Dynamic Backward Slicing of Model Transformations

Budapest University of Technology and Economics Department of Measurement and Informa<on Systems

Dynamic Backward Slicing of Model Transforma7ons

Zoltán Ujhelyi, Ákos Horváth, Dániel Varró

Models and Transforma7ons in Cri7cal Systems

System Design Model

Architecture Design Model

Component Design Model

Refine

Refine


System Design Model



Refine

Refine

Simula7on

Simula7on

Simula7on


System Design Model



Refine

Refine

Component V&V Model

Architecture V&V Model

System V&V Model

Model genera7on

Back-‐Annota7on

Model genera7on

Back-‐Annota7on Model genera7on

Back-‐Annota7on

Use

Use

Horizontal Model Transforma7ons

Formal methods

Formal methods

Simula7on

Simula7on

Simula7on


System Design Model



Refine

Refine

Design + V&V Ar7facts (Source code, Glue code,

Config. Tables, Test Cases, Monitors, Fault Trees, etc.)

Code Genera7on

Test Genera7on

Ver7cal Model Transform

a7ons

Component V&V Model


System V&V Model

Model genera7on

Back-‐Annota7on

Model genera7on


Back-‐Annota7on

Use

Use


Formal methods

Formal methods

Simula7on

Simula7on

Simula7on


System Design Model



Refine

Refine

Model Transforma7ons •  systema7c founda7on of knowledge transfer: theore7cal resultstools •  bridge / integrate exis7ng languages&tools

Design + V&V Ar7facts (Source code, Glue code,

Config. Tables, Test Cases, Monitors, Fault Trees, etc.)

Code Genera7on

Test Genera7on

Ver7cal Model Transform

a7ons

Component V&V Model


System V&V Model

Model genera7on

Back-‐Annota7on

Model genera7on


Back-‐Annota7on

Use

Use


Formal methods

Formal methods

Simula7on

Simula7on

Simula7on

Overview of Model Transforma7ons

Modeling tool

Source model

Source metamodel

Target model

Target metamodel MT rule


Modeling tool

Source model

Source metamodel

Target model


Model: Description of a concrete system


Modeling tool

Source model

Source metamodel

Target model


Metamodel: Precise spec of a modeling language



Modeling tool

Source model

Source metamodel

Target model


Metamodel: Precise spec of a modeling language


Model Transformation: How to generate a target equivalent of an arbitrary source model

Overview of Model Transforma7ons Eclipse Framework

Model Transforma7on Tool

Modeling tool

Source model

Source metamodel

Target model


MT engine



Modeling tool

Source model

Source metamodel

Target model


MT engine

Transformation engine: Support for querying and manipulating large models



Modeling tool

Source model

Source metamodel

Target model


MT engine

Transformation engine: Support for querying and manipulating large models

VIATRA2: hYp://www.eclipse.org/gmt/VIATRA2/

ATL

QVT

Demonstra7ng example: Petri net Simula7on

  Petri nets o Used e.g. in component design analysis

o Places and Transi7ons o State of the net •  Tokens

  Simulator transforma7on o In-‐place transforma7on (source and target metamodels are iden7cal)

o Uses both impera7ve and declara7ve elements

14

Client

Store

Server

Query

Respond



Simula<on

Graph transforma7on rules

15

Client

Store

Server

Query

Respond

removeToken rule

LHS RHS


16

Client

Store

Server

Query

Respond

removeToken rule

LHS RHS


17

Client

Store

Server

Query

Respond

removeToken rule

LHS RHS


18

Client

Store

Server

Query

Respond

removeToken rule

LHS RHS


19

Client

Store

Server

Query

Respond

removeToken rule

LHS RHS


20

Client

Store

Server

Query

Respond

removeToken rule

LHS RHS


21

Client

Store

Server

Query

Respond

removeToken rule

LHS RHS

Control Structure rule fireTransition(in T) = { if (find isFireable(T)) { // remove tokens from source places forall Pl with find sourcePlace(T,Pl) do apply removeToken(Pl); // creates tokens on target places forall Pl with find targetPlace(T,Pl) do apply addToken(Pl); } }

22

Debugging model transforma7ons

• Why is a certain statement executed? • Why is a certain model element created?

Basic debugging ques7ons

• Hidden data dependencies • Declara7ve rules

Finding dependencies

23

Debugging model transforma7ons

• Why is a certain statement executed? • Why is a certain model element created?

