Lecture Notes in Computer Science 3472

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18 Run-Time Verification 547 In the case of the railway gate example, the corresponding MEDL script contains the formula: ↑ is opening ⇒ [closing, ↓ is flashing). If we consider the trace open, off, flashing, closing, closed, opening, open, off, the run-time checker will observe the following states: • s0: ¬is opening, ¬is flashing • s1: ¬is opening, is flashing • s2: ¬is opening, is flashing, closing • s3: is opening, is flashing • s4: ¬is opening, is flashing • s5: ¬is opening, ¬is flashing State s0 is defined by the pair of events open and off; thens1 is obtained from s0 when event flashing takes place. Further states are obtained by considering remaining events. For this trace, the formula ↑ is opening ⇒ [closing, ↓ is flashing) is true on all states. The value of this formula is computed for each state using Table 18.1 and 18.5.3: • s0: true because ¬is opening • s1: true because ¬is opening • s2: true because ¬is opening • s3: true because in s2 the event closing has been triggered and since it, is flashing was holding • s4: true because ¬is opening • s5: true because ¬is opening Note that it is necessary to store all traces to evaluate MaC formulas. Exercise 18.4 (Algorithm Application). Consider the trace open, off, flashing, closing, closed, off, opening, open, derive information sent to the run-time checker, and evaluate the formula ↑ is opening ⇒ [closing, ↓ is flashing) for each state (the formula does not hold in all states) using Table 18.1 and 18.5.3. Last Work on MaC MaC can detect violation of properties, but cannot provide any feedback to the running system. To overcome this limit, the MaC system has been extended with a feedback capability. The resulting system is called MaCS (Monitoring and Checking with Steering) [KLS + 02]. The feedback component uses the information collected during monitoring and checking to steer the application back to a safe state after an error occurs. Computational issues of monitoring by MaC have been investigated by Kim et al [KKL + 02]. Moreover, Sammapun et al. [SSD + 03] have defined a formal model of Java MaC safety properties in terms of an operational semantics for Middleweight, which is a considerable subset of the Java language.
548 Séverine Colin and Leonardo Mariani 18.6 Run-Time Verification of Learned Properties Often, requirements are not formalized or even are not available, therefore it would be impossible to verify the correctness of observed executions. This scenario would invalidate any run-time verification techniques. The problem of the lack of formal specifications can be solved by using program synthesis techniques. Aprogram synthesis technique learns specific aspects 1 of a running system by observing its behavior. In particular, executions are first observed to produce the synthesized properties (learning phase) and then executions are checked with respect to learned properties (verification phase). In some cases, learning continues even during the verification phase. When a formal specification is not available, program synthesis can automatically produce a specification that can be checked. Program synthesis exploits its potentials when it is used to verify the correctness of an evolving system. In fact, components of a system are often replaced with new components to either extend, modify, remove or correct functionalities of an existing system, but the updated version of the system can contain more faults than the original one, i.e., due to faults contained in the new components (but missing in the existing ones), or due to incompatibilities between new components and the system. The properties inferred from the replaced component can be used at run-time to discover possible faults within the new component. In some cases, new behaviors can be desired, e.g., bug fixes, and thus violations of invariants are ignored. Furthermore, program synthesis is very effective when applied to a program that is developed with a limited knowledge of the final environment that will host the program itself. In fact, inference can be performed directly in the field so that the learned behavior depends on the context where the system is used. Therefore, the technique automatically takes advantage of information gathered from the field that was not available neither during development nor beta testing. Examples of systems that are developed without “complete” knowledge on the final environment are component-based systems (software components are usually developed in isolation, then are assembled with third-party components, and finally are deployed in unknown environments), mobile systems, agent-based systems, ubiquitous systems, wireless networks and self-adaptable systems. Learned properties can be used also to build test suites [HME03] and to check compliance of the observed behavior with respect to known properties [ECGN01]. A program synthesis technique can be effectively applied at run-time only if it is lightweight, and it is also able to infer meaningful properties that can be further checked. In this chapter, we focus on learning techniques based on invariant detection; they consist of techniques that monitor single executions to infer invariants, i.e., properties and relations over measured parameters that hold on all or most of the executions. 1 The learned aspect is dependent on the learning technique that is used, e.g., it could be a behavioral pattern, an interaction protocol, or a relation among stimuli and results.
