Download presentation

Presentation is loading. Please wait.

Published byJake O'Neal Modified over 4 years ago

1
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

2
Sept. 2010 2 Seminar: Recognition of Patterns in Design Models Acknowledgement 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.

3
Sept. 2010 3 Seminar: Recognition of Patterns in Design Models Outline 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

4
Sept. 2010 4 Seminar: Recognition of Patterns in Design Models Motivation 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 Explained informally (in English) Clarified with illustrative diagrams Specific code examples

5
Sept. 2010 5 Seminar: Recognition of Patterns in Design Models Existing works 1 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

6
Sept. 2010 6 Seminar: Recognition of Patterns in Design Models Code Level DP Recognition Tools Program Code intermediate representation Pattern Library Pattern Match Engine Program Code Code Analyzer e.g. Java Program e.g. Prolog clauses e.g. Prolog queries e.g. Prolog execution engine Architecture of the tools e.g. SQL queries e.g. Relational DB e.g. DBMS/SQL server

7
Sept. 2010 7 Seminar: Recognition of Patterns in Design Models Code Level DP Recognition Tools 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) Problems Low level of abstraction Late in development process Hard to improve precision and recall rate

8
Sept. 2010 8 Seminar: Recognition of Patterns in Design Models Design/Model Level DP Recognition Tools Model intermediate representation Pattern Library Pattern Match Engine Software Design Model Model Analyzer UML diagram Specification of design patterns Architecture Prolog statements Prolog execution engine Meta-models in RBML Special SW that matches UML diagram to RBML meta-models

9
Sept. 2010 9 Seminar: Recognition of Patterns in Design Models Design/Model Level DP Recognition Tools Current state of art (Kim and Lu, ICECCS06): Translate RBML and UML into Prolog (Kim and Shen, SAC07, 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 Problems Unclear about precision and recall rate. Behaviour features of DPs are not considered.

10
Sept. 2010 10 Seminar: Recognition of Patterns in Design Models Our Approach Model intermediate representation Pattern Match Engine Software Design Model Model Analyzer UML diagram: Class diagram + sequence diagram Specification of design patterns in first order logic Pattern Library Based on the formal descriptive semantics of the UML language Statements in first order predicate logic Logic inference engine

11
Sept. 2010 11 Seminar: Recognition of Patterns in Design Models Implementation 1: LAMBDES-DP Model intermediate representation Software Design Model Model Analyzer UML diagram: Class diagram + sequence diagram Specification of design patterns in first order logic Pattern Match Engine Pattern Library 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 UML Meta-Model

12
Sept. 2010 12 Seminar: Recognition of Patterns in Design Models Implementation 2 Model intermediate representation Pattern Match Engine Software Design Model Model Analyzer UML diagram: Class diagram + sequence diagram Specification of design patterns in first order logic Pattern Library UML to SPASS translator Logic statements in SPASS format SPASS

13
Sept. 2010 13 Seminar: Recognition of Patterns in Design Models Implementation 3 Model intermediate representation Pattern Match Engine Software Design Model Model Analyzer UML diagram: Class diagram + sequence diagram Specification of design patterns in first order logic Pattern Library UML to Prolog translator Prolog statements Prolog Interpreter

14
Sept. 2010 14 Seminar: Recognition of Patterns in Design Models Formalisation of DPs 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 Transformation of non-standard designs into instances of patterns e.g. Lano et al. (1996)

15
Sept. 2010 15 Seminar: Recognition of Patterns in Design Models Specification of DPs as 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

16
Sept. 2010 16 Seminar: Recognition of Patterns in Design Models Problems in Graphic Meta-Modelling 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 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 Thus, the need of OCL

17
Sept. 2010 17 Seminar: Recognition of Patterns in Design Models Formalisation of DPs in Formal Logics Well-known works Mikkonen (ICSE98): 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 (SEFM07, COMPSAC08, QSIC08, JSS 2010): GEBNF definition of UML abstract syntax Formal predicate logic induced from GEBNF syntax definition Specify both structural and behavioural features

18
Sept. 2010 18 Seminar: Recognition of Patterns in Design Models Our Work on Specification of DPs 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.

