AxmD AxmF T(M) H(M)
where T(M) and H(M) are the sets of statements obtained by applying the semantic mappings T and H to model M, respectively. The descriptive semantics of a model M under the hypothesis H, written DesSemH(M), is defined as follows.
DesSemH(M) = AxmD T(M) H (M)
Satisfaction relation
Definition 4. (Subject domain)
A subject domain Dom of signature Sig with an interpretation Eva is a triple <D, Sig, Eva>, where D is a collection of systems on which the formulas of the logic system defined by Sig can be evaluated according to a specific evaluation rule Eva. The value of a formula f evaluated according to the rule Eva in the context of system sD, written as Eva(f, s), is called the interpretation of the formula f in s. We write s|=Evaf, if a formula f is evaluated to true in a system sD, i.e. s|=Evaf iff Eva(f, s)= true. Definition 5. (Satisfaction of a model)
Let Sig be a given signature and Dom a subject domain of Sig. A system s in D satisfies a model M according to a semantic definition SemH(M) if s|= SemH(M), i.e. for all formulas f in SemH(M), s|=f.
A Simplified Metamodel of Class Diagram
MultiplicityElement
ValueSpecification
+upperValue+lowerValue
TypedElement
Type+type
Classifier
+isAbstract: BooleanGeneralisation
+general
DirectedRelationshipFeature
+isStatic: Boolean
StructuralFeature BehaviouralFeature
Parameter
+direction: ParameterDirectionKind
+ownedParameter
Operation ClassProperty
+aggregation: AggregationKind
+ownedAttribute0..1*
Association
+memberEnd2..*
+ownedOperation
0..1*
Relationship
ParameterDirectionKind<<enumeration>>
+in+out+inout+return
AggregationKind<<enumeration>>
+none+shared+composite
Boolean<<enumeration>>
+t+f
DataType
Signal
+specific
Interface
+ownedOperation
+ownedAttribute
NamedElement
VisibilityKind<<enumeration>>
+public+private+protected+package
+associateTo
Signature MappingThe signature mapping S from a metamodel M to a signature
= S (M) is defined by a set of signature rules so that statements representing the descriptive semantics of models are -sentences of first order predicates.
Signature RulesS1. For each metaclass named MC in the metamodel, we define a unary atomic predicate MC(x). S2. For each metaassociation from a metaclass X to a metaclass Y in a metamodel, if MA is the association end on the Y side of the metaassociation, a binary predicates MA(x, y) is defined. S3. For each metaattribute named MAttr of type MT in a metaclass MC in a metamodel, a unary function MAttr(x) is defined with domain MC and range MT. S4. For each enumeration value EV given in an enumeration metaclass ME in a metamodel, a constant EV is defined.
Example:
Signature elements: unary predicates : Class(x), Classifier(x), Generalisation(x) binary predicates general(x, y) and specific(x, y)
Classifier GeneralisationClass
+general
+specific
PersonWoman
Metamodel
Translation mappingTranslation mapping is defined by a set of translation rules that
generate formulas in the signature from a UML model M.
Translation Rules T1: Classification of elements. For each identifier id of concrete type MC, a formula in the form of MC(id) is generated. T2: Properties of elements. For each element a in the model and every applicable function MAttr that represents a metaattribute MAttr, a formula in the form of MAttr(a)=v is generated, where v is a’s value on the property. T3: Relationships between elements.
For each pair (e1, e2) of elements related by relationship R, a formula in the form of R(e1, e2) is generated to specify the relationship by applying binary predicate R(x1, x2).
Example:
Signature elements: unary predicates : Class(x), Classifier(x), Generalisation(x) binary predicates general(x, y) and specific(x, y)
Classifier GeneralisationClass
+general
+specific
PersonWoman
Metamodel
Model
Statements of the model: Class(Woman), Class(Person), Generalisation(wp) specific(wp, Woman), general(wp, Person)
Classifier GeneralisationClass
+general
+specific
PersonWoman
Interpretation in different contexts The context in which a model is interpreted can be specified as
hypothesis. A hypothesis can be defined as a rule that maps from a model to
formulas in the signature.
Hypothesis Rules H1: Distinguishability of elements.
