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Recognition of Patterns in Software Designs Models via Logic Inferences PowerPoint Presentation

Recognition of Patterns in Software Designs Models via Logic Inferences

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### Recognition of Patterns in Software Designs Models via Logic Inferences

Hong Zhu

Department of Computing and Electronics

Oxford Brookes University

Oxford OX33 1HX, UK

Email: hzhu@brookes.ac.uk

Acknowledgement Inferences

This presentation is based on joint research work with

- Dr. Ian Bayley of Oxford Brookes University
- Dr. Lijun Shan, who was my PhD student
- Mr. Richard Amphlett, who was supported by the Undergraduate Research Student Scholarship funded by the Reinvention Centre, UK.

Seminar: Recognition of Patterns in Design Models

Outline Inferences

- Motivation and related works
- Tool support to the application of DPs
- Formalisation of DPs

- Our previous work
- Specification of DPs
- Semantics of UML models

- The proposed approach
- Bridge the gaps
- The tool LAMBDES-DP
- Experiments

- Conclusion and future work

Seminar: Recognition of Patterns in Design Models

Motivation Inferences

Explained informally (in English)

- Design patterns (DPs)
- Reusable solutions to commonly occurring design problems
- Represented in Alexandrian form
- Synopsis, Context, Forces, Solution, Consequences, Implementation, Examples, Related patterns

- Proper use can improve software quality and development productivity
- Reduce ambiguity
- Automated tool support

Clarified with illustrative diagrams

Specific code examples

Seminar: Recognition of Patterns in Design Models

Existing works 1 Inferences

- Tool support to the application of DP
- Instantiation of patterns
- Generating an instance of a design pattern
- Widely available in modelling tools

- Recognition of patterns
- Code level
- Recognizing instances of patterns by analyzing the program code

- Design level
- Recognizing instances of patterns by analyzing the design documents, especially UML diagrams

- Code level

- Instantiation of patterns

Seminar: Recognition of Patterns in Design Models

e.g. SQL queries Inferences

e.g. Prolog queries

e.g. Prolog clauses

e.g. Relational DB

e.g. Prolog execution engine

e.g. DBMS/SQL server

Code Level DP Recognition ToolsArchitecture of the tools

e.g. Java Program

Program Code

Code Analyzer

Pattern Library

Program Code intermediate representation

Pattern Match Engine

Seminar: Recognition of Patterns in Design Models

Code Level DP Recognition Tools Inferences

- Current state of art
- More than a dozen such tools have been reported in the literature;
- See (Dong, Zhao and Peng, SERP 2007) for a survey;

- Well-known examples:
- HEDGEHOG (Blewitt, Bundy and Stark, ASE 2005)
- FUJABA (Niere, et al. ICSE 2002)
- PINOT (Shi and Olsson, ASE 2006)

- More than a dozen such tools have been reported in the literature;
- Problems
- Low level of abstraction
- Late in development process
- Hard to improve precision and recall rate

Seminar: Recognition of Patterns in Design Models

Prolog statements Inferences

Prolog execution engine

Special SW that matches UML diagram to RBML meta-models

Design/Model Level DP Recognition ToolsUML diagram

Software Design Model

Architecture

Meta-models in RBML

Specification of design patterns

Model Analyzer

Pattern Library

Model intermediate representation

Pattern Match Engine

Seminar: Recognition of Patterns in Design Models

Design/Model Level DP Recognition Tools Inferences

- Current state of art
- (Kim and Lu, ICECCS’06):
- Translate RBML and UML into Prolog

- (Kim and Shen, SAC’07, SQJ 2008): RBMLCC
- Plug-in to IBM Rational Rose
- Patterns are specified by meta-models in RBML
- Applied to 7 of the 23 GoF patterns
- Used class diagram only

- (Kim and Lu, ICECCS’06):
- Problems
- Unclear about precision and recall rate.
- Behaviour features of DPs are not considered.

