SlideShare uma empresa Scribd logo
1 de 51
Baixar para ler offline
01




Property Specifications for
   Workflow Modelling



            Peter Wong, University of Oxford, UK
                (Joint work with Jeremy Gibbons)
                                    February 2009
02


Overview

 • Process Semantics for BPMN in CSP (ICFEM08, QSIC08)
 • Augmented with relative timing information (FOCLASA08)
 • BPMN is not a specification language
 • Consider a generalisation of Dwyer et al.’s Property
   Specification Patterns (PSP)
 • Define a language PL to capture generalised PSP
 • Translate into Lowe’s Bounded, Positive fragment of LTL (BTL)
 • Translate BTL into CSP for refinement checks (FDR)




                                                      see: Example
03


Problems
BPMN...
  • describes the performance of behaviour
  • difficult to describe refusal of behaviour in a context while
    allowing its availability outside that context


Example requirement should also permit behaviour such as
Request Cancel before Book Seat!




                                                        see: Example
04


Content

 • Property Specification Patterns (PSP)
 • Generalised PSP
 • Our Approach
 • Bounded Positive fragment of LTL (BTL)
 • Refusal Traces Model and Semantics of BTL
 • A Property Specification Language PL
    – Patterns of Behaviour
    – Property Specification
 • Revisiting the Example
05


Property Specification Patterns
PSPs describe the structure of commonly occurring requirements on
the permissible patterns of behaviour in finite states systems




Here we only consider occurrence patterns.
06


Occurrence Patterns

 • Absence – A given event does not occur within a scope
 • Universality – A given event occurs at all times within a scope
 • Existence – A given event occurs at least once within a scope
 • Bounded Existence – A given event occurs a bounded number of
   times within a scope
07


Scopes

 • Always – throughout all possible executions
 • Before p – before the occurrence of the event p
 • After q – after the occurrence of the event q
 • Between p and q – between the occurrence of the events p and q
 • After p until q – after the occurrence of the event p until q, but
   q need not happen
08


Issue

  • Patterns have been expressed in a range of formalisms
    (LTL,CTL...) but not for refinement setting (CSP).
  • We provide a generalisation of the patterns in process-algebraic
    settings
For example: The parallel execution of task A and either task D or
task E cannot happen after task B and before task C
09


Occurrence Patterns (generalised)

 • Absence – A given pattern of behaviour does not occur within a
   scope
 • Universality – A given pattern of behaviour occurs at all times
   within a scope
 • Existence – A given pattern of behaviour occurs at least once
   within a scope
 • Bounded Existence – A given pattern of behaviour occurs a
   bounded number of times within a scope
10


Scopes (generalised)

 • Always – throughout all possible executions
 • Before p – before the occurrence of the pattern of behaviour p
 • After q – after the occurrence of the pattern of behaviour q
 • Between p and q – between the occurrence of the patterns of
   behaviour p and q
 • After p until q – after the occurrence of the pattern of behaviour
   p until q, but q need not happen
11


Our Approach

 • Define a small property specification language PL to capture
   generalised PSP
 • Translate from PL to bounded, positive fragment of LTL (BTL)
 • BTL can be automatically translated into CSP for simple
   refinement checks
12


Content
√
    • Property Specification Patterns (PSP)
√
    • Generalised PSP
√
    • Our Approach
⇒ • Bounded Positive fragment of LTL (BTL)
    • Refusal Traces Model and Semantics of BTL
    • A Property Specification Language PL
      • Patterns of Behaviour
      • Property Specification
    • Revisiting the Example
13


Bounded, Positive Fragment of LTL (BTL)
The grammar of BTL (for all a ∈ Σ)

     e, f ∈ BTL ::= e ∧ f | e ∨ f | e | Pe | e R f | a | ¬a |
                    available a | true | false | live | deadlocked


  • a – the event a is available to be performed initially, and no
    other events may be performed;
  • available a – the event a must not be refused initially, and
    other events may be performed;
  • live and deadlock – the system is live (equivalent to            a)
                                                              a∈Σ
    or deadlocked (equivalent to a∈Σ ¬a), respectively;
  • true and false – logical formulae with their normal meanings.
N.B. Does not capture eventually ( ) and until (U) and negation (¬).
14


Semantics of BTL for Refinement Checks

  • Stable Failures is not suitable
  • Requires a finer model – Refusal Traces RT [Mukarram 93]
  • P |= e where e is a BTL expression, if and only if
    Spec(e) RT P where Spec(e) is the CSP specification for e.
A refusal trace is an alternating sequence of refusal information and
events, of the form

      X 1, a1, X 2, a2, . ., Xn, an, Σ

where each Xi is a refusal set, and each ai is an event: this represents
that the process can refuse X 1, perform a1, refuse X 2, perform a2,
etc.

                                          see: Failures and Eventually
15


Content
√
    • Property Specification Patterns (PSP)
√
    • Generalised PSP
√
    • Our Approach
√
    • Bounded Positive fragment of LTL (BTL)
√
    • Refusal Traces Model and Semantics of BTL
⇒ • A Property Specification Language PL
      • Patterns of Behaviour
      • Property Specification
    • Revisiting the Example
16


Patterns of Behaviour
SPL – A Sublanguage of PL

     P ∈ SPL ::= P     P |P     P | a → P | End      where a ∈ AF

              ::= e | available e | live where e ∈ Σ
     AF



  • specifies nondeterministic systems
  • introduces the nondeterministic interleaving (    ) operator
  • End has empty semantics – RT SPL [[End ]] = ∅
17


Nondeterministic Interleaving
In CSP for any events a and b:

        a → Skip    b → Skip         a → Skip
                                RT


but not

        a → Skip ||| b → Skip        a → b → Skip
                                RT


Since

        a → Skip ||| b → Skip ≡ a → b → Skip P b → a → Skip

  • Need an operator to specify concurrent behaviour without
    determining their orders
  • Especially useful when applying in our relative timed model of
    BPMN.
18