A hypothesis that the elements of type MC in the model are all different from each other can be generated as formulas in the form of ei ej, for ij {1,2,…,k}. H2: Completeness of elements.
A hypothesis on the completeness of elements of type MC can be generated as a formula in the following form.
x. MC(x) (x = e1) (x = e2) … (x = ek) H3: Completeness of relations.
A hypothesis on the completeness of relation R in the model can be generated as a formula in the following form.
x1,x2.R(x1,x2)((x1=e1,1)(x2=e1,2))((x1= e2,1) (x2= e2,2))
… ((x1= en,1) (x2= en,2))
Definition of functional semantics
Functional semantics defines the basic concepts of object orientation
Our approach: axioms in second order predicate logic
For example, If class A inherits class B, every instance of A is also
an instance of BClass(A) Class(B) Inherits(A,B) x (A(x) B(x))
Predicates at model level in descriptive semantics.
A(x): object x is an instance of class A.It is a predicate at object level.
Object and classAxiom 1: Every object must be an instance of a class.
x (Object(x) C.(Class(C) C(x)))
Axiom 2: Every attribute declared in a class is a property of the class.
OwnedAttribute(C, x) HasAttribute(C, x)
Axiom 3: Every operation declared in a class is an operation of the class
OwnedOperation(C, x) HasOperation(C, x)
Notation: Every class C in the system is represented as a predicate C(x) such that C(a) is true if and only if a is an instance of class C.
This formula is now second order because C is a qualified variable range over predicates.
InheritanceAxiom 4: If class A inherits class B, every instance of A is
also an instance of B.Class(A) Class(B) Inherits(A,B) x (A(x) B(x))
Axiom 5: If class A inherits class B, every attribute of B is also an attribute of AClass(A) Class(B) Inherits(A,B) x (Property(x) HasAttri
bute(B, x) HasAttribute(A,x))
Axiom 6: If class A inherits class B, every operation of B is also an operation of AClass(A) Class(B) Inherits(A,B) x (Operation(x) HasOp
eration(B, x) HasOperation(A,x))
Inherits(A,B) = x (Generalisation(x) specific(x, A) general(x, B)
Re-definition and PolymorphismLet class A inherits class B. Axiom 7: If A declares attribute a with type TA, then the type of attribute a is TA regardless what is defined in class B.
Class(A), Class(B), Inherits(A,B), HasAttribute(A, a), OwnedAttribute(A,a), Type(a,TA)
CurrentType(a, A, TA)Axiom 8: If class A does not declare attribute a, but inherited attribute from B, then the type of attribute a is as in B.
Class(A), Class(B), Inherits(A,B), HasAttribute(A, a), OwnedAttribute(A,a)
( CurrentType(a, B, TB) CurrentType(a, A, TB)
Notation: CurrentType(a, A, B) means the current type of attribute a in class A is B. Note: There are similar axioms for operations.
Abstract class
Axiom 9: If class A is abstract, for every instance x of A, there must be a subclass B of A such that x is an instance of B.Class(A) IsAbstract(A)
x.(A(x) B.(Class(B)Inherits(B,A) B(x)))
Notation: IsAbstract(C) == IsAbstract(C, True)
Attribute and associationAxiom 10:
Class(A) HasAttribute(A, a) CurrentType(a, A, B) (x, y. a(x, y) A(x) B(y))
Axiom 11: Class(A) Class(B) Association(a) memberEnd(a, Ea) CurrentType(Ea, A) memberEnd(a, Eb) CurrentType(Eb, B) (x, y. Eb(x, y) A(x) B(y)) (x, y. Ea(x, y) B(x) A(y)) A B
aEa Eb
A
a: B
Notation: For any attribute or association a, a(x,y) denotes x.a=y .This is to keep notation consistent with the notation,e.g. Association(A,B) used in descriptive semantics derived from metamodel.