Seminar: Recognition of Patterns in Design Models

Our Approach Inferences

UML diagram:

Class diagram + sequence diagram

Software Design Model

Specification of design patterns in first order logic

Based on the formal descriptive semantics of the UML language

Model Analyzer

Pattern Library

Statements in first order predicate logic

Model intermediate representation

Logic inference engine

Pattern Match Engine

Seminar: Recognition of Patterns in Design Models

UML Meta-Model Inferences

LAMBDES: Logic Analyser of Models and Meta-Models Based on Descriptive Semantics of UML

Logic statements in SPASS format

SPASS:

A general purpose first order predicate logic inference engine

Implementation 1: LAMBDES-DPUML diagram: Class diagram + sequence diagram

Software Design Model

Specification of design patterns in first order logic

Model Analyzer

Pattern Library

Model intermediate representation

Pattern Match Engine

Seminar: Recognition of Patterns in Design Models

UML to SPASS translator Inferences

Logic statements in SPASS format

SPASS

Implementation 2Specification of design patterns in first order logic

UML diagram:

Class diagram + sequence diagram

Software Design Model

Model Analyzer

Pattern Library

Model intermediate representation

Pattern Match Engine

Seminar: Recognition of Patterns in Design Models

UML to Prolog translator Inferences

Prolog statements

Prolog Interpreter

Implementation 3UML diagram:

Class diagram + sequence diagram

Specification of design patterns in first order logic

Software Design Model

Model Analyzer

Pattern Library

Model intermediate representation

Pattern Match Engine

Seminar: Recognition of Patterns in Design Models

Formalisation of DPs Inferences

Two approaches in the literature:

- Definition of the structural and behavioural features of design patterns
- Using a graphic meta-modelling language
- Extension of UML meta-model
- Devise new meta-modelling language

- Using formal logics

- Using a graphic meta-modelling language
- Transformation of “non-standard” designs into instances of patterns
- e.g. Lano et al. (1996)

Seminar: Recognition of Patterns in Design Models

Specification of DPs as Inferences Graphic Meta-Models

- Well-known works
- Lauder and Kent (1998):
- UML meta-model

- Le Guennec et al. (UML 2000):
- an extension of the UML meta-model and OCL

- Eden (2001):
- the graphical language LePUS

- Mapelsden et al. (CRPIT ’02):
- Design Pattern Modeling Language

- Kim, France, Ghosh, and Song (COMPSAC 2003):
- Role-based metamodelling language RBML

- Lauder and Kent (1998):

Seminar: Recognition of Patterns in Design Models

Problems in Graphic Meta-Modelling Inferences

Thus, the need of OCL

- Expressiveness:
- Difficult (if not impossible) to specify negative features
- such as to specify no association between two classes

- Difficult (if not impossible) to specify variant features
- such as to specify object adapter and class adapter patterns in one meta-model

- Difficult (if not impossible) to specify negative features
- Readability:
- Graphic meta-models are hard to understand

- Precision:
- Meta-modelling languages are usually informally defined
- It is non-trivial to define what is an instance of a meta-model

Seminar: Recognition of Patterns in Design Models

Formalisation of DPs in Formal Logics Inferences

- Well-known works
- Mikkonen (ICSE’98):
- the use of predicate logic to specify structural features

- Taibi (2003, 2006):
- the use of first order predicate logic to specify structural features and temporal logic to specify behavioural features

- Bayley and Zhu (SEFM’07, COMPSAC’08, QSIC’08, JSS 2010):
- GEBNF definition of UML abstract syntax
- Formal predicate logic induced from GEBNF syntax definition
- Specify both structural and behavioural features

- Mikkonen (ICSE’98):

Seminar: Recognition of Patterns in Design Models

Our Work on Specification of DPs Inferences

Formal meta-modelling in first order logic (Bayley and Zhu 2007, 2008, 2009, 2010)

- Basic ideas:
- The abstract syntax of UML diagrams specified in GEBNF (Graphically Extended BNF).
- A formal predicate logic (FOL) language systematically derived from the abstract syntax definition
- Specifying design patterns in the FOL as predicate on UML diagrams and pattern instantiation is predicate satisfaction.

Seminar: Recognition of Patterns in Design Models

GEBNF: Example Inferences

Non-terminal symbol: the type of entities in the model.

