Lecture Notes in Computer Science 3472

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386 Kirsten Berkenkötter and Raimund Kirner the problem of generating curves as input data if time-continuous input is needed. With respect to output data, fuzziness is considered for test evaluation. Test Case Generation Algorithms We presented two different test case generation algorithms for real-time systems that are based on discrete time and one algorithm that is based on dense time. Also one algorithm for creating test suites for hybrid systems was discussed. The most important function of these algorithms is the way in which the size of the test suite is reduced to a manageable size, i.e the way in which test cases are selected. The first test case generation algorithm discussed is based on the process algebra ACSR where test cases are derived from the constraint graph of the ACSR model. Test data is selected to cover two type of timing constraints, namely performance constraints and behavioral constraints. To handle also applications with recursive elements, upper bounds on the length of tested execution scenarios have to be introduced. Furthermore, test case generation for systems with discrete time can be done with timed automata models. Here, the central idea is using test views that restrict the model of the SUT to a specified test purpose. Test views are composed by parameters like clock granularity and the division of actions into observable and hidden actions. Therefore the task of the test engineer is creating test views that lead to manageable test suites. This must be done carefully as parts of the system may never be tested while other test views overlap. One important advantage of this approach is that the size of test suites is scalable. With respect to dense time, the main idea is finding equivalent parts in the model called partitions. Due to the dense time domain, the model has infinitely many states with respect to the infinitely many possible time values. Partitions group these values so that a finite graph is the result that can be used for test case generation. As this may be too large for full exploration, heuristics must be used to limit the size of the state space. For hybrid systems, the presented algorithm is based on the results of test case generation for real-time systems. In addition to the hybrid model, a second, discrete, model is created that abstracts from the hybrid system. This can be used in combination with the test case generation algorithms for real-time systems presented. The original hybrid model is needed to derive correct evaluations for variables as the discrete model being only an abstraction is not capable of doing this. The selection of dense input curves is not tackled. Optimization of Test Suites Testing timing constraints is still a difficult topic as delays for stimulating the SUT must be chosen and the correctness of outputs of the SUT must be accessed. First attempts with using evolutionary theory based test case generation methods have shown that these can improve the test suite with respect to determining best- and worst-case-execution times. This can be helpful to prove if a SUT meets its timing requirements. All other algorithms presented built up on results of testing untimed systems. In contrast, evolutionary testing has a different background and therefore
13 Real-Time and Hybrid Systems Testing 387 provides a new point of view for future work in the field of real-time and hybrid systems testing. Future Work Model-based test case generation for real-time and hybrid systems has been successfully applied in relatively small examples. More effort has to be put into this to prove the practicability of test case generation algorithms. There is also only few tool support for test case generation and execution for real-time systems and no tool support for hybrid systems. New techniques like evolutionary testing to test timing constraints must be further surveyed. Also cross-fertilization between the different approaches seems useful to improve the presented algorithms as each has its advantages and disadvantages.
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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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Page 385 and 386: 390 Part IV. Tools and Case Studies
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438 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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494 George Din ptcType2 ptcType3
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496 George Din communicate with the
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498 Zhen Ru Dai U2TP document is no
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500 Zhen Ru Dai state machines, sta
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502 Zhen Ru Dai TestResult TestCase
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504 Zhen Ru Dai can be mapped to JU
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506 Zhen Ru Dai 17.4 Test Developme
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508 Zhen Ru Dai Slave: Master: SRI
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510 Zhen Ru Dai sa: Slave− Appli
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512 Zhen Ru Dai sdConnect_To_Master
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514 Zhen Ru Dai BluetoothUnitTest
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516 Zhen Ru Dai is returned to the
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518 Zhen Ru Dai (1) /* test configu
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520 Zhen Ru Dai (1) /* test case */
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Part VI Beyond Testing This last pa
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526 Séverine Colin and Leonardo Ma
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19 Model Checking Therese Berg 1 an
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19 Model Checking 559 The first sta
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19 Model Checking 561 function I :
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coffee coin tea 19 Model Checking 5
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AX (φ) :=¬EX (¬φ) AF (φ) :=Atr
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19 Model Checking 567 Another appro
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19 Model Checking 569 has a transit
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19 Model Checking 571 The alternati
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19 Model Checking 573 Obviously the
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19 Model Checking 575 purposes ther
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19 Model Checking 577 otherwise it
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19 Model Checking 579 Expanding a P
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19 Model Checking 581 We conduct an
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• SA ⊆ Σ ∗ is a nonempty fin
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19 Model Checking 585 When OT is cl
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19 Model Checking 587 • se = s
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19 Model Checking 589 the new colum
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19 Model Checking 591 Henceforth th
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19 Model Checking 593 DT , the tran
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19 Model Checking 595 queries. At t
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19 Model Checking 597 Assistant 4.
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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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