Multiplicity
Axiom 12: Multiplicity of associationAssociation(a) memberEnd(a, Ea) type(Ea, A) membe
rEnd(a, Eb) type(Eb, B) upperValue(Eb, m) lowerValue(Eb, n) (x. A(x) n||{y | Eb(x, y) }||m)
Axiom 13: Multiplicity of attributesClass(A) ownedAttribute(A, a) type(a, B) upperValue(
a, m) lowerValue(a, n) (x. A(x) n||{y | a(x, y) }||m)
Enumeration
Axiom 14: Distinguishability of the literal constants
Enumeration(A) ownedLiteral(A, v1) ownedLiteral(A, v2)
(Identifier(v1) Identifier(v2 ) (v1 v2))
Axiom 15: Type of the literal constants Enumeration(A) ownedLiteral(A, v) A(v)
Axiom 16: Completeness of the enumeration EnumClass(A) (x.( A(x) ownedLiteral(A, x)))
Whole-part relationships
Axiom 17: Composite relation
(Class(A) Class(B) Association(C) memberEnd(C, b) type(b, B) aggregation(b, composite) )x. (B(x) !y.(A(y) b(x,y))
A B
C
+b+a
Combination of functional and descriptive semantics
Applying functional semantics to models ‘Ordinary’ semantics in first order logic,
i.e. the properties that objects of the system at run time must satisfy
For example, [Berardi, Cal and Calvanese, 2005], [Kaneiwa and Satoh, 2006], etc.
Applying functional semantics to metamodel Axioms of models,
i.e. the properties satisfied by all models
Apply functional semantics to model: Example
Class(Woman), Class(Person), Generalisation(wp), specific(wp, Woman), general(wp, Person) ,
A, B (Class(A) Class(B) Inherits(A,B) x (A(x) B(x)))
x (Woman(x) Person(x))
where Inherits(A,B) =
x (Generalisation(x) specific(x, A) general(x, B)
Classifier GeneralisationClass
+general
+specific
PersonWoman Descriptive semantics
Functional semanticsObject level semantics
Apply functional semantics to metamodelClassifier GeneralisationClass
+general
+specific
PersonWoman
MetaClass(Class), MetaClass(Classifier), MetaGeneralisation(cc),
specific(cc, Class), general(cc, Classifier) ,
A, B.(Class(A) Class(B) Inherits(A,B) x (A(x) B(x)))
x (Class(x) Classifier(x)) where Inherits(A,B) =
x (MetaGeneralisation(x) specific(x, A) general(x, B)
Descriptive semantics applied to metamodel
Functional semantics
Axiom of models
Implementation of functional semanticsThe functional semantics for the OO concepts used in UML
class diagram are defined as a set of rules that maps from class diagram (metamodel) to axioms (formulas of the -sentences) and implemented directly.
Axiom rules:A1: Completeness of classification.
Let MC1, MC2, …, MCn be the set of concrete metaclasses in a metamodel. We have an axiom x. MC1(x) MC2(x) … MCn(x)A2: Disjointness of classification.
Let MC1, MC2, …, MCn be the set of concrete metaclasses in a metamodel. For each pair of different concrete metaclasses MCi and MCj, ij, we have an axiom x. MCi(x) ¬ MCj(x). A3: Logical implication of inheritance.
For a generalisation relation from metaclass MA to MB in a metamodel, we have an axiom x. MA(x) MB(x).
Corresponding to Axiom 4 of functional semantics
Axiom Rules (Continue)
A4: Completeness of specialisations.
Let MA be a metaclass in a metamodel and MB1, MB2, …, MBk be the set of metaclasses specialising MA. We have an axiom x. MA(x) MB1(x) MB2(x) … MBk(x).
A5: Types of parameters of predicates.
For each binary predicate MA(x, y) derived from an association from metaclass MC1 to MC2 in a metamodel, we have an axiom x, y. MA(x, y) MC1(x) MC2(y).
A6: Domain and range of functions.
For each function MAttr(x) derived from a metaattribute MAttr of type MT in a metaclass MC, we have an axiom x,y. MC(x) (MAttr(x) = y) MT(y).
A7: Multiplicity of binary predicate. For each binary predicate MA(x, y) derived from an association from metaclass MC1 to MC2 in a metamodel, let Mul be the multicity value specified on the association end MA, we have axioms in the following form.If Mul = 0..1: x, y, z. MC1(x) MA(x, y) MA(x, z) (y = z)If Mul = 1 or unspecified: x. MC1(x) y. MA(x, y) and
x, y, z. MC1(x) MA(x, y) MA(x, z) (y = z)If Mul = 1..*: x. MC1(x) y. MA(x, y)If Mul = 2..*: x. MC1(x) y, z. MA(x, y) MA(x, z) (y z)If Mul = 0..2: x, y, z, u. MC1(x) MA(x, y) MA(x, z) MA(x, u)
(y = z) (y = u) (u = z)
Corresponding to Axiom 10
Corresponding to Axiom 11
Corresponding to Axiom 12
Axiom Rules (continue)A8: Multiplicity of function.