Nondeterministic Interleaving
The process P     Q communicates events from both P and Q
nondeterministically.
If P = p → P and Q = q → Q then

              Q = (p → (P         (q → (P
        P                   Q))              Q ))       [   -step]

also:

        End     Q =Q                                    [   -End]



Note   is both commutative and associative and is defined in terms
of and →.
19


From SPL to BTL

 • translate SPL to BTL∗ inductively

        pattern : SPL → BTL∗

 • BTL∗ is BTL augmented with the atomic formula ∗
   (RT SPL [[End ]] = RT BTL∗ [[ ∗ ]] = ∅)
 • convert BTL∗ back to BTL, we simply remove ∗ according to the
   following equivalences:

        φ∨∗≡φ
        ∗∧φ≡φ
        φ∧ ∗≡φ



                          see: Formalising SPL in Temporal Logic
20


Example
Given a pattern of behaviour (a → End )  (b → End ) we get the
following BTL expression φ = (a ∧ b) ∨ (b ∧ a), which can be
automatically translated into CSP:
     Spec = let
              Spec0 = b → Spec2
              Spec1 = a → Spec3
              Spec2 = a → Spec4
              Spec3 = b → Spec4
              Spec4 = Stop ( x : Σ • x → Spec4)
            in Spec0 Spec1
Moreover,
                 a → b → Stop
     Spec   RT
                 a → Stop ||| b → Stop
     Spec   RT
21


Content
√
    • Property Specification Patterns (PSP)
√
    • Generalised PSP
√
    • Our Approach
√
    • Bounded Positive fragment of LTL (BTL)
√
    • Refusal Traces Model and Semantics of BTL
⇒ • A Property Specification Language PL
   √
      • Patterns of Behaviour
     ⇒ • Property Specification
    • Revisiting the Example
22


Property Specification Language – PL
for all p ∈ SPL, n ∈ N1 , b ∈ Bound and s ∈ Scope
  • Abs(p, s) – absence of behaviour p in scope s
  • Un(p, s) – universality of behaviour p in scope s
  • Ex(p, n, s) – existence of behaviour p within subsequent n states
    from the start of scope s
  • BEx(p, b, s) – existence of behaviour p with bound b in scope s


N.B. state is in the sense of a transition system of a CSP process
describing a BPMN diagram: a graph showing the states it can go
through and actions, each denoted by a single CSP event, that it
takes to get from one to another.
23


Scopes

 • always – throughout all possible execution
 • before(p, n) – before behaviour p if p happens in nth state from
   the start.
 • after p – after of behaviour p
 • between p and (q, n) – between behaviour p and q if q happens
   in nth state after p
 • from p until (q, n) – after behaviour p and before q if q happens
   (not necessary) in nth state after p




                                            see: Bounded Existence
24


Content
√
    • Property Specification Patterns (PSP)
√
    • Generalised PSP
√
    • Our Approach
√
    • Bounded Positive fragment of LTL (BTL)
√
    • Refusal Traces Model and Semantics of BTL
√
    • A Property Specification Language PL
     √
        • Patterns of Behaviour
     √
        • Property Specification
⇒ • Revisiting the Example
25


Revisiting the Example
Use the absence pattern “the absence of p between some behaviour q
and r ”

     Abs(Cancel , between bookseat → End and(sendinvoice → End , 2))

where the pattern of behaviour Cancel is defined as follows:

     Cancel = requestcancel → End       reservetimeout → End

See if the diagram satisfies this property by checking the following
refusal traces refinement assertion using the FDR tool.

                 Agent  N
     Spec   RT

where N = Σ  { bookseat, requestcancel , reservetimeout, sendinvoice }

                             see: Travel Agent, Requirement and Spec
26


Summary

 • Generalised PSP to specify patterns of behaviour
 • Defined PL to capture the generalised PSP
 • Translated PL into BTL
 • Implemented a prototype in Haskell
27




    Thank You


Web site: http://www.comlab.ox.ac.uk/peter.wong/
                Email: peter.wong@comlab.ox.ac.uk
28


Summary
   •   Overview
   •   Problems
   •   Content
   •   Property Specification Patterns
   •   Occurrence Patterns
   •   Scopes
   •   Issue
   •   Occurrence Patterns (generalised)
   •   Scopes (generalised)
   •   Our Approach
   •   Content
   •   Bounded, Positive Fragment of LTL (BTL)
29


•   Semantics of BTL for Refinement Checks
•   Content
•   Patterns of Behaviour
•   Nondeterministic Interleaving
•   Nondeterministic Interleaving
•   From SPL to BTL
•   Example
•   Content
•   Property Specification Language – PL
•   Scopes
•   Content
•   Revisiting the Example
•   Summary
30


•   CSP
•   CSP
•   CSP
•   CSP
•   Travel Agent
•   Requirement
•   Requirement
•   Unsuitability of the stable failures
•   Impossibility of eventually , U and negation
•   Formalising SPL in Temporal logic
•   Formalising SPL in Temporal logic
•   Bounded Existence – Preliminaries
•   Bounded Existence
31


•   Bounded Existence
•   Example – “The bounded existence of p after q”
•   Corresponding CSP specification...
32


Index

    2 Overview
    3 Problems
    4 Content
    5 Property Specification Patterns
    6 Occurrence Patterns
    7 Scopes
    8 Issue
    9 Occurrence Patterns (generalised)
   10 Scopes (generalised)
   11 Our Approach
   12 Content
   13 Bounded, Positive Fragment of LTL (BTL)
33


14   Semantics of BTL for Refinement Checks
15   Content
16   Patterns of Behaviour
17   Nondeterministic Interleaving
18   Nondeterministic Interleaving
19   From SPL to BTL
20   Example
21   Content
22   Property Specification Language – PL
23   Scopes
24   Content
25   Revisiting the Example
26   Summary
34


28   Summary
29   Index
30   CSP
31   CSP
32   CSP
33   CSP
34   Travel Agent
35   Requirement
36   Requirement
37   Unsuitability of the stable failures
     Impossibility of eventually , U and negation
38
39   Formalising SPL in Temporal logic
40   Formalising SPL in Temporal logic
35


41   Bounded Existence – Preliminaries
42   Bounded Existence
43   Bounded Existence
44   Example – “The bounded existence of p after q”
45   Corresponding CSP specification...
36


CSP
The grammar of CSP.