Basic debugging ques7ons

• Hidden data dependencies • Declara7ve rules

Finding dependencies

24

‘Dynamic backward slicing’ problem

Program Slicing   Dependency analysis   Use cases

o  Debugging solware o  Impact analysis o  ...

  Types of slicing o  Dynamic/sta7c o  Backward/Forward

Example 1.  int i; 2.  int sum = 0; 3.  int product = 1; 4.  for(i = 1; i < N; ++i) { 5.  sum = sum + i; 6.  product = product *i; 7.  } 8.  write(sum); 9.  write(product);

25





26

Slicing criterion: a statement and a variable





27

Slice: statements the criterion depends on





28

Slice: statements the criterion depends on

Slicing Model Transforma7ons   Similar to program slicing   BUT: processing (input) models is required

  Our approach:

29

Model transforma7on

slicing

Transforma7on

Models

Transforma7on Slice

Model Slice

Criterion

Transforma7on Program

Input model

rule fireTransition(in T) = { if (find isFireable(T)) { // remove tokens from source places forall Pl with find sourcePlace(T,Pl) do apply removeToken(Pl); // creates tokens on target places forall Pl with find targetPlace(T,Pl) do apply addToken(Pl); } }

Approach

30


Input model 1. Execu7ng the

Transforma7on


Approach

31


Input model 1. Execu7ng the

Transforma7on


Approach

32

Output model


Input model

2. Genera7ng an Execu7on Trace

1. Execu7ng the Transforma7on


Approach

33

Output model


Input model




For each statement record: •  Variable Usage •  Model Usage •  Model Manipula7on

Approach

34

Output model


Input model




Approach

35

Output model


Input model



3. Calcula7ng the Slice


Approach

36

Output model


Input model





Approach

37

Output model

Criteria: Model element


Input model





Criteria: Program statement (and a variable)

Approach

38

Output model

Criteria: Model element


Input model





Approach

39

Output model


Input model





•  Traversing Trace Records Backwards •  Selec7ng Dependent Trace Records

Approach

40

Output model


Input model





Approach

41

Output model


Input model





Approach

42

Output model


Input model





Approach

43

Output model

Sliced model

Slicing Model Transforma7ons – An Example

44


Client

Server

Query

Respond

Store

Criteria: rule call addToken, variable Pl

Lookup:

(1) (2)

(3) (4) (5)

(6) (7) (9) (8) (10)

Code Instance model

Trace Records:

10. apply addToken(Pl) Uses variable: Pl Uses model: Server Modifies model: Token


45


Client

Server

Query

Respond

Store


Lookup:

(1) (2)

(3) (4) (5)

(6) (7) (9) (8) (10)

Code Instance model

Trace Records:

10. apply addToken(Pl) Uses variable: Pl Uses model: Server Modifies model: Token


46


Client

Server

Query

Respond

Store


Lookup: variable Pl

(1) (2)

(3) (4) (5)

(6) (7) (9) (8) (10)

Code Instance model

Trace Records:

9. find targetPlace(T,Pl) Modifies variable: Pl Uses variable: T Uses model: Server, Query


47


Client

Server

Query

Respond

Store


Lookup: variable Pl

(1) (2)

(3) (4) (5)

(6) (7) (9) (8) (10)

Code Instance model

Trace Records:

9. find targetPlace(T,Pl) Modifies variable: Pl Uses variable: T Uses model: Server, Query


48


Client

Server

Query

Respond

Store


Lookup:

variable T

(1) (2)

(3) (4) (5)

(6) (7) (9) (8) (10)

Code Instance model

Trace Records:

Data/model dependency


49


Client

Server

Query

Respond

8. apply addToken(Pl) Uses variable: Pl Uses model: Store Modifies model: Token

Store


Lookup:

variable T

(1) (2)

(3) (4) (5)

(6) (7) (9) (8) (10)

Code Instance model

Trace Records:


50


Client

Server

Query

Respond

7. find targetPlace(T,Pl) Modifies variable: Pl Uses variable: T Uses model: Store, Query

Store


Lookup:

variable T

(1) (2)

(3) (4) (5)

(6) (7) (9) (8) (10)

Code Instance model

Trace Records:


51


Client

Server

Query

Respond

6. forall Pl

Store


Lookup:

variable T

(1) (2)

(3) (4) (5)

(6) (7) (9) (8) (10)

Code Instance model

Trace Records:


52


Client

Server

Query

Respond

6. forall Pl

Store


Lookup:

variable T

(1) (2)

