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Runtime Verification and

Software Fault Protectionwith Eagle

Allen GoldbergKlaus Havelund

Kestrel TechnologyPalo Alto, USA

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Overview

• Run-time Monitoring• About EAGLE • Software Fault Protection• Summary

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Motivation for Runtime Verification

• Model checking and Theorem Proving are rigorous, but:• Not fully automated:

– model creation is often manual– Abstraction is often manual in the case of model checking– Lemma discovery is often manual in the case of theorem proving

• Therefore: not very scalable

• Applied Testing is scalable and widely used, but is ad hoc:• Lack of formal coverage• Lack of formal conformance checking

• Combine Formal Methods and Testing

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Run-time Verification

• Combine temporal logic specification and testing:• Specify properties in some temporal logic.• Instrument program to generate events.• Monitor properties against a trace of events

emitted by the running program.

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test & property

generation

event streamImplemented

system

under test

instrumentation

disp

atch

Observer

Model

test

inputs

behavioral

properties

reports

A Model-Based Verification Architecture

Rqmts

Deadlock

Dataraces

instrumentation

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Static and Dynamic Analysis

Program

Specification

Input Output

Test case generation Runtime verification

Program instrumentation

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So many logics …

• What is the most basic, yet, general specification language suitable for monitoring?

EAGLE is our answer.

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Logics

• Assertions (Java 1.4)• Pre-post conditions, invariants (Eiffel, JML)• Temporal logic (Mac, Temporal Rover)• Time lines (Smith, Dillon)• Live Sequence Charts (Harel)• Automata (Buchi, Alternating, Timed)• Regular expressions (Rosu, Lee)• Process algebra (Jass)• Mathematical modeling/programming languages (Alloy, Prolog, Maude, Haskell, Specware)

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Eagle’s Core Concepts

• Finite trace monitoring logic • Prop. logic + Three temporal

connectives: • Next: @F• Previous: #F• Concatenation: F1;F2

• Recursive parameterized rules : Always(Term t) = t /\ @Always(t) .

@F#Fnow

F1 F2

Each state is a environment

mapping variables to values

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Introducing EAGLE

Can encode: • future time temporal logic • past-time logic • extended regular expressions • µ-calculus • real-time, data-binding, statistics….

• Monitoring formulas are evaluated online over a given input trace, on a state by state basis• identify failure as early as reasonably possible

• Evaluation proceeds by checking facts about the past and generating obligations about the future.

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Basic Propositional Example

// LTL rules:

max Alws(Term t) = t /\ @ Alws(t) .

min Even(Term t) = t \/ @ Even(t) .

min Prev(Term t) = t \/ # Prev(t) .

// Monitor:

Mon M = Alws(y>0 -> (Prev(x>0) /\ Even(z>0))) .

library

y>0x>0 z>0… …

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Data Bindings

min R(int k) = Prev(x==k) /\ Even(z == k) .

// Monitor:

mon M = Alws(y>0 -> R(y)) .

mon Wrong = Alws(y>0 -> (Prev(x==y) /\ Even(z==y))) .

y>0 z==kx==k

k := y

… …

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Time is Just Data

min Before(long t, Term F) = clock <= t /\ (F \/ @ Before(t,F)) .

min Within(long t, Term F) = Before(t+clock, F) .

min Before(long t, Term F) = clock <= t /\ (F \/ @ Before(t,F)) .

min Within(long t, Term F) = Before(t+clock, F) .

x>0 y>0 x>0 y>0

mon M = Alws(x>0 -> Within(4,y>0)) .

