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Leveraging Semantic Web Service Descriptions for Validation by Automated Functional Testing

Leveraging Semantic Web Service Descriptions for Validation by Automated Functional Testing. Ervin Ramollari 1 , Dimitrios Kourtesis 1 , Dimitris Dranidis 2 , and Anthony Simons 3 1 SEERC – South East European Research Centre 2 Computer Science Department – CITY College

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Leveraging Semantic Web Service Descriptions for Validation by Automated Functional Testing

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  1. Leveraging Semantic Web Service Descriptions for Validation by Automated Functional Testing Ervin Ramollari1, Dimitrios Kourtesis1, Dimitris Dranidis2, and Anthony Simons3 1 SEERC – South East European Research Centre 2 Computer Science Department – CITY College 3 Department of Computer Science – University of Sheffield SEERC - Research Centre of the University of Sheffield and CITY College 1

  2. Presentation outline • Background and motivation • The starting point: semantic descriptions of Web service behaviour • The destination: Stream X-Machine model of Web service behaviour • The derivation method: from IOPE descriptions to an SXM model • Test case generation and execution • Summary and future work SEERC - Research Centre of the University of Sheffield and CITY College 2

  3. Background and motivation SEERC - Research Centre of the University of Sheffield and CITY College 3

  4. Testing and validation of Web services • A service-oriented environment can be open • Integration of third-party Web services • Dependability is a major challenge • Service validation is crucial for engineering dependable service-based systems • Web service implementation should conform to given functional/behavioural specifications • Validation done through functional testing • Manual Web service testing is highly laborious • Definition of test cases, execution and evaluation SEERC - Research Centre of the University of Sheffield and CITY College 4

  5. Leveraging SWS descriptions for automating testing • Semantic Web Service descriptions are utilised for automating a range of activities • Service discovery, composition, mediation… • Less attention has been paid to using them for service testing and validation • Information in SWS descriptions could be exploited for automating test case generation and execution • This information should be represented in a formalism amenable to processing by existing algorithms and tools for test case generation and execution SEERC - Research Centre of the University of Sheffield and CITY College 5

  6. Our background work • Functional testing of Web services • Automated generation and execution of test cases (with completeness guarantees) • Stream X-Machine (SXM) formalism • Used for modelling the behaviour of stateful and conversational Web services • Associated with proven testing method revealing all inconsistencies between implementation and model • Tool support • SXM modelling, model animation, test case generation, test case execution on WS SEERC - Research Centre of the University of Sheffield and CITY College 6

  7. Aim of this work • Could we derive a SXM model from a Semantic Web Service description for the purposes of automated testing and validation? • The derivation approach should be: • Automatable to the greatest extent possible • Independent of any particular SWS framework • Using Semantic Web technology standards SEERC - Research Centre of the University of Sheffield and CITY College 7

  8. The starting point:semantic descriptions of Web service behaviour SEERC - Research Centre of the University of Sheffield and CITY College 8

  9. Semantic descriptions of Web service behaviour • SWS frameworks like OWL-S, WSMO (and ad-hoc SA-WSDL combinations) are heterogeneous • A commonality is describing service operations in terms of inputs, outputs, preconditions, and effects (IOPE) • Transformation of inputs to outputs • Conditional effects on the internal state • All SWS frameworks support IOPE in some form • OWL-S: Inputs, Outputs, Preconditions, Results • WSMO: Preconditions, Postconditions, Assumptions, Effects • SAWSDL: ModelReference annotations on operations SEERC - Research Centre of the University of Sheffield and CITY College 9

  10. IOPE-based descriptions • IOPE is useful for describing stateful and conversational Web services • E.g. shopping cart, e-banking, order management… • IOPE representation requires a combination of • Production rules for expressing: • Conditions for execution (preconditions) • Actions of execution (effects in terms of assertions and retractions) • Ontology for expressing: • Structure of inputs and outputs • Internal state-related variables (i.e. not visible in WSDL) SEERC - Research Centre of the University of Sheffield and CITY College 10