    P , Q ::= P ||| Q | P |[ A ]| Q | P Q | P  A | P Q|
              P Q | P P Q | P o Q | e → P | Skip | Stop
                                      9




 • Skip, Stop - termination, deadlock;.
 • e → P - prefixing;
 • P o Q - sequential composition.
     9
37


CSP
The grammar of CSP.

    P , Q ::= P ||| Q | P |[ A ]| Q | P Q | P  A | P Q|
              P Q | P P Q | P o Q | e → P | Skip | Stop
                                      9




 • P ||| Q - interleaving;
 • P |[ A ]| Q - partial interleaving;
 •P     Q - parallel composition.
38


CSP
The grammar of CSP.

    P , Q ::= P ||| Q | P |[ A ]| Q | P Q | P  A | P Q|
              P Q | P P Q | P o Q | e → P | Skip | Stop
                                      9




 • P  A - hiding;
 •P     Q - interrupt;
 • P P Q - external choice.
 •P    Q - internal choice.
39


CSP
The grammar of CSP.

    P , Q ::= P ||| Q | P |[ A ]| Q | P Q | P  A | P Q|
              P Q | P P Q | P o Q | e → P | Skip | Stop
                                      9




 • We write P i : { 1 . . n } • P (i ) to denote P (1) P . . P P (n),
   similarly for operators , ||| and ;
 • Our semantic definition uses Stable Failures F;
 • Formal verification via refinement checks;
 • FDR - automated CSP model checker.
40


Travel Agent




               see: Introduction and Revisit
41


Requirement




Property : Agent must not allow cancellation after booking if invoice
is to be sent.




                             see: Introduction, Problems and Revisit
42


Requirement




Property : Agent must not allow cancellation after booking if invoice
is to be sent.
Try checking this: Agent  N       Requirement ?
                               F




                                      see: Introduction and Problems
43


Unsuitability of the stable failures

     F[[available a]] = { ( , X ) | a ∈ X } ∪ { (tr , X ) | tr =   ∧ X ∈ PΣ}
                                      /

     F[[ available a]] =
             { ( b , X ) | b ∈ Σ ∧ a ∈ X } ∪ { (tr , X ) | #tr = 1 ∧ X ∈ P Σ }
                                     /

     F[[available a ∨ available a]] =
             F[[available a]] ∪ F[[ available a]] =
                     { (tr , X ) | tr ∈ Σ∗ ∧ X ∈ P }

i.e. available a ∨   available a is satisfied by every process!




                                                 see: Semantics of BTL
44


Impossibility of eventually , U and negation
Suppose P |= a if and only if Spec RT P where Spec is the CSP
specification for a. Spec would have the refusal trace
 Σ  {b}, b n    •, a for all n. However RT is prefixed-closed
therefore it would also have the refusal trace Σ  {b}, b n for all n
and this is satisfied by the process P = b → P !




Also since φ = true U φ and φ = ¬(P¬φ)




                                                see: Semantics of BTL
45


Formalising SPL in Temporal logic
 • translate SPL to BTL∗
 • convert BTL∗ back to BTL
SPL to BTL∗
    pattern(End ) = ∗
    pattern(a → P ) = atom(a) ∧ (pattern P )
    pattern(P Q) = pattern(P ) ∨ pattern(Q)
    pattern(P     Q) = pattern(npar (P , Q))
where
    atom(available t) = available (event(t))
    atom(live) = live
    atom(t) = event(t)

                                               see: SPL
46


Formalising SPL in Temporal logic

     npar (End , End ) = End
     npar (End , Q) = Q
     npar (P , End ) = P
     npar (P , Q) = ( (a, X ) : initials(P ) • a → npar (X , Q))
                       ( (a, X ) : initials(Q) • a → npar (X , P ))

where i : I • P (i ) denotes the nondeterministic choice of a set of
indexed terms P (i ) where i ranges over I .

     initials(P Q) = initials(P ) ∪ initials(Q)
     initials(P     Q) = initials(npar (P , Q))
     initials(a → P ) = { (a, P ) }
     initials(End ) = ∅

                                                                see: SPL
47


Bounded Existence – Preliminaries
We extend BTL to BTLδ to include two new operators ¡ and U
                                                    £
such that:

     ψ Un φ = (                      nextsi∗states(ψ) (φ ∨ ψ)) ∧ nexts(n−1)∗states(ψ) φ
                      i∈{ 0..n−2 }

where
  • states(φ) returns one minus the furthest state the expression φ
                  i
  • nextsi ψ =        ψ for i ∈ N
                 n
  • nextφ ψ =         ψ where n = states(φ)
Note : ¡ n φ = true U n φ
        £
48


Bounded Existence

 • The global existence p with bound b – bound (p, false, b);
 • The existence of p with bound b before some behaviour q –
   ¡ n q ⇒ ¬q U n−getbound(b)∗states(p) bound (p, q, b)
    £
 • The existence of p with bound b after some behaviour q –
   P(q ⇒ nextq (bound (p, q, b)))
 • The existence of p with bound b between behaviour q and r –