< 4 < 4

clock is a variable defined in the state

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Automata

open

closeS1 S2

access

init

max S1() = init -> @ S1)( \/ open -> @ S2 )(

(~ \/ access \/ close. )min S2() = access -> @ S2)(

\/ close -> @ S1 )((~ \/ init \/ open. )

mon M = S1. )(

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Timed Automata

open

closeS1 S2

accessinit

max S1() = init -> @ S1)( \/ open -> @ S2(clock)

(~ \/ access \/ close. )min S2(long t) = clock <= t+10

\/ access -> @ S2)( \/ close -> @ S1 )(

(~ \/ init \/ open. )

x := 0

x < 10

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Combining Automatae and Temporal Logic

open /\ Prev(init)

closeS1 S2

access

init

max S1() = init -> @ S1)( \/ open -> (Prev(init) /\ @ S2())

(~ \/ access \/ close. )min S2() = access -> @ S2)(

\/ close -> @ S1 )((~ \/ init \/ open. )

mon M = S1. )(

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EAGLE by example: statistical logics

Monitor that state property F holds with at least probability p:

max Empty() = false .min Last() = @Empty() .

min A(Term F, float p, int f, int t) = ( Last() /\ (( F /\ (1 – f/t) >= p) \/

(¬F /\ (1 – (f+1)/t) >= p)) ) \/

(¬Last() /\ (( F → @A(F, p, f, t+1)) /\ (¬F → @A(F, p, f+1, t+1))) .

mon AtLeast (Term F, float p) = A(F, p, 0, 1) .

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Regular Expressions

M = (idle*;open;access*;close)*

Property:File accesses are always enclosed by open and close operations.

mon M = S(S(idle());open();S(access());close()) .

max S(Term t) = t /\ @ S(t) . // Starmin P(Term t) = t /\ @ P(t) . // Plus

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Grammars

// Match rule:

max Match (Term l, Term r) =Empty() \/ (l;Match(l,r);r;Match(l,r)) .

// Monitor:

mon M = Match(lock(),release()) .

Property:Locks are acquired and released nested.

lock release

lock lock

release release

lock release

lock lock

release release

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EAGLE by example: beyond regular

Monitor a sequence of login and logout events – at no point should there be more logouts than logins and they should match at the end of the trace.

min Match (Term F1, Term F2) =Empty() \/

F1 ; Match(F1, F2) ; F2 ; Match(F1, F2)

mon M1 = Match(login, logout)

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Syntax

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Semantics

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Some EAGLE facts

• EAGLE-LTL (past and future). Monitoring formula of size m has space complexity bounded by m2 2m log m

• EAGLE with data binding has worst case dependent on length of input trace

• EAGLE without data is at least Context Free

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EAGLE interface

class Observer {

Monitors mons;

State state;

eventHandler(Event e){

state.update(e);

mons.apply(state);

}

}

class MyState extends EagleState{ int x,y; update(Event e){ x = e.x; y = e.y; }}

class Monitors {

Formula M1, M2;

apply(State s){

M1.apply(s);

M2.apply(s); }

}

class Event {

int x,y;

}

e1 e2 e3 …

User definesthese

classes

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Eagle Implementation

• Built an initial prototype implementation and used it for a number of applications• test case monitoring scenario• monitoring the behavior of a planning system

• High performance algorithm• pay for what you use

– e.g. state machine

• symbolic manipulation -> automata-like solutions

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Online Algorithm

• Start with the initial formula M to monitor• “see” a new state• Transform M to M’ so that

M is valid on the whole trace

iff

M’ is valid on the remaining trace

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Basic Algorithm: Future Time LTL

max Alws(Term t) = t /\ @ Alws(t) .min Even(Term t) = t \/ @ Even(t) .mon M = Alws(y>0 -> Even(z>0))) .M0 = Alws(y>0 -> Even(z>0)))

M1 = Alws(y>0 -> Even(z>0)))M2 = Alws(y>0 -> Even(z>0))) /\ Even(z>0)M3 = Alws(y>0 -> Even(z>0))) /\ Even(z>0)M4 = Alws(y>0 -> Even(z>0)))M5 = Alws(y>0 -> Even(z>0))) /\ Even(z>0)

y=2z=0

y=-1z=0

y=-1z=0

y=-1z=2

y=2z=0

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Toward an Algorithm

The previous example suggests how monitoring may be reduced to state machine execution• For propositional future time LTL by employing

normal forms and simplification, the set of derivable monitors is finite. This is the idea behind generating a Buchi automata for model checking propositional future time LTL.

• However Eagle has,• Past time operators• Data parameters

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Past Time

max Alws(Term t) = t /\ @ Alws(t) . min Prev(Term t) = t \/ # Prev(t) .

mon M = Alws(y>0 -> (Prev(x>0))) .