ClassDiagram ::=

classes : Class+,

assocs,inherits, CompAg : Rel*

Class ::=

name : String ,

[attrs : Property*],

[opers : Operation*]

Rel ::=

[name : String ],

source, end : End

End ::=

node : Class,

[name : String ],

[mult : MultiplicityElement]

Functionsymbol: function induced from the syntax definition. For example, classes is a function from “ClassDiagram” to the set of “Classes” in the diagram.

Terminal symbol: the basic entities that may occur in a model. For example, here ‘String’ represent any string of characters can be used as the name of the relation.

Referential occurrence of non-terminal symbol: the model construction contains a reference to an existing element of the type of entities. Here, the end of a relation refers to an existing class in the diagram.

Seminar: Recognition of Patterns in Design Models

Comparison with Other Approaches Inferences

- Expressiveness:
- Both structural feature and behavioural features are specified in the same FOL
- Variants of patterns can be specified
- All 23 GoF patterns are specified

- Readability:
- More readable than its rivals

- Tool support
- Facilitate reasoning,
- To formally prove patterns’ properties and relationships
- To recognise pattern instances in designs

- Facilitate operations and transformations of DPs
- E.g. to compose patterns: A Calculus of DP Composition

- Facilitate reasoning,

Seminar: Recognition of Patterns in Design Models

Our Work on Semantics of UML Inferences

Semantics of UML models

(Shan and Zhu, 2008, 2009)

- Basic Ideas: the formal semantics of UML is defined separately on two aspects:
- descriptive semantics:
- defines which systems are instances of a model.
- e.g. the system consists of two classes A and B, and A is a subclass of B.

- functional semantics:
- defines the basic modeling concepts,
- e.g. If class X is a subclass of Y, then all instances of X are also instances of Y.

- descriptive semantics:

It describes the system without referring to what is meant by class and subclass.

It defines the notion of class and subclass.

Seminar: Recognition of Patterns in Design Models

Translation of models into Formal Logic Inferences

- Signature mapping: rules to derive symbols of FOL from the metamodel
- Axiom mapping: rules to derive statements in the FOL from the metamodel that must be true for all valid models
- Translation mapping: rules to translate a graphical model into predicates in FOL that it is true if and only if a system is an instance of the model
- Hypothesis mapping: rules that selected by the user to be applied in order to characterise the context in which the model is used

Seminar: Recognition of Patterns in Design Models

Example: Inferences

- The following is a subset of the predicates generated from the diagram

Seminar: Recognition of Patterns in Design Models

Bridging the Gap Inferences

- Differences between the FOL for DP spec and the FOL for UML semantics
- Syntactic difference
- Semantic difference
- Predicates in a DP specification are evaluated on UML models
- Predicates in the descriptive semantics of UML models are evaluated on software systems

- DP specification is translated into the syntax of FOL for descriptive semantics

Seminar: Recognition of Patterns in Design Models

Example: Inferences

- The specification of Template Method can be translated into:

Seminar: Recognition of Patterns in Design Models

P Inferences is a pattern.

Spec(P) is the formal specification of P.

The descriptive semantics of model m.

System s is an instance of model m.

System s satisfies the specification.

Recognition of a pattern at design level becomes a logic inference problem.

Seminar: Recognition of Patterns in Design Models

The Tool LAMBDES-DP Inferences

Seminar: Recognition of Patterns in Design Models

Experiments: Inferences

1. Use StarUML to produce design instances as UML diagrams and export them as XMI representations.

2. Use LAMBDES to convert these XMI representations to FOL;

3. Use LAMBDES to check these FOL representations for consistency errors, revising them until there are no more errors;

4. For each pattern, use LAMBDES-DP to determine if the model conforms to (i.e. implies the specification of) the pattern.

- Three possible outcomes:
- Proof Found, meaning definitely yes,
- Completion Found, meaning definitely no, and
- Time Out, meaning that no proof was found in the maximum time limit that SPASS allows, which is 990 seconds.

Seminar: Recognition of Patterns in Design Models

Subjects of the experiments Inferences

- Patterns:
- 23 Patterns in GoF book

- Design Instances:
Two sets of design instances were produced manually from the diagrams in the GoF book.

- Set 1 (Class Only): contains a class diagram for each of the 23 patterns in the book.
- Set 2 (Class + Seq): contains class and sequence diagrams for the only 6 patterns in the book that contain both.