         P(q ⇒
             (nextq ¡ n r ⇒ (bound (p, r , b) ∧ bound (p, r , b) R ¬r ∧ r R ¬q)))
                     £

   where n > getbound (b) ∗ states(p)
 • The existence of p after behaviour q until r –

         P(q ⇒ (nextq ¬r U 1 bound (p, r ∨ q, b)))
49


Bounded Existence
bound (p, q, b) is defined as follows :
  • (= n) –                                     p) ∧ nextsn∗states(p) (q R ¬p)
               i∈{ 0..n−1 } (nextsi∗states(p)

  • (≥ n) –    i∈{ 0..n−1 } (nextsi∗states(p)   p)
  • (≤ n) – nextsn∗states(p) (q R ¬p)
and getbound (b) for some bound b denotes the number part of the
value.
50


Example – “The bounded existence of p after q”
Property – Either task A or C has to occur followed by either one of
them again after Task B has occurred.

     Spec = let
              Spec0 = Proceed ({ b }, Spec0 Spec1)
              Spec1 = b → (Spec2 Spec3)
              Spec2 = c → (Spec4 Spec5)
              Spec3 = a → (Spec4 Spec5)
              Spec4 = c → (Spec6 Spec7)
              Spec5 = a → (Spec6 Spec7)
              Spec6 = Proceed ({ a, b, c }, Spec6 Spec7)
              Spec7 = b → (Spec2 Spec3)
           in Spec0 Spec1

                                       ( x : Σ  X • x → P)
where Proceed (X , P ) = Stop   Skip
51


Corresponding CSP specification...

    Spec0 = Proceed ({ bookseat }, Spec0 Spec1)
    Spec1 = bookseat → (Spec2 Spec3 Spec4 Spec5 Spec6)
    Spec2 = Proceed ({ bookseat, sendinvoice }, Spec7 Spec1)
    Spec3 = sendinvoice → (Spec0 Spec1)
    Spec4 = bookseat → (Spec2 Spec4 Spec8 Spec9)
    Spec5 = Proceed ({ bookseat, requestcancel , reservetimeout }, Spec3)
    Spec6 = bookseat → (Spec3)
    Spec7 = Proceed ({ bookseat, sendinvoice }, Spec0 Spec1)
    Spec8 = let poss = { bookseat, requestcancel , reservetimeout, sendinvoice }
             in Proceed (poss, Spec3)
    Spec9 = bookseat → (Spec3)
    Spec = Spec0 Spec1


                                                          see: Revisit

Mais conteúdo relacionado

Último

GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationMichael W. Hawkins
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEarley Information Science
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024The Digital Insurer
 
Tech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdfTech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdfhans926745
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...Martijn de Jong
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processorsdebabhi2
 
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?Antenna Manufacturer Coco
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024The Digital Insurer
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Drew Madelung
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel Araújo
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Enterprise Knowledge
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreternaman860154
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking MenDelhi Call girls
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonAnna Loughnan Colquhoun
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfEnterprise Knowledge
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 

Último (20)

GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
Tech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdfTech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdf
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 

Destaque

2024 State of Marketing Report – by Hubspot
2024 State of Marketing Report – by Hubspot2024 State of Marketing Report – by Hubspot
2024 State of Marketing Report – by HubspotMarius Sescu
 
Everything You Need To Know About ChatGPT
Everything You Need To Know About ChatGPTEverything You Need To Know About ChatGPT
Everything You Need To Know About ChatGPTExpeed Software
 
Product Design Trends in 2024 | Teenage Engineerings
Product Design Trends in 2024 | Teenage EngineeringsProduct Design Trends in 2024 | Teenage Engineerings
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
 
How Race, Age and Gender Shape Attitudes Towards Mental Health
How Race, Age and Gender Shape Attitudes Towards Mental HealthHow Race, Age and Gender Shape Attitudes Towards Mental Health
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
 
AI Trends in Creative Operations 2024 by Artwork Flow.pdf
AI Trends in Creative Operations 2024 by Artwork Flow.pdfAI Trends in Creative Operations 2024 by Artwork Flow.pdf
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
 
PEPSICO Presentation to CAGNY Conference Feb 2024
PEPSICO Presentation to CAGNY Conference Feb 2024PEPSICO Presentation to CAGNY Conference Feb 2024
PEPSICO Presentation to CAGNY Conference Feb 2024Neil Kimberley
 
Content Methodology: A Best Practices Report (Webinar)
Content Methodology: A Best Practices Report (Webinar)Content Methodology: A Best Practices Report (Webinar)
Content Methodology: A Best Practices Report (Webinar)contently
 
How to Prepare For a Successful Job Search for 2024
How to Prepare For a Successful Job Search for 2024How to Prepare For a Successful Job Search for 2024
How to Prepare For a Successful Job Search for 2024Albert Qian
 
Social Media Marketing Trends 2024 // The Global Indie Insights
Social Media Marketing Trends 2024 // The Global Indie InsightsSocial Media Marketing Trends 2024 // The Global Indie Insights
Social Media Marketing Trends 2024 // The Global Indie InsightsKurio // The Social Media Age(ncy)
 
Trends In Paid Search: Navigating The Digital Landscape In 2024
Trends In Paid Search: Navigating The Digital Landscape In 2024Trends In Paid Search: Navigating The Digital Landscape In 2024
Trends In Paid Search: Navigating The Digital Landscape In 2024Search Engine Journal
 
5 Public speaking tips from TED - Visualized summary
5 Public speaking tips from TED - Visualized summary5 Public speaking tips from TED - Visualized summary
5 Public speaking tips from TED - Visualized summarySpeakerHub
 
ChatGPT and the Future of Work - Clark Boyd
ChatGPT and the Future of Work - Clark Boyd ChatGPT and the Future of Work - Clark Boyd
ChatGPT and the Future of Work - Clark Boyd Clark Boyd
 