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Lecture Notes in Computer Science 3
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Volume Editors Manfred Broy Martin
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VI Preface The last part of the boo
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VIII Contents 13 Real-Time and Hybr
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2 Part I. Testing of Finite State M
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1 Homing and Synchronizing Sequence
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s2 a/1 1 Homing and Synchronizing S
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1.3.2 Computing Synchronizing Seque
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A1 A2 Bad 1 Homing and Synchronizin
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36 Moez Krichen b/1 a/0 s1 b/1 a/1
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38 Moez Krichen Now, even for minim
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40 Moez Krichen the algorithms for
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42 Moez Krichen ∀ s, s ′ ∈ S
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44 Moez Krichen In this proposition
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46 Moez Krichen Proposition 2.6. If
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48 Moez Krichen Algorithm 5 Checkin
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50 Moez Krichen (5) Edges emanating
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52 Moez Krichen graph of this machi
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54 Moez Krichen v12 v6 v11 1 I = {s
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56 Moez Krichen B of π, a is a-val
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58 Moez Krichen I = {s1, s2, s3, s4
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60 Moez Krichen Definition 2.19. A
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62 Moez Krichen Algorithm 7 Computi
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64 Moez Krichen Algorithm 8 Computi
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66 Moez Krichen The two authors als
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3 State Verification Henrik Björkl
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a/1 s1 s3 b/1 a/1 b/1 a/0 s2 s4 3 S
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3 State Verification 73 Thus (s, Q)
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3 State Verification 75 0, and x ca
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s1 s3 a/0 b/0 a/1 s2 s4 3 State Ver
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3.4.2 Naik’s Algorithm 3 State Ve
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s1 s2 s3 s1 s3 s3 a/0 a/0 s1 s2 s3
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3 State Verification 83 Since the m
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3 State Verification 85 x = a1a2 ..
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4 Conformance Testing Angelo Gargan
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4 Conformance Testing 89 the test u
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4 Conformance Testing 91 state. For
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4 Conformance Testing 93 This check
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s1 s1 a b s2 s2 a b s3 4 Conformanc
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4 Conformance Testing 97 Using a Q
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4 Conformance Testing 99 this case
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4.4.5 Cost and Length 4 Conformance
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t τ (t,si−1) x τti−1 ,si si
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si 1 2 w1 t i w 2 3 a: in MS si 1 w
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4 Conformance Testing 107 ti = δ(s
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4 Conformance Testing 109 Example.
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4 Conformance Testing 111 • If on
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114 Part II. Testing of Labeled Tra
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5 Preorder Relations ∗ Stefan D.
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5 Preorder Relations 119 power of r
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5 Preorder Relations 121 To summari
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5 Preorder Relations 123 Two import
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∧ ⊥⊤ ⊥ ⊥⊥ ⊤ ⊥⊤
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5 Preorder Relations 127 Definition
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5 Preorder Relations 129 For one th
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5 Preorder Relations 131 We close t
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s a b t 5 Preorder Relations 133 y
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5 Preorder Relations 135 (1) p ⊑m
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a p ′ τ b p ′′ τ τ a a b 5
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5 Preorder Relations 139 It should
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coin coin e c coin coin τ 5 Preord
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5 Preorder Relations 143 for free.
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5 Preorder Relations 145 condition
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5 Preorder Relations 147 ⊑←→
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5 Preorder Relations 149 The utilit
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152 Valéry Tschaen The labels repr
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154 Valéry Tschaen Intuitively, th
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156 Valéry Tschaen Definition 6.2.
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158 Valéry Tschaen By this theorem
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160 Valéry Tschaen The test suite
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162 Valéry Tschaen each sequence (
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164 Valéry Tschaen and B2 are LOTO
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166 Valéry Tschaen 6.4.2 The CO-OP
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168 Valéry Tschaen a τ q0 τ τ q
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170 Valéry Tschaen Concerning the
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7 I/O-automata Based Testing Machie
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7 I/O-automata Based Testing 175 th
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7 I/O-automata Based Testing 177 in
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7 I/O-automata Based Testing 179 sp
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7 I/O-automata Based Testing 181 na
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7 I/O-automata Based Testing 183 Un
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7 I/O-automata Based Testing 185 We
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7 I/O-automata Based Testing 187 fa
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7 I/O-automata Based Testing 189 Th
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7 I/O-automata Based Testing 191 Ac
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7 I/O-automata Based Testing 193 vi
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7 I/O-automata Based Testing 195 De
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7 I/O-automata Based Testing 197 To
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7 I/O-automata Based Testing 199 In
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8 Test Derivation from Timed Automa
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s0 on, true, {c} off , c =5, ∅ 8
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8.3 Testing Event Recording Automat
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[s1, z], z = con < 5, 8 Test Deriva
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Ψ(S ′ )={PE ′ | E ′ ∈ 2E(S
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{s0}, p0 p0 : tt 8 Test Derivation
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s0 c = ∞ 8 Test Derivation from T
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Example. The Light Switch can be de
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��s0� ,c=0.0 s0 en, on, {c :=