Cache: C = Prev(x>0)

C0 = false M0 = Alws(y>0 -> (Prev(x>0)))

c1 = false M1 = Alws(y>0 -> (Prev(x>0)))

c2 = true M2 = false

y=2x=2

y=-1x=0

y=-1x=0

y=-1x=2

y=2x=0

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Algorithm with Past Time

• Static Analysis• determine what formulas are needed in cache

• Evaluate formulas in cache in state 0• for each state:

• Evaluate monitors, referring to cache to look up values of formulas headed by #

• update the cache

• End of trace• evaluate monitor for “beyond the last state”

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Improvements

• Conjunctive normal form• Subsumption: (a \/ b) /\ a => a• Term caching

• assign a unique id to each normalized term• Define various caches of the form

cache:TermId -> TermId– subsumption cache– rule application cache

• Normalization folded into evaluation• Ordering evaluation of conjuncts and disjuncts

• Terms which do not (hereditarily) contain instances of the @ operator will fully evaluate to true/false.

• Evaluate such terms first

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Improvements : Automata Construction

• On the fly automata construction• for terms that are conjunctions, disjunctions

or rule applications dynamically create a state machine (decision tree) for evaluation.

• map termId -> Automata

x>0

z==ytrue

w==y

result is termID1

?

falseresult is termID2

?

true

true

falsefalse

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Improvements: Reflection Removal

• Removal of use of reflection for Java expression evaluation• Alws(y>0 -> (Prev(x>0))• static boolean greater(int a, int b)• Defined in user-defined State class• Generate during static analysis an interface

class that dispatches on term id of methodCall terms to directly call the user-supplied method

– all arguments must be available, otherwise it must be symbolically evaluated

– must treat substitution instances

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On the way to…

• An Eagle compiler• static analysis of monitor specification to

generate an alternating automata with no term manipulation and no use of reflection

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Instrument Specification

• An instrument specification is a collection of rules lhs rhs• LHS conjunction of syntactic conditions on a program

point (local conditions only)• RHS set of actions that log reporting events

• Each bytecode “statement” is examined to see if it satisfies RHS. Actions of all matching LHS are collected and inserted into the bytecode at that “point”.

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Predicates

Predicate Name Parameters Returns true iff current statement:

InMethod StringmethodNameSpec

is within method with name that matches methodNameSpec.

AtMethodStart StringmethodNameSpec

is the first statement within method with name that matches method-NameSpec.

AtFieldAccess StringfieldNameSpecBooleanonlyUpdates

accesses a field who’s name matches fieldNameSpec. If onlyUpdates is true, only write accesses are matched.

AtSyncStart enters a synchronized block, taking a lock.

AtStatementType String stmtType Is a statement of type stmtType, which ranges over 32 different statement types, such as: ”assign”,”break”, ”return”, ”try”, ”throw”, ”while”, etc.

InStatementRange int lowerBound int upperBound

is within the range of line numbers lowerBound and upperBound

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Reportable Events

ActionName

Parameters Inserts code that reports:

ReportMethedStart String methodNameSpec

name of method entered, who’s name matches ethodNameSpec.

ReportField String fieldNameSpe boolean onlyUpdates

name of field that is accessed, who’s name matches fieldNameSpec. If onlyUpdates is true, only write accesses are matched.

ReportLocal String varNameSpecboolean onlyUpdates

name of local variable that is accessed, who’s name matches var-NameSpec. If onlyUpdates is true,only write accesses are matched.

ReportSyncStart String classNameSpec identity and class (name) of object that is locked, where the class name matches classNameSpec.

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Reportable EventsActionName

Parameters Inserts code that reports:

ReportTimeStamp the current time.

ReportProgramPoint the current line number.