  11. IOPE-based descriptions with RIF-PRD + OWL • In this paper we adopt RIF-PRD and OWL as a possible combination • Why RIF-PRD? • RIF as rule language for the Semantic Web • Production Rule Dialect has operational semantics (specified as an LTS) • RIF-PRD forthcoming W3C recommendation • Why OWL? • W3C recommendation SEERC - Research Centre of the University of Sheffield and CITY College 11

  12. The destination:Stream X-machine model of Web service behaviour SEERC - Research Centre of the University of Sheffield and CITY College 12

  13. Stream X-machines • Stream X-Machines, specialisation of the X-machine model (Eilenberg 1974) • 8-tuple (Σ, Γ, Q, M, Φ, F, q0, m0) • Extend Finite State Machines (FSM) with: • Memory • Processing functions labelling transitions • Complete functional testing method • Proven to reveal all faults in an implementation given that certain design for test conditions are satisfied SEERC - Research Centre of the University of Sheffield and CITY College 13

  14. Bank account service example • Service operations • open • deposit • withdraw • getBalance • close • Operations are invoked according to a protocol (choreography) SEERC - Research Centre of the University of Sheffield and CITY College 14

  15. Bank account service SXM Model SEERC - Research Centre of the University of Sheffield and CITY College 15

  16. SXM processing functions for operation withdraw <function name="withdraw" input="withdraw" output="withdrawOut">   <precondition>withdraw.amount > 0 && balance > withdraw.amount</precondition>   <effect>balance = balance - withdraw.amount; withdrawOut.amount = withdraw.amount;</effect>   </function> <function name="withdrawAll" input="withdraw" output="withdrawOut">   <precondition>withdraw.amount > 0 && balance == withdraw.amount</precondition>   <effect>balance = balance - withdraw.amount; withdrawOut.amount = withdraw.amount;</effect>   </function> SEERC - Research Centre of the University of Sheffield and CITY College 16

  17. The derivation method:from IOPE descriptions to an SXM model SEERC - Research Centre of the University of Sheffield and CITY College 17

  18. Identifying state variables (* wsdl:operation withdraw *) Forall ?account ?status ?balance ?request ?withdrawAmount ( And ( ?account#Account ?account[hasStatus->?status] ?account[hasBalance->?balance] ?request#WithdrawRequest ?request[hasAmount->?withdrawAmount] ) If And (External (pred:string-equal(?status "ACTIVE") External (pred:numeric-greater-than-or-equal(?balance ?withdrawAmount) ) Then Do(Retract (?account[hasBalance->?balance]) Assert(?account[hasBalance->External(func:numeric-subtract(?balance?withdrawAmount))]) Retract (?request) (?response New(?response#WithdrawResponse)) (?newBalance ?account[hasBalance->?newBalance]) Assert (?response[hasAmount->?newBalance) ) ) • Two state variables: • hasBalance • hasStatus SEERC - Research Centre of the University of Sheffield and CITY College 18

  19. Partition analysis • The domains of state variables are determined by consulting their respective ranges in the OWL ontology. <owl:DatatypeProperty rdf:about="#hasBalance"> <rdf:type rdf:resource="&owl;FunctionalProperty"/> <rdfs:domain rdf:resource="#Account"/> <rdfs:range rdf:resource="&xsd;nonNegativeInteger"/> </owl:DatatypeProperty> • hasBalance :== 0…∞ • hasStatus :== {INITIAL, ACTIVE, CLOSED} SEERC - Research Centre of the University of Sheffield and CITY College 19