Getting into the tech field. what next
Getting into the tech field. what next Getting into the tech field. what next
Getting into the tech field. what next Tessa Mero
 
Google's Just Not That Into You: Understanding Core Updates & Search Intent
Google's Just Not That Into You: Understanding Core Updates & Search IntentGoogle's Just Not That Into You: Understanding Core Updates & Search Intent
Google's Just Not That Into You: Understanding Core Updates & Search IntentLily Ray
 
Time Management & Productivity - Best Practices
Time Management & Productivity -  Best PracticesTime Management & Productivity -  Best Practices
Time Management & Productivity - Best PracticesVit Horky
 
The six step guide to practical project management
The six step guide to practical project managementThe six step guide to practical project management
The six step guide to practical project managementMindGenius
 
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...RachelPearson36
 

Destaque (20)

2024 State of Marketing Report – by Hubspot
2024 State of Marketing Report – by Hubspot2024 State of Marketing Report – by Hubspot
2024 State of Marketing Report – by Hubspot
 
Everything You Need To Know About ChatGPT
Everything You Need To Know About ChatGPTEverything You Need To Know About ChatGPT
Everything You Need To Know About ChatGPT
 
Product Design Trends in 2024 | Teenage Engineerings
Product Design Trends in 2024 | Teenage EngineeringsProduct Design Trends in 2024 | Teenage Engineerings
Product Design Trends in 2024 | Teenage Engineerings
 
How Race, Age and Gender Shape Attitudes Towards Mental Health
How Race, Age and Gender Shape Attitudes Towards Mental HealthHow Race, Age and Gender Shape Attitudes Towards Mental Health
How Race, Age and Gender Shape Attitudes Towards Mental Health
 
AI Trends in Creative Operations 2024 by Artwork Flow.pdf
AI Trends in Creative Operations 2024 by Artwork Flow.pdfAI Trends in Creative Operations 2024 by Artwork Flow.pdf
AI Trends in Creative Operations 2024 by Artwork Flow.pdf
 
Skeleton Culture Code
Skeleton Culture CodeSkeleton Culture Code
Skeleton Culture Code
 
PEPSICO Presentation to CAGNY Conference Feb 2024
PEPSICO Presentation to CAGNY Conference Feb 2024PEPSICO Presentation to CAGNY Conference Feb 2024
PEPSICO Presentation to CAGNY Conference Feb 2024
 
Content Methodology: A Best Practices Report (Webinar)
Content Methodology: A Best Practices Report (Webinar)Content Methodology: A Best Practices Report (Webinar)
Content Methodology: A Best Practices Report (Webinar)
 
How to Prepare For a Successful Job Search for 2024
How to Prepare For a Successful Job Search for 2024How to Prepare For a Successful Job Search for 2024
How to Prepare For a Successful Job Search for 2024
 
Social Media Marketing Trends 2024 // The Global Indie Insights
Social Media Marketing Trends 2024 // The Global Indie InsightsSocial Media Marketing Trends 2024 // The Global Indie Insights
Social Media Marketing Trends 2024 // The Global Indie Insights
 
Trends In Paid Search: Navigating The Digital Landscape In 2024
Trends In Paid Search: Navigating The Digital Landscape In 2024Trends In Paid Search: Navigating The Digital Landscape In 2024
Trends In Paid Search: Navigating The Digital Landscape In 2024
 
5 Public speaking tips from TED - Visualized summary
5 Public speaking tips from TED - Visualized summary5 Public speaking tips from TED - Visualized summary
5 Public speaking tips from TED - Visualized summary
 
ChatGPT and the Future of Work - Clark Boyd
ChatGPT and the Future of Work - Clark Boyd ChatGPT and the Future of Work - Clark Boyd
ChatGPT and the Future of Work - Clark Boyd
 
Getting into the tech field. what next
Getting into the tech field. what next Getting into the tech field. what next
Getting into the tech field. what next
 
Google's Just Not That Into You: Understanding Core Updates & Search Intent
Google's Just Not That Into You: Understanding Core Updates & Search IntentGoogle's Just Not That Into You: Understanding Core Updates & Search Intent
Google's Just Not That Into You: Understanding Core Updates & Search Intent
 
How to have difficult conversations
How to have difficult conversations How to have difficult conversations
How to have difficult conversations
 
Introduction to Data Science
Introduction to Data ScienceIntroduction to Data Science
Introduction to Data Science
 
Time Management & Productivity - Best Practices
Time Management & Productivity -  Best PracticesTime Management & Productivity -  Best Practices
Time Management & Productivity - Best Practices
 
The six step guide to practical project management
The six step guide to practical project managementThe six step guide to practical project management
The six step guide to practical project management
 