19
Sept. 2010 19 Seminar: Recognition of Patterns in Design Models GEBNF: Example 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] Function symbol: function induced from the syntax definition. For example, classes is a function from ClassDiagram to the set of Classes in the diagram. Non-terminal symbol: the type of entities in the model. 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.

20
Sept. 2010 20 Seminar: Recognition of Patterns in Design Models Example: Template Method pattern =

21
Sept. 2010 21 Seminar: Recognition of Patterns in Design Models Comparison with Other Approaches 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

22
Sept. 2010 22 Seminar: Recognition of Patterns in Design Models Our Work on Semantics of UML 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. It describes the system without referring to what is meant by class and subclass. It defines the notion of class and subclass.

23
Sept. 2010 23 Seminar: Recognition of Patterns in Design Models Translation of models into Formal Logic 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

24
Sept. 2010 24 Seminar: Recognition of Patterns in Design Models Example: The following is a subset of the predicates generated from the diagram

25
Sept. 2010 25 Seminar: Recognition of Patterns in Design Models Bridging the Gap 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

26
Sept. 2010 26 Seminar: Recognition of Patterns in Design Models Example: The specification of Template Method can be translated into:

27
Sept. 2010 27 Seminar: Recognition of Patterns in Design Models P 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.

28
Sept. 2010 28 Seminar: Recognition of Patterns in Design Models The Tool LAMBDES-DP

29
Sept. 2010 29 Seminar: Recognition of Patterns in Design Models Experiments: 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.

30
Sept. 2010 30 Seminar: Recognition of Patterns in Design Models Subjects of the experiments 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.

31
Sept. 2010 31 Seminar: Recognition of Patterns in Design Models Overview of the Design Instances: ClassOnly Set

32
Sept. 2010 32 Seminar: Recognition of Patterns in Design Models Overview of the Design Instances: Class+Seq Set

33
Sept. 2010 33 Seminar: Recognition of Patterns in Design Models Experiment Results ClassOnlyClass+Seq Recall (False negative error rate) 0% Precision (False positive error rate) < 22%0% 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?

34
Sept. 2010 34 Seminar: Recognition of Patterns in Design Models Why is the error rate is so high? 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

35
Sept. 2010 35 Seminar: Recognition of Patterns in Design Models Inclusion Relation on Patterns Definition 1. (Inclusion relation on patterns) A pattern A includes 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.

36
Sept. 2010 36 Seminar: Recognition of Patterns in Design Models Redundancy in test models Definition 2. (Inclusion relation on models) A model A structurally 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.

37
Sept. 2010 37 Seminar: Recognition of Patterns in Design Models Inclusions between DP Specs and Test Models

38
Sept. 2010 38 Seminar: Recognition of Patterns in Design Models The limit of false positive error rates 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.

39
Sept. 2010 39 Seminar: Recognition of Patterns in Design Models Results of Testing against Minimal Models The error rate is 8%.

40
Sept. 2010 40 Seminar: Recognition of Patterns in Design Models Conclusion: 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);

41
Sept. 2010 41 Seminar: Recognition of Patterns in Design Models Future work 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

42
Sept. 2010 42 Seminar: Recognition of Patterns in Design Models References I. Bayley and H. Zhu. Formalising design patterns in predicate logic. In Proc. of SEFM07, pp 25–36. I. Bayley and H. Zhu. Specifying behavioural features of design patterns in first order logic. Proc. of COMPSAC08, pp203–210. L. Shan and H. Zhu. A formal descriptive semantics of UML. Proc. of ICFEM09, 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 TASE09, 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

Similar presentations

OK

Chapter 2 Entity-Relationship Data Modeling: Tools and Techniques

Chapter 2 Entity-Relationship Data Modeling: Tools and Techniques

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google

Free ppt on animals and their homes Ppt on total internal reflection experiment Ppt on office 365 Ppt on fire extinguisher types Ppt on accounting standard 12 Ppt on integrated power electronics module Ppt online education Ppt on ocean food web Ppt on pythagoras theorem for class 10 Ppt on disk formatting utility