For each function MAttr(x) derived from a metaattribute MAttr of type MT in a metaclass MC, let Mul be the multicity value of the metaattribute MAttr, we have axioms:If Mul = 0..1: x, y, z. MC(x) (MAttr(x) = y) (MAttr(x) = z) -> (y = z)If Mul = 1: x. MC(x) -> y. (MAttr(x) = y) and x, y, z. MC(x) (MAttr(x) = y) (MAttr(x) = z) -> (y = z)If Mul = 1..*: x. MC(x) -> y. (MAttr(x) = y)A9: Distinguishability of the literal constants.
For each pair of different literal values a and b of an enumeration type, we have an axiom a b. A10: Type of the literal constants.
For each enumeration value a defined in an enumeration metaclass ME, we have an axiom in the form of ME(a) stating that the type of a is ME. A11: Completeness of the enumeration.
An enumeration type only contains the listed literal constants as its values, hence for each enumeration metaclass ME with literal values a1, a2, …, ak, we have an axiom in the form ofx. ME(x) -> (x = a1) (x = a2) … (x = ak). Axiom Rule 12: Well-formedness rules.
For each WFR formally specified in OCL, we have a corresponding axiom in the first order language.
Corresponding to Axiom 13
Corresponding to Axiom 14
Corresponding to Axiom 15
Corresponding to Axiom 16
Strict metamodelling principle Axiom rules also contain ‘hypothesis’ on the uses of class diagra
ms as metamodels, such as the strict meta-modelling principle, which is to ensure that a metamodel is a well-defined abstract syntax of modelling language.
Strict Metamodelling:
In an n-level modelling architecture M0, M1, …, Mn, every element of an Mm-level model must be an instance-of exactly one element of an Mm+1-level model, for all 0 m < n-1, and any relationship other than the instance-of relationship between two elements X and Y implies that level(X ) = level(Y).
Corresponding to axiom rules A1, A2 and A4.
Semantics of Interaction and State Machine Same signature, axiom and formula mappings are applied to the
meta-models of UML state machine and interaction diagrams. Additional Axiom Rules for inter-metamodel connectionsAxiom Rules
A10: Cross metamodel association and inheritance.
For each cross metamodel inheritance from metaclass MA to external metaclass MB, we have an axiom in the form of x. MA(x) -> MB(x).
A2’: Completeness of specialisations across metamodels.
Let MA be a metaclass depicted in two metamodels MM1 and MM2. Let metaclasses MB1, MB2, …, MBk be the set of metaclasses that specialise MA in metamodel MM1, and MC1, MC2, …, MCp be the set of metaclasses that specialise MA in metamodel MM2. We have the following axiom when a model is defined by MM1 and MM2.