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
 

Property Specifications for Workflow Modelling

  • 1. 01 Property Specifications for Workflow Modelling Peter Wong, University of Oxford, UK (Joint work with Jeremy Gibbons) February 2009
  • 2. 02 Overview • Process Semantics for BPMN in CSP (ICFEM08, QSIC08) • Augmented with relative timing information (FOCLASA08) • BPMN is not a specification language • Consider a generalisation of Dwyer et al.’s Property Specification Patterns (PSP) • Define a language PL to capture generalised PSP • Translate into Lowe’s Bounded, Positive fragment of LTL (BTL) • Translate BTL into CSP for refinement checks (FDR) see: Example
  • 3. 03 Problems BPMN... • describes the performance of behaviour • difficult to describe refusal of behaviour in a context while allowing its availability outside that context Example requirement should also permit behaviour such as Request Cancel before Book Seat! see: Example
  • 4. 04 Content • Property Specification Patterns (PSP) • Generalised PSP • Our Approach • Bounded Positive fragment of LTL (BTL) • Refusal Traces Model and Semantics of BTL • A Property Specification Language PL – Patterns of Behaviour – Property Specification • Revisiting the Example
  • 5. 05 Property Specification Patterns PSPs describe the structure of commonly occurring requirements on the permissible patterns of behaviour in finite states systems Here we only consider occurrence patterns.
  • 6. 06 Occurrence Patterns • Absence – A given event does not occur within a scope • Universality – A given event occurs at all times within a scope • Existence – A given event occurs at least once within a scope • Bounded Existence – A given event occurs a bounded number of times within a scope
  • 7. 07 Scopes • Always – throughout all possible executions • Before p – before the occurrence of the event p • After q – after the occurrence of the event q • Between p and q – between the occurrence of the events p and q • After p until q – after the occurrence of the event p until q, but q need not happen
  • 8. 08 Issue • Patterns have been expressed in a range of formalisms (LTL,CTL...) but not for refinement setting (CSP). • We provide a generalisation of the patterns in process-algebraic settings For example: The parallel execution of task A and either task D or task E cannot happen after task B and before task C
  • 9. 09 Occurrence Patterns (generalised) • Absence – A given pattern of behaviour does not occur within a scope • Universality – A given pattern of behaviour occurs at all times within a scope • Existence – A given pattern of behaviour occurs at least once within a scope • Bounded Existence – A given pattern of behaviour occurs a bounded number of times within a scope
  • 10. 10 Scopes (generalised) • Always – throughout all possible executions • Before p – before the occurrence of the pattern of behaviour p • After q – after the occurrence of the pattern of behaviour q • Between p and q – between the occurrence of the patterns of behaviour p and q • After p until q – after the occurrence of the pattern of behaviour p until q, but q need not happen
  • 11. 11 Our Approach • Define a small property specification language PL to capture generalised PSP • Translate from PL to bounded, positive fragment of LTL (BTL) • BTL can be automatically translated into CSP for simple refinement checks
  • 12. 12 Content √ • Property Specification Patterns (PSP) √ • Generalised PSP √ • Our Approach ⇒ • Bounded Positive fragment of LTL (BTL) • Refusal Traces Model and Semantics of BTL • A Property Specification Language PL • Patterns of Behaviour • Property Specification • Revisiting the Example
  • 13. 13 Bounded, Positive Fragment of LTL (BTL) The grammar of BTL (for all a ∈ Σ) e, f ∈ BTL ::= e ∧ f | e ∨ f | e | Pe | e R f | a | ¬a | available a | true | false | live | deadlocked • a – the event a is available to be performed initially, and no other events may be performed; • available a – the event a must not be refused initially, and other events may be performed; • live and deadlock – the system is live (equivalent to a) a∈Σ or deadlocked (equivalent to a∈Σ ¬a), respectively; • true and false – logical formulae with their normal meanings. N.B. Does not capture eventually ( ) and until (U) and negation (¬).
  • 14. 14 Semantics of BTL for Refinement Checks • Stable Failures is not suitable • Requires a finer model – Refusal Traces RT [Mukarram 93] • P |= e where e is a BTL expression, if and only if Spec(e) RT P where Spec(e) is the CSP specification for e. A refusal trace is an alternating sequence of refusal information and events, of the form X 1, a1, X 2, a2, . ., Xn, an, Σ where each Xi is a refusal set, and each ai is an event: this represents that the process can refuse X 1, perform a1, refuse X 2, perform a2, etc. see: Failures and Eventually
  • 15. 15 Content √ • Property Specification Patterns (PSP) √ • Generalised PSP √ • Our Approach √ • Bounded Positive fragment of LTL (BTL) √ • Refusal Traces Model and Semantics of BTL ⇒ • A Property Specification Language PL • Patterns of Behaviour • Property Specification • Revisiting the Example
  • 16. 16 Patterns of Behaviour SPL – A Sublanguage of PL P ∈ SPL ::= P P |P P | a → P | End where a ∈ AF ::= e | available e | live where e ∈ Σ AF • specifies nondeterministic systems • introduces the nondeterministic interleaving ( ) operator • End has empty semantics – RT SPL [[End ]] = ∅