ReportExpression String expressionName String importsString expressionBody byte expressionType String parameters

the value of expressionBody, and an identifer expressionName. Parameters and expressionType indicate the expression’s free parameters and the result type. Expression is evaluated locally

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Using AOP for instrumentation

F ::= true | false | ~ F | F /\ F | f \/ F | F → F | @ F | # F | F;F

|E | E => F | E & F

E ::= [Nm:]Nm.Id(Nm,*)[returns [Nm]]Id ::= Java identifier

Nm ::= Id[?| ]*

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Example Using AOP

observer BufferObs{ max Always(Term t ) = t /\ @ Always(t).

min Eventually(Term t) = t \/ @ Eventually(t). min Previously(term t) = t \/ # Previously(t).

var Buffer b; var Object o;

monitor InOut = Always(put(b?,o?) => Eventually(get(b) returns o)).

monitor OutIn = Always(get(b?) returns o? => Previously(put(b,o))).

}

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Software Fault Protection: Motivation

To obtain higher levels of assurance and quality for safety and mission critical software.

Residual error rate per line of code

Marginal cost to rem

ove next erro

r

3 errors/1KLOC 10 errors/1KLOC

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Approach

• Instead of climbing the error curve, build mechanisms into the code to protect against inevitable residual software errors.

• Software Fault Protection.

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Three Levels of Fault Protection

• Caution and Warning Systems• Warning generated for off nominal measurements of

system state and resources

• Autonomic response• Circuit breaker• MER low battery warning stopped repeated reboot

cycle and transition into safe mode.

• Model Based Diagnosis: fault detection isolation and repair• HAL reports a module will go critical in 7 days

requiring EVA

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Differences between System Engineering and Software

• Software does not wear out, but suffers from design and coding errors.

• Software has not been traditionally designed with using fault containment concepts• E.g. on MER hardware exceptions generated by out of

memory faults were not explicitly dealt with.• A natural concept of component based on physicality

exists for hardware. These components have known failure modes • E.g. a valve may be stuck on or a pipe may leak.

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Component

• Component is • Organizing notion for system

composition/decomposition• Goal is to identify a failed component and possibly

reconfigure components maximizing functional capability

• Spectrum of component choices• Low granularity: a statement is a component.• Medium granularity : a procedure/method is a

component.• High granularity: a component in a modeling

framework.

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Model

• It must be cost effective to formulate model• Model must be useful in identifying and isolating faults.• Model has a delicate relationship to code:

• structural + limited behavioral code synthesis,• monitor behavioral properties that are beyond synthesis.

Software Fault protection is like V&V in one important respect:

Consistency check between code andmodel

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Approach

• High granularity components• difficult to model, diagnose and repair at lower levels of

granularity• Addresses higher-payoff, system of system and system

integration issues.

• Model• structural: component/connector models properties

– interface behavior: • patterns of event interactions over connectors• real time response• data validity (pre- post- conditions)

– resource usage• memory, object creation, disk, communication

devices, quality of service, deadlock and other concurrency problems

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Software Fault Protection Fault Detection, Isolation and Repair (FDIR)

• Detection: instrument and monitor system and identify an error state

• Isolation: identify the faulty component• Repair: take corrective action

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Detection

• Detection = property violation• Use Eagle to define and monitor propertie

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Isolation (Diagnosis)

• Process of moving from symptom to identification or isolation of faulty component.• Our simplistic approach:

– Associate a failed component with each property.

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Repair

• “Do no harm”• generate a problem report• Micro-reboot: reset or re-initialize a

component to repair an inconsistent data state.

• Reconfiguration with reduced functionality

• Replace module with a less capable backup– 1.5 version programming

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Example System

Producer 1

Queue Consumer

Producer 2

• Every request must be responded to the consumer within 50 ms. • Consumer must stay alive. It does not stop processing requests.• Queue must stay alive. It does not stop receiving and forwarding

transactions• Any transaction processed by the consumer did indeed originate

from one of the producers. • Queue size remains bounded at size at most 50. • Requests have priority that the Queue must respect

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The State

class State extends EagleState { static String con; static int ident; static int clock;

static void update(String event) {...} static boolean B1(){return con.equals("B1");} static boolean B2(){return con.equals("B2");} static boolean C() {return con.equals("C");} static boolean D() {return con.equals("D");} }