Seminar: Recognition of Patterns in Design Models

Overview of the Design Instances: ClassOnly Set Inferences

Seminar: Recognition of Patterns in Design Models

Overview of the Design Instances: Class+Seq Set Inferences

Seminar: Recognition of Patterns in Design Models

Experiment Results Inferences

- Why is the false positive error rate so high for class only subjects?
- Can we improve the error rate?
- Is there a limit to which the error rate can be improved?

Seminar: Recognition of Patterns in Design Models

Why is the error rate is so high? Inferences

- Interdependence between patterns
- Inadequate specification of design patterns
- Inclusive relationships between patterns

- Redundancy in the subject models used in the experiment
- a model for testing its conformance to one pattern may coincidently contain an instance of another pattern

Seminar: Recognition of Patterns in Design Models

Inclusion Relation on Patterns Inferences

Definition 1. (Inclusion relation on patterns)

A pattern Aincludes pattern B if the logic specification of pattern A implies the logic specification of pattern B. Formally,

Spec(A)Spec(B).

Theorem 1.

For all patterns A and B, if a model contains an instance of pattern A, then the model must also contains an instance of pattern B if A includes B.

Seminar: Recognition of Patterns in Design Models

Redundancy in test models Inferences

Definition 2. (Inclusion relation on models)A model Astructurally includes (or includes for short) model B if B can be obtained by deleting some elements and systematically renaming the elements in B. In such a case, we say that model A is a superset of model B, or B is a subset of model A.

Theorem 2. For all models A and B, if model B contains an instance of a DP and B is a subset of model A, then, A also contains an instance of the DP.

Seminar: Recognition of Patterns in Design Models

Inclusions between DP Specs and Test Models Inferences

Seminar: Recognition of Patterns in Design Models

The limit of false positive error rates Inferences

Definition 3. (Minimal model of design pattern)

A model M is a minimal model of a design pattern DP, if it contains an instance of pattern DP and any model obtained by removing an element from M contains no instance of pattern DP.

Corollary of Theorem 2.

For all sets of models to test the same set of specifications of the design patterns used in the experiment, the error rate of false positives must be greater than or equal to the error rate obtained in the test on the minimal models.

Seminar: Recognition of Patterns in Design Models

Results of Testing against Minimal Models Inferences

The error rate is ≈8%.

Seminar: Recognition of Patterns in Design Models

Conclusion: Inferences

- Recognition of patterns at design level can be accurate with good precision and recall rate;
- Behavioural feature is crucial for accurate specification and hence the recognition of patterns, as we have argued in (Bayley and Zhu, COMPSAC 2008);

Seminar: Recognition of Patterns in Design Models

Future work Inferences

- Experiment with industrial real systems
- Integration with code level tools
- Some tools extract information from code and represent the extracted information in the form of first order logic predicates

Seminar: Recognition of Patterns in Design Models

References Inferences

- I. Bayley and H. Zhu. Formalising design patterns in predicate logic. In Proc. of SEFM’07, pp 25–36.
- I. Bayley and H. Zhu. Specifying behavioural features of design patterns in first order logic. Proc. of COMPSAC’08, pp203–210.
- L. Shan and H. Zhu. A formal descriptive semantics of UML. Proc. of ICFEM’09, pp375–396.
- H. Zhu, I. Bayley, L. Shan and R. Amphlett, Tool Support for Design Pattern Recognition at Model Level, Proc. of COMPSAC'09, July 2009.
- L. Shan and H. Zhu, Semantics of Metamodels in UML, Proc. of TASE’09, Aug. 2009
- H. Zhu, L. Shan, I. Bayley and R. Amphlett, A Formal Descriptive Semantics of UML And Its Applications, in UML 2 Semantics and Applications, Kevin Lano (Eds.), John Wiley & Sons, pp95-123, Nov. 2009.
- I. Bayley and H. Zhu, Formal Specification of the Variants and Behavioural Features of Design Patterns, Journal of Systems and Software Vol. 83, No. 2, Feb. 2010, pp 209–221

Seminar: Recognition of Patterns in Design Models

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