  • 17. 17 Nondeterministic Interleaving In CSP for any events a and b: a → Skip b → Skip a → Skip RT but not a → Skip ||| b → Skip a → b → Skip RT Since a → Skip ||| b → Skip ≡ a → b → Skip P b → a → Skip • Need an operator to specify concurrent behaviour without determining their orders • Especially useful when applying in our relative timed model of BPMN.
  • 18. 18 Nondeterministic Interleaving The process P Q communicates events from both P and Q nondeterministically. If P = p → P and Q = q → Q then Q = (p → (P (q → (P P Q)) Q )) [ -step] also: End Q =Q [ -End] Note is both commutative and associative and is defined in terms of and →.
  • 19. 19 From SPL to BTL • translate SPL to BTL∗ inductively pattern : SPL → BTL∗ • BTL∗ is BTL augmented with the atomic formula ∗ (RT SPL [[End ]] = RT BTL∗ [[ ∗ ]] = ∅) • convert BTL∗ back to BTL, we simply remove ∗ according to the following equivalences: φ∨∗≡φ ∗∧φ≡φ φ∧ ∗≡φ see: Formalising SPL in Temporal Logic
  • 20. 20 Example Given a pattern of behaviour (a → End ) (b → End ) we get the following BTL expression φ = (a ∧ b) ∨ (b ∧ a), which can be automatically translated into CSP: Spec = let Spec0 = b → Spec2 Spec1 = a → Spec3 Spec2 = a → Spec4 Spec3 = b → Spec4 Spec4 = Stop ( x : Σ • x → Spec4) in Spec0 Spec1 Moreover, a → b → Stop Spec RT a → Stop ||| b → Stop Spec RT
  • 21. 21 Content √ • Property Specification Patterns (PSP) √ • Generalised PSP √ • Our Approach √ • Bounded Positive fragment of LTL (BTL) √ • Refusal Traces Model and Semantics of BTL ⇒ • A Property Specification Language PL √ • Patterns of Behaviour ⇒ • Property Specification • Revisiting the Example
  • 22. 22 Property Specification Language – PL for all p ∈ SPL, n ∈ N1 , b ∈ Bound and s ∈ Scope • Abs(p, s) – absence of behaviour p in scope s • Un(p, s) – universality of behaviour p in scope s • Ex(p, n, s) – existence of behaviour p within subsequent n states from the start of scope s • BEx(p, b, s) – existence of behaviour p with bound b in scope s N.B. state is in the sense of a transition system of a CSP process describing a BPMN diagram: a graph showing the states it can go through and actions, each denoted by a single CSP event, that it takes to get from one to another.
  • 23. 23 Scopes • always – throughout all possible execution • before(p, n) – before behaviour p if p happens in nth state from the start. • after p – after of behaviour p • between p and (q, n) – between behaviour p and q if q happens in nth state after p • from p until (q, n) – after behaviour p and before q if q happens (not necessary) in nth state after p see: Bounded Existence
  • 24. 24 Content √ • Property Specification Patterns (PSP) √ • Generalised PSP √ • Our Approach √ • Bounded Positive fragment of LTL (BTL) √ • Refusal Traces Model and Semantics of BTL √ • A Property Specification Language PL √ • Patterns of Behaviour √ • Property Specification ⇒ • Revisiting the Example
  • 25. 25 Revisiting the Example Use the absence pattern “the absence of p between some behaviour q and r ” Abs(Cancel , between bookseat → End and(sendinvoice → End , 2)) where the pattern of behaviour Cancel is defined as follows: Cancel = requestcancel → End reservetimeout → End See if the diagram satisfies this property by checking the following refusal traces refinement assertion using the FDR tool. Agent N Spec RT where N = Σ { bookseat, requestcancel , reservetimeout, sendinvoice } see: Travel Agent, Requirement and Spec
  • 26. 26 Summary • Generalised PSP to specify patterns of behaviour • Defined PL to capture the generalised PSP • Translated PL into BTL • Implemented a prototype in Haskell
  • 27. 27 Thank You Web site: http://www.comlab.ox.ac.uk/peter.wong/ Email: peter.wong@comlab.ox.ac.uk
  • 28. 28 Summary • Overview • Problems • Content • Property Specification Patterns • Occurrence Patterns • Scopes • Issue • Occurrence Patterns (generalised) • Scopes (generalised) • Our Approach • Content • Bounded, Positive Fragment of LTL (BTL)
  • 29. 29 • Semantics of BTL for Refinement Checks • Content • Patterns of Behaviour • Nondeterministic Interleaving • Nondeterministic Interleaving • From SPL to BTL • Example • Content • Property Specification Language – PL • Scopes • Content • Revisiting the Example • Summary
  • 30. 30 • CSP • CSP • CSP • CSP • Travel Agent • Requirement • Requirement • Unsuitability of the stable failures • Impossibility of eventually , U and negation • Formalising SPL in Temporal logic • Formalising SPL in Temporal logic • Bounded Existence – Preliminaries • Bounded Existence
  • 31. 31 • Bounded Existence • Example – “The bounded existence of p after q” • Corresponding CSP specification...
  • 32. 32 Index 2 Overview 3 Problems 4 Content 5 Property Specification Patterns 6 Occurrence Patterns 7 Scopes 8 Issue 9 Occurrence Patterns (generalised) 10 Scopes (generalised) 11 Our Approach 12 Content 13 Bounded, Positive Fragment of LTL (BTL)
  • 33. 33 14 Semantics of BTL for Refinement Checks 15 Content 16 Patterns of Behaviour 17 Nondeterministic Interleaving 18 Nondeterministic Interleaving 19 From SPL to BTL 20 Example 21 Content 22 Property Specification Language – PL 23 Scopes 24 Content 25 Revisiting the Example 26 Summary
  • 34. 34 28 Summary 29 Index 30 CSP 31 CSP 32 CSP 33 CSP 34 Travel Agent 35 Requirement 36 Requirement 37 Unsuitability of the stable failures Impossibility of eventually , U and negation 38 39 Formalising SPL in Temporal logic 40 Formalising SPL in Temporal logic