x. MA(x) -> MB1(x) … MBk(x) MC1(x) … MCp(x)
A Simplified Metamodel of Interaction Diagram
Interaction
Lifeline Message
+lifeline +message
ConnectableElement
+represents
MessageEvent
SendOperationEvent SendSignalEventOperation (from Kernel) Signal (from Kernel)
+operation +signal
+event
+sender
+receiver+after
Behaviour BehaviouralFeature (from Kernel)Classifier(from Kernel)
+specification
+context
TypedElement(from kernel)
A Simplified Metamodel of State Machine
StateMachine
ProtocolStateMachine Vertex
StatePseudoState
+kind: PseudostateKind
Transition
+vertex
+transition
+source
+target
Trigger Constraint
+trigger +guard
ProtocolConformance
DirectedRelationship(from Kernel)
+generalMachine+specificMachine
Behaviour (from Interaction)
+exit +doActivity+entry
+effect
PseudostateKind<<enueration>>
+initial+final+deepHistory+shallowHistory+join+fork+junction+choice
BehaviourStateMachine
StateBehaviour
The Tool LAMBDESLAMBDES stands for Logic Analyser of Model/Metamodel Based on Descriptive Semantics
User InterfaceLAMBDES
Logic system for metamodel
Modelling tool StarUML
Axiom Generator
Metamodel
Metamodel in XMI
Theorem prover SPASS
Inference result
Statements
Model
Model in XMI
Signature Generator
Formula Generator
Signature Axioms
Proof Goal
Hypothesis
Modelling Context
Hypothesis Generator
Domain Generator
Auxiliary constants &
formulas
Conjecture Generator
Conjecture
Logic system for model
Design Pattern Specification
Design Pattern Spec
Repository
Applications of Descriptive Semantics
Consistency checking of UML models Validation of consistency constraints for UML models Consistency checking of UML meta-models Conformance checking of designs to design patterns Consistency checking of specification of design patterns
Definition 3. (Properties of a model)
Let SemH(M) be the semantics of a model M. M has a property P (represented as a formula in the logic system defined by Sig) under the semantics definition SemH(M) and the hypothesis H, if and only if AxmDAxmFT(M) H(M) P in the formal logic system. Similarly, we say that M has a property P in descriptive semantics, if and only if AxmDT(M) H(M) P in the formal logic system.
Consistency checking of UML models
Definition 6. (Logical consistency)
Let SemH(M) = AxmD AxmF T(M) H(M) be the semantics of a model M. Model M is said to be logically inconsistent in the semantic definition SemH(M) if SemH(M)|-false; otherwise, we say that the model is logically consistent.
Definition 7. (Consistent interpretation of formulas in a subject domain)
Let Dom=<D, Sig, Eva> be a subject domain as defined in Definition 4. The interpretation of formulas in signature Sig is consistent with first order logic if and only if for all formulas q and p1, p2, …, pk that p1, p2, …, pk |- q, and for all systems s in D that Eva(pi, s) =true for i=1,2,…, k, we always have Eva(q, s) =true.
Validity of consistency checkingTheorem 1. (Unsatisfiability of inconsistent model)
A model M that is logically inconsistent in the semantic definition SemH(M) is not satisfiable on any subject domain whose interpretation of formulas is consistent with first order logic.
Inconsistent model => it cannot be implemented (not satisfiable) Consistency model => not necessarily implementable Other issues effect satisfiability:
Property of the subject domain: e.g. whether the programming language is powerful enough to implement
Non-logic property of the model: e.g. whether the system is feasible
Validation of consistency constraints Consistency constraints:
Logic statements about models, e.g. ‘a life line must represent an instance of a class’ x, y, z. Lifeline(x) represent(x,y) type(y, z) Class(z)
Definition 8. (Consistency w.r.t. consistency constraints)
Given a set of consistency constraints C={c1, c2, …, cn}, the consistency of a model M with respect to the constraints C under the semantics definition SemH(M) is the consistency of the set U = SemH(M) C of formulas. In particular, we say that a model fails on a specific constraint ck, if SemH(M) is consistent, but SemH(M) {ck} is not.
Results of experiments:• A number of sample UML models were checked to be
consistent.• Mutants of the models were checked and detected inconsistency
in mutants.
Definition of validity and effectiveness
Definition 9. (Validity of consistency constraints)
Let AxmD and AxmF be the sets of axioms for descriptive semantics and functional semantics, respectively. A set C={c1, c2, …, cn} of consistency constraints is descriptively valid if AxmDC is logically consistent. The set C of consistency constraints is functionally valid AxmDAxmFC is logically consistent. Definition 10. (Effectiveness of consistency constraints)
Let A be a set of semantics axioms. A set C={c1, c2, …, cn} of consistency constraints is logically ineffective with respect to the set A of axioms if A C.
Results of Experiments:A set of 5 consistency constraints were validated by using LAMBDES tool. They were proved valid and effective.
Consistency checking of meta-modelsDefinition 11 (Inconsistency of meta-model).
A meta-model M is inconsistent if AxmD(M) is logically inconsistent.
Experiments: Subjects:
Simplified UML, UML 2.0 metamodel, AspectJ profile Findings:
Simplified metamodel: consistent UML 2.0:
16 inconsistencies due to voilation of strict meta-modelling 1 inconsistently as abstract and concrete metaclasses in different metamodel class diagrams.
