Using the Multiple-Clue approach for system testing on AIRBUS FAL (Final Assembly Line)

Transcription

1 Usng the Multple-Clue approach for system testng on AIRBUS FAL (Fnal Assembly Lne) Fassely Doumba, Odle Laurent, Dder Atger AIRBUS France 316 route de Bayonne Toulouse Cedex 09 Chantal Robach LCIS Grenoble INP 50 rue Barthélémy de Laffemas Valence Cedex 09 Abstract Our work s focused on a dagnoss approach for the system testng process on AIRBUS Fnal Assembly Lne. The method descrbed below supports tests defnton for dagnoss and faulty component dentfcaton based on functonal testng. 1. Introducton Testng actvtes can be manly splt nto two complementary parts: test desgn phase and test executon and dagnoss phase. Whle the obectve of testng s falure detecton, dagnoss ams at locatng, f any, the faulty components n a system. Dagnoss s a task whch can be extremely dffcult dependng on the nature of the faults and the system complexty. Accordng to the context (unt or ntegraton testng), test and dagnoss are more or less related. The obectve of unt testng s to defne test cases that are able to detect and localze a faulty component n a small part of a system. In ths case, test cases are desgned for dagnoss. Integraton testng ams at verfyng the system functonal behavor as a whole. In ths context, t s dffcult to make a correlaton between test and dagnoss and the assocated effort. Even f faults are easly revealed durng testng, there s no reason for the detected faults to be easy to locate and to correct [1]. Therefore, fault solaton usually requres more thorough and costly procedures than fault detecton. Thus, t s mperatve to fnd solutons that reduce the cost of dagnoss problems. Basc prncples of system dagnostcs are summarzed n [2] and reasonng-based dagnoss has been presented by Manley and Eklow [3]. Our approach, Multple-Clue [4, 5], whch concerns an automatc post-test dagnoss method, s appled to the system verfcaton process on AIRBUS FAL. The man purpose of the defned methodology s the dagnosablty assessment, defned here as the property of a faulty component of the system to be easly and precsely located, usng an abstract model. Applcaton of ths methodology, n the FAL context, gves nformaton helpng systems verfcaton process from test desgn or defnton down to fault locaton. Ths paper ntroduces the Multple-Clue approach and the abstract model for system components dagnosablty analyss n secton 2. The secton 3 presents the system verfcaton process on Arbus FAL. The fourth secton deals wth the applcaton of Multple-Clue n ths process. We descrbe a dagnosablty mprovement method n secton 5. The sxth secton presents the proposed methodology. Expermental results are dscussed n Secton Multple-Clue approach The Multple-Clue approach s based on a crosscheckng test strategy. A test strategy defnes the way to conduct test actvtes and to analyze test results. The cross-checkng test strategy conssts of runnng a set of tests and then collectng all test results before tryng to locate and fx any faulty components. Multple-Clue chooses relevant tests n order to exercse each resource at least once durng the tests campagn and to ensure a precse dagnoss when a falure occurs Hypothess Followng a test campagn, detecton of faulty resources s based on dscrepancy dentfcaton between effectve and expected test results. So, dagnoss reles on the confdence n oracles and test Lecture 1.2 INTERNATIONAL TEST CONFERENCE /09/$ IEEE

