Rewriting Logic Model of Compositional Abstraction of Aspect-Oriented Software

1. Rewriting Logic Model of Compositional Abstraction of Aspect-Oriented Software Yasuyuki Tahara, Akihiko Ohsuga The University of Electro-Communications, Tokyo, Japan Shinichi Honiden National Institute of Informatics and The University of Tokyo, Japan FOAL '10 Mar. 15, 2010

2. Contents Backgrounds: Compositionality for AO software Research aim: Compositional abstraction of AO software Our approach Based on equational abstraction in rewriting logic Consistent with an existing state machine model Related work Conclutions and future work

3. Backgrounds Compositionality is a useful feature of software specification approaches Analysis and reasoning of the entire system can be reduced to those of the components Potential reduction of computational costs Reuse of results of analysis and reasoning Also considered important to aspect-oriented (AO) software specifications

4. Compositionality for AO Software Aspect Weaving Entire System Base System Analysis/ Reasoning Information about Aspect Compose Information about Entire System Information about Base System Both paths lead to the same information

5. Examples of Compositionality for AO Software [Jagadeesan et al. '07]: Compositional bisimilarity relation for a process calculus model of AO software Aspect 1 Weaving Base System 1 Entire System 1 Bisimilar Bisimilar Aspect 2 Weaving Base System 2 Entire System 2

6. Examples of Compositionality for AO Software [Goldman & Katz '07], [Katz & Katz '09]: Modular model checking of state machine models of AO software Aspect Weaving Base System Entire System Assume- Guarantee Reasoning Model Checking true implies true true and

7. Aim of Our Research Abstraction of AO software in a compositional way Abstraction: Building a system model (abstract model) consisting of abstract constituents obtained from the original system model (concrete model) Analysis and reasoning about the abstract model provide useful information about the concrete model efficiently

8. Compositional Abstraction of AO Software Aspect Weaving Entire System Base System Abstraction Abstraction Abstract Aspect Weaving Abstract Entire System Abstract Base System Both paths lead to the same model

9. Our Approach Try to use the model of [Katz & Katz '09] Reason: We have a simple abstraction theory for state machine models Problem: Difficult (or perhaps impossible) to show the compositionality of abstraction

10. Our Approach Solution: Use the equational abstraction theory [Meseguer et al. '08] Based on an algebraic specification framework called rewriting logic Easy to build compositional models Extension of state machine abstraction

11. Our Approach Step 1: Build a rewriting logic model extending the state machine model of aspects In fact, this model is more generic than state machine For example, it can represent operational semantics of programming languages in detail Step 2: Show compositionality of equational abstraction of the model built in Step 1

12. Our Approach Property State machine model Abstraction + Aspects Mapping Aspect model Mapping Property Rewriting logic Equational abstraction (Our original contributions)

13. Our Approach Property State machine model Abstraction + Aspects Mapping Aspect model Mapping Property Rewriting logic Equational abstraction (Our original contributions)

14. State Machine Model A (finite) state machine M is a tuple (SM , S0M, →M , LM ) where SM is the finite set of states S0M (⊆ SM ) is the set of initial states →M (⊆ SM × SM ) is the transition relation This needs to be total, i. e. there is at least one transition from each state

15. State Machine Model (Continued from the definition of the state machine M ) LM : SM → 2AP is the labeling function on the finite set of atomic propositionsAP “p ∈ LM (s )” means that the proposition p holds at the state s For a temporal logic (such as CTL*) proposition Φ, the satisfaction relation “M |=Φ ” is defined

16. Example of State Machine (Taken from [Goldman & Katz '07]) ({s1, s2}, {s1}, {(s1, s1), (s1, s2), (s2, s2), (s2, s1)}, L ) L(s1) = {a }, L(s2) = {b } s1 s2 {a } {b } a holds at s1 and b does not b holds at s2 and a does not

17. Abstraction of State Machines A state machine M ' is an abstraction of M if and only if we have a surjective mapping (called an abstraction mapping) SM '→ SM consistent with the other constructs Theorem: For any proposition Φ of a temporal logic system called ACTL, M |= Φ implies M ' |= Φ