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��s0� ,c=0 s0 8 Test Derivati
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234 Verena Wolf to probabilistic tr
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236 Verena Wolf • A finite trace
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238 Verena Wolf (a, 0.2) v1 sP (a,
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240 Verena Wolf 1 2 sP a b µ λ 1
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242 Verena Wolf (a, 1 4 ) (a, 1 4 )
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244 Verena Wolf 9.6 Test Processes
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246 Verena Wolf We present two appr
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248 Verena Wolf specification proce
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250 Verena Wolf sP (τ, 1 1 ) (c, 3
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252 Verena Wolf sP (a, 1 1 ) (a, 2
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254 Verena Wolf v1 u1 w1 1 2 sP λ
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256 Verena Wolf • P ⊑ must JY Q
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258 Verena Wolf u1 s M ′ 1 (a, r1
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260 Verena Wolf Proposition 9.22. L
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262 Verena Wolf Now let Q min be th
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264 Verena Wolf (8) (⊑BC , ⊑CH
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266 Verena Wolf is that an action a
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268 Verena Wolf can perform τ-tran
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270 Verena Wolf T np,re seq ⊑ tr
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272 Verena Wolf t2 t4 a sT t1 b b t
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274 Verena Wolf model test processe
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Part III Model-Based Test Case Gene
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Part III. Model-Based Test Case Gen
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282 Alexander Pretschner and Jan Ph
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11 Evaluating Coverage Based Testin
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12 Technology of Test-Case Generati
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Increment ∆Counter 12 Technology
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(12.4) ✟ nat(X ′ ) {A/0, B/X
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a: b: (6) α1+1−α2 α2 a: b: PC:
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12.5 Summary 12 Technology of Test-
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13 Real-Time and Hybrid Systems Tes
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Test Driver SUT 13 Real-Time and Hy
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fitness 13 Real-Time and Hybrid Sys
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13.5 Summary 13 Real-Time and Hybri
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390 Part IV. Tools and Case Studies
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392 Axel Belinfante, Lars Frantzen,
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440 Wolfgang Prenninger, Mohammad E
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Part V Standardized Test Notation a
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466 George Din inter-operability te
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468 George Din • Tabular Presenta
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470 George Din 16.3.1 Test Building
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472 George Din Abstract Test System
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474 George Din field can be marked
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476 George Din • omit: the value
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478 George Din altstep Default() ru
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480 George Din alt { [] httpPort.re
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482 George Din • Test Management
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484 George Din Value Type getType()
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486 George Din This task is rather
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488 George Din hosts, of preparing
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490 George Din A B C D MEM = 256 MB
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492 George Din A hardware load moni
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Page 499 and 500: 506 Zhen Ru Dai 17.4 Test Developme
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Page 515 and 516: Part VI Beyond Testing This last pa
Page 517 and 518: 526 Séverine Colin and Leonardo Ma
Page 537: 546 Séverine Colin and Leonardo Ma
Page 547 and 548: 19 Model Checking Therese Berg 1 an
Page 549 and 550: 19 Model Checking 559 The first sta
Page 551 and 552: 19 Model Checking 561 function I :
Page 553 and 554: coffee coin tea 19 Model Checking 5
Page 555 and 556: AX (φ) :=¬EX (¬φ) AF (φ) :=Atr
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Page 559 and 560: 19 Model Checking 569 has a transit
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Page 563 and 564: 19 Model Checking 573 Obviously the
Page 565 and 566: 19 Model Checking 575 purposes ther
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Page 569 and 570: 19 Model Checking 579 Expanding a P
Page 571 and 572: 19 Model Checking 581 We conduct an
Page 573 and 574: • SA ⊆ Σ ∗ is a nonempty fin
Page 575 and 576: 19 Model Checking 585 When OT is cl
Page 577 and 578: 19 Model Checking 587 • se = s
Page 579 and 580: 19 Model Checking 589 the new colum
Page 581 and 582: 19 Model Checking 591 Henceforth th
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19 Model Checking 599 have created
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19 Model Checking 601 Logic (see Se
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19 Model Checking 603 linear time l
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20 Model-Based Testing - A Glossary
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20 Model-Based Testing - A Glossary
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612 Bengt Jonsson a/0 b/0 b/1 s2 s4
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614 Bengt Jonsson of input symbols
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616 Joost-Pieter Katoen The followi
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618 Literature [AFH94] Rajeev Alur,
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620 Literature [BCMD90] J. R. Burch
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622 Literature [BRV95] Ed Brinksma,
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624 Literature [CL97a] Duncan Clark
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626 Literature [DGNP04] Z. R. Dai,
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628 Literature [FHP02] Eitan Farchi
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630 Literature [GL02] Hubert Garave
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632 Literature [Hib61] Thomas N. Hi
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634 Literature [HR01b] Klaus Havelu
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636 Literature [KKL + 01] M. Kim, S
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638 Literature [LPY97] Kim Guldstra
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640 Literature [Müh97] H. Mühlenb
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642 Literature [Pha93] M. Phalippou
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644 Literature [RdBJ00] V. Rusu, L.
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646 Literature [Sch00] Johann M. Sc
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648 Literature [SVG02] S. Schulz an
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650 Literature [Vas73] M. P. Vasile
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Index =⇒ , 175 ioco, 187 ioconf,
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homing sequence, 8 - adaptive, 8, 2
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eset sequence, see synchronizing se
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timed transition system, 223, 360,
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