2 results. But, two unknown parameters are used for constructng such a dagnoss: the number of revealed faults by a set of test cases and the confdence n the test cases whch do not reveal a fault (.e. the probablty of nfectng a faulty statement and propagatng ts results). Consderng these parameters, the hypothess adapted to the Multple-Clue s: only one faulty resource among the exercsed components s revealed by a test case set (sngle fault hypothess) and oracles are sure. Indeed, test cases set that do not reveal a fault prove that the executed components are correct (probablty of nfecton and propagaton of the faulty result when a statement s executed = 1): all the test cases are taken nto account n the dagnoss strategy. We assume ths hypothess for Multple-Clue applcaton hereafter n the paper Buldng up the system model Multple-Clue uses an abstract model of the system for analyzng the dagnosablty of ts components. A system S can be generally consdered as a set of components (hardware and software) mplementng functons n order to fulfll ts requrements. These functons are verfed by executng the system based on a set of test cases T. The system model corresponds to a matrx representaton assocatng all the system resources and related test cases. A resource R corresponds to any component that can cause a falure n system S. A test case T corresponds to a combnaton of system nputs that allows exercsng a set of resources contrbutng to a system functon. Each test case T may reveal a falure among exercsed resources. The trace of a test case T (trace(t )) s the set of resources exercsed durng ts executon. Let R be the set of resources and T be the set of defned test cases for a system S, we can wrte the followng expressons: = T = R R The matrx representaton, called hereafter the dagnosablty matrx, corresponds to an array where refers to resources and refers to test cases. It s bult such as: m = 1; R trace( T ) or T m = 0 otherwse exercses R Fgure 1 gves an llustraton of a dagnosablty matrx modelng. R 2 R 1 R 3 (a) (b) Fgure 1: System 3R Fgure 1 (a) shows a system 3R composed of three resources (R1, R2 and R3). Test case T1 exercses R1 and R2; test case T2 exercses R2 and R3. The dagnosablty matrx s represented n Fgure 1 (b). The Multple-Clue approach conssts n assessng the dagnoss effort usng the system model n order to provde useful nformaton descrbed below. Indstngushable resources: two resources are ndstngushable from each other f they are always exercsed together. Equvalent test cases for dagnoss: two test cases are equvalent or redundant for dagnoss f they exercse the same set of resources. Dagnoss set: A dagnoss set s a mnmum subset of necessary and suffcent test cases to exercse at least once all the system resources and to dentfy ether the faulty resource or the set of suspected ndstngushable resources durng dagnoss Dagnoss tree constructon R 1 R 2 R 3 T T The cross-checkng method s carred out usng the dagnoss set for faulty resources dentfcaton. It s based on the dcng method, whch s a partcular use of slcng [6]. It allows the best fault locaton after falure detecton n the sngle fault hypothess. Ths method s llustrated n Fgure 2. R 1 and R 2 are correct Correct system Success Test T 2 Success Fal R 3 faulty Test T 1 Fal R 1 and R 2 are suspected Success R 1 faulty Test T 2 Fgure 2: Dagnoss tree of 3R Fal R 2 faulty In accordance wth our hypothess (see secton 2.1), a test case s successful when ts executon result corresponds to the expected one descrbed n the oracle and all exercsed resources are correct. It fals otherwse. Fgure 2 shows the dagnoss tree bult for System 3R usng the cross-checkng method. It Lecture 1.2 INTERNATIONAL TEST CONFERENCE 2

3 proposes a best locaton process for faulty resource dentfcaton wth a mnmum subset of test cases. In ths secton, we show that nformaton provded by Multple-Clue approach can be helpful to reduce dagnoss effort. However, the applcaton of ths approach depends on the context. In partcular, the dentfcaton of approprate resources and test cases for the system modelng s decsve n Multple-Clue usage. In the next secton, we present the system verfcaton process on AIRBUS FAL n order to pont out test cases defnton and dagnoss actvtes whch can support Multple-Clue approach deployment. execute most of the systems test nstructons and gve the verdct. But, for some partcular tests, human experence s needed to gve the verdct. Dagnoss after falure detecton: Ths actvty ams at dentfyng and replacng the faulty resource. It s carred out, for each test obectve, when one or more test cases results are ncorrect. At the FAL level, dagnoss s essentally based on human expertse. 3. Systems verfcaton process on FAL The fnal assembly lne actvtes consst n assemblng the arcraft structure elements (sectons equpped wth approprate wres and equpments, wngs, etc.) and nstallng avoncs systems and engne before arcraft delvery to arlnes. When nstallng avoncs systems, tests are led. The man purpose of ths functonal testng on FAL s the verfcaton of nstalled systems and ther nterconnectons. Ths verfcaton conssts n controllng that they work properly. In ths secton, we focus on systems verfcaton actvtes on AIRBUS FAL. Consderng the arcraft development process, these actvtes correspond to a part of the ntegraton testng. Indeed, t s supposed that all systems components nvolved n FAL tests (computers, push buttons, sensors, etc.) are tested separately and are not faulty. Systems verfcaton process on FAL s represented n Fgure 3. Two man parts can be hghlghted n the systems verfcaton process on FAL: test cases defnton phase; test case executon and dagnoss phase. The test cases defnton part s composed of actvtes whch are descrbed below. Test Requrements (TR) specfcaton: From the arcraft hgh level requrement, TR specfcaton documents are produced. These documents contan system requrements descrpton and test obectves defnton for testng actvtes on FAL before delvery frst flght. Test Instructons (TI) descrpton: For each test obectve of the TR, a set of test nstructons s defned. A test nstructon s composed of tests defnton and test envronment on whch the tests wll be run. A TI descrpton document s carred out for each system. The man actvtes of test executon and the dagnoss part are descrbed below. Test executon and verdct: Each test obectve s verfed usng test cases and expected tests results descrbed n the TI documents. Automatc testng tools Fgure 3: Systems verfcaton process on FAL As tme savng s an mportant pont to consder durng systems verfcaton on FAL. Then, methods that can mprove tests desgn and dagnoss shall be explored. More precsely, for test cases specfcaton, the man challenges are: the defnton of relevant and non-redundant test cases; the test requrements coverage aganst test cases. We wll focus on relevant and non-redundant test cases n ths paper. In the dagnoss phase, the man challenge s the dentfcaton for certan the faulty resource. In the next secton, we wll show how to ntegrate Multple-Clue approach n the system verfcaton process on FAL n order to meet these challenges. 4. Applng Multple-Clue on AIRBUS FAL In secton 2.2, we menton that the mplementaton of the Multple-Clue approach s based on an abstract model of the system (dagnosablty matrx) whch represents all the exstng relatons between system resources and test cases. Consderng the system verfcaton process on AIRBUS FAL, nformaton about resources and test cases s clearly specfed n the TI descrpton documents (see Fgure 3). Lecture 1.2 INTERNATIONAL TEST CONFERENCE 3