18. Our Approach Property State machine model Abstraction + Aspects Mapping Aspect model Mapping Property Rewriting logic Equational abstraction (Our original contributions)

19. State Machine Model of Aspects An aspect machineA is a tuple (SA , S0A,→A , LA ) defined similarly as state machines except →A needs not to be total The set of states without outgoing transitions is written as SretA (⊆ SA) and its elements are called return states

20. Example of Aspect Machine (Taken from [Goldman & Katz '07] and modified) ({s3, s4, s5}, {s3}, {(s3, s4), (s4, s5)}, L ) L(s3) = {a, b }, L(s4) = {}, L(s5) = {b } s3 s4 {a } {} {b } s5

21. State Machine Model of Aspects A label is a subset of AP The label of a paths1... sn of M (i. e. si →Msi+1 for each i = 1, ..., n -1) is the sequence of labels LM (s1)... LM (sn ) written as label (s1... sn ) label (s1s2s1) = {a}{b}{a} s1 s2 label (s1s2s2s1) = {a}{b}{b}{a} {a } {b }

22. State Machine Model of Aspects A pointcut descriptorρ over AP is a predicate on a finite sequence of labels ρ : (2AP )* → {true, false} where X * represents the set of finite sequences of elements of X

23. State Machine Model of Aspects Pointcut-ready machine for a state machine B and a pointcut descriptor ρ is a state machine B ρsatisfying the following conditions SB⊆ SB ρ A new atomic proposition pointcutholds at a state s ∈ SB ρ if and only if there is a path s1... sn where s1∈ S0B ρ, sn = s, and ρ (label (s1... sn )) is true “New” means that ￢(pointcut ∈ AP )

24. State Machine Model of Aspects (Continued from the definition of the pointcut-ready machine B ρ ) Each infinite path of B or B ρ have its counterpart in the other machine that is mapped by the function “label ” to the same label except pointcut B and B ρ are trace equivalent w. r. t. their labeling functions

25. Example of Pointcut-Ready Machine (Taken from [Goldman & Katz '07]) ρ (l ) is true if and only if l ends withthree labels including “b ”, “b ”, and “a ” respectively B s1 s2 {a } {b } B ρ {a }{b }{b }{a } s1 s2 {a } {b } {a, pointcut } s6 s7

26. State Machine Model of Aspects The augmented machineB obtained from a pointcut-ready machine B ρ and an aspect machine A is created as follows The state set and the labeling function of B are the unions of B ρ and A The initial states of B are the initial states of B ρ ~ ~ ~

27. State Machine Model of Aspects (Continued from the definition of the augmented machine B ) The transitions of B consist of the following Most of the transitions of B ρ and A New transitions connecting B ρ and A The details are shown in the next slide ~ ~

28. Example of Augmented Machine s1 s2 B ρ {a } {b } {a, pointcut} s6 s7 s3 s4 No outgoing transitions {a } {} A {b } s5

29. Example of Augmented Machine s1 s2 B ρ {a } {b } {a, pointcut} s6 s7 s3 s4 The same label except pointcut {a } {} A {b } s5

30. Example of Augmented Machine s1 s2 B ρ {a } {b } {a, pointcut} s6 s7 s3 s4 {a } {} A {b } s5

31. Example of Augmented Machine s1 s2 B ρ {a } {b } {a, pointcut } s6 s7 s3 s4 The same label with the return states {a } {} A {b } s5

32. Example of Augmented Machine s1 s2 B ρ {a } {b } {a, pointcut } s6 s7 s3 s4 {a } {} A {b } s5

33. Our Approach Property State machine model Abstraction + Aspects Mapping Aspect model Mapping Property Rewriting logic Equational abstraction (Our original contributions)

34. Rewriting Logic Extension of equational logic Equational logic A formula is an equality of terms A term is composed by constant, variable, and operator symbols Equalities are derived from axioms (equations) and inference rules