  • 35. 35 41 Bounded Existence – Preliminaries 42 Bounded Existence 43 Bounded Existence 44 Example – “The bounded existence of p after q” 45 Corresponding CSP specification...
  • 36. 36 CSP The grammar of CSP. P , Q ::= P ||| Q | P |[ A ]| Q | P Q | P A | P Q| P Q | P P Q | P o Q | e → P | Skip | Stop 9 • Skip, Stop - termination, deadlock;. • e → P - prefixing; • P o Q - sequential composition. 9
  • 37. 37 CSP The grammar of CSP. P , Q ::= P ||| Q | P |[ A ]| Q | P Q | P A | P Q| P Q | P P Q | P o Q | e → P | Skip | Stop 9 • P ||| Q - interleaving; • P |[ A ]| Q - partial interleaving; •P Q - parallel composition.
  • 38. 38 CSP The grammar of CSP. P , Q ::= P ||| Q | P |[ A ]| Q | P Q | P A | P Q| P Q | P P Q | P o Q | e → P | Skip | Stop 9 • P A - hiding; •P Q - interrupt; • P P Q - external choice. •P Q - internal choice.
  • 39. 39 CSP The grammar of CSP. P , Q ::= P ||| Q | P |[ A ]| Q | P Q | P A | P Q| P Q | P P Q | P o Q | e → P | Skip | Stop 9 • We write P i : { 1 . . n } • P (i ) to denote P (1) P . . P P (n), similarly for operators , ||| and ; • Our semantic definition uses Stable Failures F; • Formal verification via refinement checks; • FDR - automated CSP model checker.
  • 40. 40 Travel Agent see: Introduction and Revisit
  • 41. 41 Requirement Property : Agent must not allow cancellation after booking if invoice is to be sent. see: Introduction, Problems and Revisit
  • 42. 42 Requirement Property : Agent must not allow cancellation after booking if invoice is to be sent. Try checking this: Agent N Requirement ? F see: Introduction and Problems
  • 43. 43 Unsuitability of the stable failures F[[available a]] = { ( , X ) | a ∈ X } ∪ { (tr , X ) | tr = ∧ X ∈ PΣ} / F[[ available a]] = { ( b , X ) | b ∈ Σ ∧ a ∈ X } ∪ { (tr , X ) | #tr = 1 ∧ X ∈ P Σ } / F[[available a ∨ available a]] = F[[available a]] ∪ F[[ available a]] = { (tr , X ) | tr ∈ Σ∗ ∧ X ∈ P } i.e. available a ∨ available a is satisfied by every process! see: Semantics of BTL
  • 44. 44 Impossibility of eventually , U and negation Suppose P |= a if and only if Spec RT P where Spec is the CSP specification for a. Spec would have the refusal trace Σ {b}, b n •, a for all n. However RT is prefixed-closed therefore it would also have the refusal trace Σ {b}, b n for all n and this is satisfied by the process P = b → P ! Also since φ = true U φ and φ = ¬(P¬φ) see: Semantics of BTL
  • 45. 45 Formalising SPL in Temporal logic • translate SPL to BTL∗ • convert BTL∗ back to BTL SPL to BTL∗ pattern(End ) = ∗ pattern(a → P ) = atom(a) ∧ (pattern P ) pattern(P Q) = pattern(P ) ∨ pattern(Q) pattern(P Q) = pattern(npar (P , Q)) where atom(available t) = available (event(t)) atom(live) = live atom(t) = event(t) see: SPL
  • 46. 46 Formalising SPL in Temporal logic npar (End , End ) = End npar (End , Q) = Q npar (P , End ) = P npar (P , Q) = ( (a, X ) : initials(P ) • a → npar (X , Q)) ( (a, X ) : initials(Q) • a → npar (X , P )) where i : I • P (i ) denotes the nondeterministic choice of a set of indexed terms P (i ) where i ranges over I . initials(P Q) = initials(P ) ∪ initials(Q) initials(P Q) = initials(npar (P , Q)) initials(a → P ) = { (a, P ) } initials(End ) = ∅ see: SPL
  • 47. 47 Bounded Existence – Preliminaries We extend BTL to BTLδ to include two new operators ¡ and U £ such that: ψ Un φ = ( nextsi∗states(ψ) (φ ∨ ψ)) ∧ nexts(n−1)∗states(ψ) φ i∈{ 0..n−2 } where • states(φ) returns one minus the furthest state the expression φ i • nextsi ψ = ψ for i ∈ N n • nextφ ψ = ψ where n = states(φ) Note : ¡ n φ = true U n φ £
  • 48. 48 Bounded Existence • The global existence p with bound b – bound (p, false, b); • The existence of p with bound b before some behaviour q – ¡ n q ⇒ ¬q U n−getbound(b)∗states(p) bound (p, q, b) £ • The existence of p with bound b after some behaviour q – P(q ⇒ nextq (bound (p, q, b))) • The existence of p with bound b between behaviour q and r – P(q ⇒ (nextq ¡ n r ⇒ (bound (p, r , b) ∧ bound (p, r , b) R ¬r ∧ r R ¬q))) £ where n > getbound (b) ∗ states(p) • The existence of p after behaviour q until r – P(q ⇒ (nextq ¬r U 1 bound (p, r ∨ q, b)))
  • 49. 49 Bounded Existence bound (p, q, b) is defined as follows : • (= n) – p) ∧ nextsn∗states(p) (q R ¬p) i∈{ 0..n−1 } (nextsi∗states(p) • (≥ n) – i∈{ 0..n−1 } (nextsi∗states(p) p) • (≤ n) – nextsn∗states(p) (q R ¬p) and getbound (b) for some bound b denotes the number part of the value.
  • 50. 50 Example – “The bounded existence of p after q” Property – Either task A or C has to occur followed by either one of them again after Task B has occurred. Spec = let Spec0 = Proceed ({ b }, Spec0 Spec1) Spec1 = b → (Spec2 Spec3) Spec2 = c → (Spec4 Spec5) Spec3 = a → (Spec4 Spec5) Spec4 = c → (Spec6 Spec7) Spec5 = a → (Spec6 Spec7) Spec6 = Proceed ({ a, b, c }, Spec6 Spec7) Spec7 = b → (Spec2 Spec3) in Spec0 Spec1 ( x : Σ X • x → P) where Proceed (X , P ) = Stop Skip
  • 51. 51 Corresponding CSP specification... Spec0 = Proceed ({ bookseat }, Spec0 Spec1) Spec1 = bookseat → (Spec2 Spec3 Spec4 Spec5 Spec6) Spec2 = Proceed ({ bookseat, sendinvoice }, Spec7 Spec1) Spec3 = sendinvoice → (Spec0 Spec1) Spec4 = bookseat → (Spec2 Spec4 Spec8 Spec9) Spec5 = Proceed ({ bookseat, requestcancel , reservetimeout }, Spec3) Spec6 = bookseat → (Spec3) Spec7 = Proceed ({ bookseat, sendinvoice }, Spec0 Spec1) Spec8 = let poss = { bookseat, requestcancel , reservetimeout, sendinvoice } in Proceed (poss, Spec3) Spec9 = bookseat → (Spec3) Spec = Spec0 Spec1 see: Revisit