  20. Partition analysis • State :== hasStatus x hasBalance • E.g. (INITIAL, 0), (INITIAL, 1), …, (ACTIVE, 0), (ACTIVE, 1), … • Examine the preconditions to partition ranges of state variables If And(External (pred:string-equal(?status "ACTIVE") External (pred:numeric-greater-than-or-equal(?balance?withdrawAmount) ) • hasStatus: • ?status = INITIAL • ?status = ACTIVE • hasBalance: • ?balance = 0 • ?balance ≥ ?withdrawAmount AND ?withdrawAmount > 0 • Result • hasStatus: {INITIAL}, {ACTIVE}, {CLOSED} • hasBalance: {0}, {x | x>0} SEERC - Research Centre of the University of Sheffield and CITY College 20

  21. Identifying preliminary states • The preliminary state space is defined as the product of the state variable partitions. • Six states SEERC - Research Centre of the University of Sheffield and CITY College 21

  22. Determining transition pre-states • An input is accepted at a state (pre-state) iff the preconditions are satisfied at the pre-state • E.g. the preconditions of the withdraw operation triggered WithdrawRequest are (hasStatus=ACTIVE and hasBalance>0) . SEERC - Research Centre of the University of Sheffield and CITY College 22

  23. Merging states • Preliminary states in which the same set of inputs is accepted are merged • Cannot be distinguished SEERC - Research Centre of the University of Sheffield and CITY College 23

  24. Determining transition post-states • Apply the effects of the invoked operation on the pre-state to determine the possible post-states. • WithdrawRequest leads to two possible post-states • Two different transitions labelled by different processing functions • Isolated states are removed. SEERC - Research Centre of the University of Sheffield and CITY College 24

  25. Determining guard conditions for processing functions • The guard conditions are the same as the corresponding rule preconditions • Any predicates already satisfied in the pre-state are omitted. SEERC - Research Centre of the University of Sheffield and CITY College 25

  26. Determining memory updates for processing functions • The memory updates of each processing function consist only of the effects which update the memory (M) variables. SEERC - Research Centre of the University of Sheffield and CITY College 26

  27. Test case generation and execution SEERC - Research Centre of the University of Sheffield and CITY College 27

  28. Generation of test cases • SXM test generation based on Chow’s W-method for FSMs • X = S(Φk+1U Φk U… U Φ U{ϵ})W, where • Φ is the set of processing functions (type of the machine) • W is the characterisation set • S is the state cover • k is an estimate of maximum path length between redundant states in the implementation • Supported by tools SEERC - Research Centre of the University of Sheffield and CITY College 28

  29. From abstract to concrete test cases • The generated test cases are at the same level of abstraction as the specification and cannot be executed directly • Abstract test cases are transformed to JUnit test cases • Generation of Java clients with libraries such as WSDL2Java SEERC - Research Centre of the University of Sheffield and CITY College 29

  30. Execution of Test Cases SEERC - Research Centre of the University of Sheffield and CITY College 30

  31. Summary and future work SEERC - Research Centre of the University of Sheffield and CITY College 31

  32. Summary of the approach • Leveraging SWS descriptions for the added-value of testing and validation of services • Strengths of the approach: • SWS framework-independent • Testing supported by existing tools • Uses proven SXM functional testing method SEERC - Research Centre of the University of Sheffield and CITY College 32

  33. Related work • Sinha and Paradkar 2006 • OWL and SWRL rules to describe IOPE • Single-state EFSM • No prescribed test generation method • Keum et al. 2006 • Derive multi-state EFSM from plain WSDL + user-supplied information • Only derivation of states is described SEERC - Research Centre of the University of Sheffield and CITY College 33

  34. Future work • Investigate the decidability of the algorithm and the automation of all steps • Implement tools supporting the transformation • Prove equivalence relationship among original specification and derived SXM model • Utilise SWS grounding information to automate the transformation of abstract test cases to concrete (SA-WSDL annotations pointing to RIF-PRD and OWL) SEERC - Research Centre of the University of Sheffield and CITY College 34

  35. Thank you for your attention! SEERC - Research Centre of the University of Sheffield and CITY College 35

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