AspectJ: Incomplete definition of 7 meta-classes2 inconsistency due to violation of strict meta-modelling
Conformance of designs to patterns Patterns are meta-models (rather than models)
A pattern specifies a set of models that have common structural and behavioural properties
A pattern can be specified as a predicate on models in first order logic (Bayley and Zhu 2007, 2008)
Translation of pattern specification A specification of design pattern is translated into a
specification of systems in sig-formulasTemplate Method Specification
formula(exists([%Components: xAbstractClass, xTemplateMethod, xOthers ], and(%Static conditions: Class(xAbstractClass), ownedOperation(xAbstractClass, xTemplateMethod), ownedOperation(xAbstractClass,xOthers), isLeaf(xTemplateMethod,bTrue), not(equal(xTemplateMethod,xOthers)), isLeaf(xOthers,bFalse)%Dynamic conditions: callsHook(xTemplateMethod,xOthers)))).
Validity of conformance checking
Definition 12 (Correctness of translation)Let p be a predicate on models, p' be a predicate on systems. The predicate p' is a correct translation of p, if for all models m, we have that
m |- p iff sD. (s |= (Sem(m) p’).
TheoremLet P be a pattern and Spec(P) be a specification of the pattern. Suppose that Spec'(P) is a correct translation of Spec(P). For all models m, if Sem(m) Spec'(P) is true in FOL, then, for all systems s D, s |= m implies s |= Spec'(P).
Experiments Specification of patterns:
23 design patterns in GoF book are specified in first order logic on models Translation:
The specifications are translated into first order logic in LAMBDES syntax Experiment 1: checking specifications Consistency
23 specifications of patterns were checked for consistency with the axioms Results: all specification are consistent
Experiment 2: Testing conformance checking ability 23 designs represented in UML diagrams are created according to GOF; Each design is checked against all 23 patterns using LAMBDES Results:
100% recognition of designs as instances of intended pattern 22% of false positive recognitions (including time-out, which is 5%)
Experiment 3: Testing modelling tool’s correctness 23 design created using StarUML templates Each design is checked against all 23 patterns using LAMBDES Results: 61% is not recognised, Overall fault detecting rate: 81%
Conclusion and comparison Separation of functional semantics from descriptive semantics ca
n simplify the formal semantic definition of UML and it is scalable Applicable for all types of diagrams defined in meta-model uniformly Applicable to multiple views defined by multiple meta-models, and addre
ssed the extendability issues Addressed the issue due to flexibility in the uses of modelling language in
different development context Addressed the issue for different interpretation of modelling languages in
different subject domains Reasoning about models in first order logic can be feasible and u
seful in model drive software development Can be automated by tools such as LAMBDES + SPASS + StarUML Can reason about properties that are not possible for semantics at object le
vel, such as The conformance of designs to patterns, The validation of consistency constraints, The consistency of meta-model, etc.
Future work Further development of the axioms in second order
predicate logic for functional semantics; Theoretical analysis of the logic properties of the
semantics definition Soundness of the rules: yes Consistency of the rules: yes Completeness of the rules:
In what sense? How to prove?
Case studies on reasoning about other properties of designs, such as Platform specific models, platform independence, etc. Transformation of models,
References1. Hong Zhu, Ian Bayley, Lijun Shan and Richard Amphlett, Tool
Support for Design Patterns in Model-Driven Development, submitted to ICSE 2009.
2. Lijun Shan and Hong Zhu, A Formal Descriptive Semantics of UML, Proc. of ICFEM’08, 27-31 October 2008 Kitakyushu-City, Japan. (In press)
3. Ian Bayley and Hong Zhu, On the Composition of Design Patterns, Proc. of QSIC’08, IEEE CS Press, 12-13 August, 2008, Oxford, UK.
4. Ian Bayley and Hong Zhu, Specifying Behavioural Features of Design Patterns in First Order Logic, Proc. of COMPSAC’08, (Note: A full length version: Technical report TR-08-01, Department of Computing, Oxford Brookes Univ., Oxford, UK).
5. Ian Bayley and Hong Zhu, Formalising Design Patterns in Predicate Logic, Proc. of SEFM’07, London, UK, Sept. 2007.