(3) (4) (5)

(6) (7) (9) (8) (10)

Code Instance model

Trace Records:

Control dependency


53


Client

Server

Query

Respond

5. apply removeToken(Pl) Uses variable: Pl Uses model: Client Removes model: Token

Store


Lookup:

variable T

(1) (2)

(3) (4) (5)

(6) (7) (9) (8) (10)

Code Instance model

Trace Records:


54


Client

Server

Query

Respond

4. find sourcePlace(T,Pl) Modifies variable: Pl Uses variable: T Uses model: Client, Query

Store


Lookup:

variable T

(1) (2)

(3) (4) (5)

(6) (7) (9) (8) (10)

Code Instance model

Trace Records:


55


Client

Server

Query

Respond

3. forall Pl

Store


Lookup:

variable T

(1) (2)

(3) (4) (5)

(6) (7) (9) (8) (10)

Code Instance model

Trace Records:


56


Client

Server

Query

Respond

2. if (find isFireable(T)) Uses variable: T Uses model: Client, Query

Store


Lookup:

variable T

(1) (2)

(3) (4) (5)

(6) (7) (9) (8) (10)

Code Instance model

Trace Records:


57


Client

Server

Query

Respond

2. if (find isFireable(T)) Uses variable: T Uses model: Client, Query

Store


Lookup:

variable T

(1) (2)

(3) (4) (5)

(6) (7) (9) (8) (10)

Code Instance model

Trace Records:

Control dependency


58


Client

Server

Query

Respond

1. rule fireTransition(in T) Modifies: variable T

Store


Lookup:

variable T

(1) (2)

(3) (4) (5)

(6) (7) (9) (8) (10)

Code Instance model

Trace Records:


59


Client

Server

Query

Respond

1. rule fireTransition(in T) Modifies: variable T

Store


Lookup:

(1) (2)

(3) (4) (5)

(6) (7) (9) (8) (10)

Code Instance model

Trace Records:

Data/model dependency

Implementa7on Architecture Model Transforma7on Tool

Source model

Source metamodel

Target model

Target metamodel MT program

MT engine


Source model

Source metamodel

Target model


MT engine

Execu7on Trace


Source model

Source metamodel

Target model


MT engine

Execu7on Trace

Trace metamodel defined for adaptability


Source model

Source metamodel

Target model


MT engine

Execu7on Trace


Source model

Source metamodel

Target model


MT engine

Execu7on Trace

Slicing algorithm


Source model

Source metamodel

Target model


MT engine

Execu7on Trace

Slicing algorithm Model Slice


Source model

Source metamodel

Target model


MT engine

Execu7on Trace

Slicing algorithm Model Slice

Program Slice

Case Studies

Petri net simula7on • Simple transforma7on, large models

Antworld Case Study • Complex simula7on • Ever-‐growing models

BPEL2SAL transforma7on • Complex impera7ve control structure

Reengineering Case Study • Model Extrac7on

67

Evalua7on

Measurements: Size of

• Model slice • Transforma7on slice

Manually selected slicing criteria

• Mul7ple for each transforma7on

• Covering all phases of the program

Ques7ons:

•  Independence of model slice from selected statement

•  Independence of program slice from model instances

68

Evalua7on

Measurements: Size of

• Model slice • Transforma7on slice

Manually selected slicing criteria

• Mul7ple for each transforma7on

• Covering all phases of the program

Ques7ons:

•  Independence of model slice from selected statement

•  Independence of program slice from model instances

69

Data-‐driven transforma7ons or more impera7ve style

Lessons Learned (Evalua7on of Case Studies)

Petri net simula7on • Transforma7on slice largely model independent •  Sta7c slices would be similar

Antworld Case Study • Transforma7on slices more model-‐dependent

BPEL2SAL transforma7on • Well-‐separated phases •  Few dependencies between •  Small slices

Reengineering Case Study • Many dependencies between phases

•  Ignored model elements missing from trace and slice •  Small slices

70

Conclusion

• Models and transforma7on simultaneously

Dynamic backward slicing of model transforma7ons

•  Various transforma7on case studies •  Using the VIATRA2 transforma7on framework

Implementa7on and evalua7on

•  Transforma7on debugging support •  Por7ng to other transforma7ons tools •  Back-‐annota7on between trace and concrete syntax necessary

Future plans

71

Dynamic Backward Slicing of Model Transformations

Technology

Transcript of Dynamic Backward Slicing of Model Transformations