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Linear Temmporal Logic

max A(Term t) = t /\ @ A(t)

min E(Term t) = t \/ @ A(t)

min P(Term t) = t \/ # P(t)

min U(Term t1, Term t2) = t2 \/ (t1 /\ @U(t1,t2))

max W(Term t1, Term t2) = A(t1) \/ U(t1,t2)

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Some Basic Definitions

min B12() = B1() \/ B2()

min B2(int id) = B2() /\ ident = id

min C(int id) = C() /\ ident = id

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Extensions of E and P withData Constraints

min Et(Term t, int time) = E(t /\ clock <= time)

min Eti(Term t, int time, int id) = E(t /\ clock <= time /\ ident = id)

min Pi(Term t, int id) = P(t /\ ident = id)

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Requirement #1

requiredResponse Every message from a producer (communicated oneither B1 or B2) must be responded to and acknowledged (on D)

bythe consumer within 50 seconds.

mon requiredResponse = A(B12() → Eti(D(), clock + 50, ident))

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Requirement #2

stayAliveQueue Whenever a message is sent to the queue from a producer (on either B1 or B2), a message (not necessarily the same) must be consumed by the consumer (on C) within 32 seconds.

mon stayAliveQueue = A(B12() → Et(C(), clock + 32))

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Requirement #3

limitedSize There are never more that 40 messages in the queue.

max CountSize(int size) = B12() → (size < 40 @ CountSize(size + 1)) /\ C() → @ CountSize(size − 1) /\ D() → @ CountSize(size)

mon limitedSize = CountSize(0)

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Requirement #4

C D Alternation The consumer should alternate between consuming messages (on C) and acknowledging messages (on D).

max S1() = (C() -> @S2()) /\ (~C() -> @S1()) /\ ~D()

Min S2() = (D() -> @S1()) /\ (~D() -> @S2()) /\ ~C()

mon C_D_Alternation = S1()

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Requirement #5

stayAliveConsumer Every message consumed (on C) by the consumer is processed and acknowledged (on D) within 30 seconds.

mon stayAliveConsumer = A(C() → Et(D(), clock + 30))

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Requirement #6

noJunkConsumed Every message consumed by the coonsumer (on C) has

previously Been produced (on Bor B).

mon noJunkConsumed = A(C() → Pi(B12(), ident))

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Requirement #7

noJunkAcknowledged Every message processed and acknowledged by the consumer (on

D) haspreviously been consumed by the consumer (on C).

mon noJunkAcknowledged = A(D() -> Pi(C(), ident))

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Requirement #8orderPreserved The queue behaves as a FIFO queue wrt. messages produced byproducer 1. That is, the consumer consumes (on C) messages

produced on B1 in the order in which they were produced.

max R0() = (B1() -> R1’(ident,0)) /\ @R0()

max R1’(int id1,int id2) = C(id1) \/ ((B1() -> R2’(id1,ident)) /\ (~B1() -> @R1(id1,id2)))

max R2’(int id1,int id2) = @R2(id1,id2)max R2(int id1,int id2) = W(~C(id2),C(id1))

mon orderPreserved = R0()

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Requirement #9

B1HasPriority Messages produced by producer 1 (on B1) have priority over

messages produced by producer 2 (on B2). That is, as long as there are

pending B1 messages in the queue, no B2 message will be consumed (on C)

by the consumer. min CForB1BeforeCForB2(int id) = U(~(C() /\ identFromB2(ident)), C(id))

min identFromB2(int id) = P(B2(id))

mon B1HasPriority = A(B1() -> CForB1BeforeCForB2(ident))

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Example Implementation

• Each component is a process • Components communicate using Java

Messaging Service• Instrumentation by listening to message

traffic• FDIR component can start, reset,

terminate, or replace components

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Summary

• EAGLE is a succinct but highly expressive finite trace monitoring logic. Can elegantly encode any monitoring logic we have investigated.

• EAGLE can be efficiently implemented, but users must remain aware of expensive features.

• EAGLE demonstrated by integration within a formal test environment, showing the benefit of novel combinations of formal methods and test.

• EAGLE has a natural application in fault protection, but this is a very preliminary idea yet to be validated as useful.