35. Examples in Equational Logic f(x, a), pop(push(a, push(b, empty))): examples of terms a, b, empty: constant symbols x: a variable symbol f, pop, push: operator symbols The word “symbol(s)” will be omitted hereafter

36. Examples in Equational Logic Replacement inference rule For terms s1 and s2 that may contain variables x1, ..., xn, and terms t1, ..., tn, s1 = s2 implies s1([t1/x1], ..., [tn /xn ]) = s2([t1/x1], ..., [tn /xn ]) where ([t1/x1], ..., [tn /xn ]) represents simultaneous substitutions of x1, ..., xn to t1, ..., tn

37. Examples in Equational Logic Equation “pop(push(x, s)) = s” derives an equality pop(push(a, push(b, empty))) = push(b, empty) by the Replacement inference rule

38. Rewriting Logic Equational logic + rewriting relation Represented by an arrow: s → t Rewrite rules: axioms for the rewriting relation Inference rules similar as equational logic Except the Symmetry rule (x = y implies y = x )

39. Our Approach Property State machine model Abstraction + Aspects Mapping Aspect model Mapping Property Rewriting logic Equational abstraction (Our original contributions)

40. Mapping State Machines to Rewriting Logic States, atomic propositions → Constants Transitions → Rewrite rules for states Labeling function → Operators Mapping a pair (state, atomic proposition) to a boolean value

41. Mapping State Machines to Rewriting Logic An example Constants: s1, s2, a, b operators: init, _|=_ _|=_(s, p) is also written as (s |= p ) Rewrite rules: s1 → s1, s1 → s2, s2 → s2, s2 → s1 Equations: init(s1) = true, (s2 |= a) = false, etc. s1 s2 {a } {b }

42. Mapping Rewriting Logic to State Machines Equivalence classes of terms → States One-step rewriting relations → Transitions “One-step”: Not using the Transitivity inference rule (s → t and t → u implies s → u ) (Other constructs are given in advance)

43. Our Approach Property State machine model Abstraction + Aspects Mapping Aspect model Mapping Property Rewriting logic Equational abstraction (Our original contributions)

44. Equational Abstraction For an axiomatic system of rewriting logic (called a rewrite theory) R, K (R ) represents the state machine created from R Theorem: If E is a set of equations for the terms of R above satisfying some properties, K (R ∪ E ) is an abstraction of K (R ) Abstraction mapping: [t ]R is mapped to [t ]R ∪ E where [t ]... represents the equivalence class

45. Our Approach Property State machine model Abstraction + Aspects Mapping Aspect model Mapping Property Rewriting logic Equational abstraction (Our original contributions)

46. Aspectual Rewrite Theory (ART) An ART is a rewrite theory in which States and transitions of all of the base system and the aspects are treated as constants and rewrite rules resp. Constructs for state sequences are included ts denotes a sequence where “s ” is the last state succeeding the sequence “t ” Treated as execution traces

47. Aspectual Rewrite Theory (ART) (Continued from the definition of ARTs) For a base system state sb and an aspect state sa as(tsb , sa ) = true if and only if sa can be the next state of sbwhen the pointcut of the aspect matches the trace tsb rstrt(sa , sb) = true if and only if sa is a terminal state of its aspect and sb can be its next state “as” and “rstrt” stands for “aspect selection” and “restart” respectively

48. Example of ART Consider the rewrite theory created from these state and aspect machines s1 s2 rstrt(s1, s3) = true {a } {b } s3 s4 as(s1s2s2s1, s3) = true {a } {} {b } s5

49. Creating an Augmented ART An augmented ART (AART) R+ is obtained from an ART R as follows Transformation: A rewrite rule for the state terms of Rs → s' → A rewrite rule for the state sequences in R+ ts →tss' Add ts →tss' if as(s, s') = true or rstrt(s, s') = true ts tss' s s s' t

50. Example of AART Consider the rewrite theory created from these state and aspect machines s1 s2 {a } {b } s3 s4 as(s1s2s2s1, s3) = true {a } {} {b } s5