4 Based on ths nformaton, we propose a methodology allowng Multple-Clue approach applcaton. It conssts, for each test obectve, n: modelng resources and test cases; buldng the dagnosablty matrx and applyng the cross-checkng test strategy on ths dagnosablty matrx. The TI descrpton document s structured as follows: The frst part s dedcated to the descrpton of the confguraton phase. It conssts n the defnton of the confguraton that has to be set up before runnng test cases. Most of the system resources are dentfed at ths stage. For each test obectve, the requred confguraton s specfed and functonal test cases are defned. These test cases encompass system functons and resources verfcaton. For traceablty ssues each test case has an ID (dentfer). Ths ID s used for test case modelng n our approach. Consderng the ntegraton verfcaton actvtes based on functonal test cases led on FAL, a system resource s a component of the functonal chan as an avoncs computer, a cable, a crcut breaker, a sensor, an actuator, etc. The next step conssts n buldng the dagnosablty matrx (see Fgure 4). Fgure 6: Multple-Clue mplementaton process The method, descrbes below, permts to pont out per test obectve: Potental redundant test through the equvalent test cases dentfcaton; Resources belongng to an ndstngushable set whch not be able to be declared faulty for certan after tests executon related to the test obectve; The mnmum set of tests to run for an optmal dagnoss through the dagnoss set selecton. When tests fal and nvolve ndstngushable resources, addtonal verfcaton actvtes based on tests executon results of other test obectves sharng suspected resources may be necessary to conclude. Secton 5 proposes a method to mprove the dagnosablty of resources when a set of ndstngushable resources s suspected. 5. Improvng dagnoss methodology Fgure 4: System model constructon process As descrbed n Fgure 3, each test obectve s verfed before swtchng to the followng one. Then, we can dentfy a dagnosablty (sub-) matrx for each test obectve (see Fgure 5). Ths mprovement method ams at usng several test obectves (dagnosablty matrces) analyss nformaton n order to help the dentfcaton of the faulty resource n a set of ndstngushable resources. The mplementaton of ths method conssts n: dentfyng other test obectves whch have common resources wth the set of suspected resources; usng the cross-checkng approach n order to ncrmnate the faulty resource or to reduce the set of suspected ndstngushable resources Prncples Fgure 5: Dagnosablty matrx representaton Fgure 6 depcts the man phases of Multple-Clue mplementaton for each test obectve. The basc prncples of ths method, consderng hypothess n secton 2.1, are llustrated usng Fgure 7. The fnal obectve of ths method s to reduce dagnoss effort accordng to systems verfcaton process on AIRBUS FAL. Fgure 7 presents Ob N test obectve composed of fve resources wth an ndstngushable set of three resources (set 1). Two test cases are selected by Multple-Clue for dagnoss. We assume that the Lecture 1.2 INTERNATIONAL TEST CONFERENCE 4

5 verdct of T1 s success and T2 s fal durng the verfcaton of Ob N. Then, the dagnoss tree s bult and Set 1 s suspected (see Fgure 2). (a) (b) Fgure 10: Reducton of suspected resources Fgure 7: Dagnoss nformaton about Ob N To mprove ths stuaton (three suspected resources), our method conssts n searchng and verfyng others test obectves usng some of suspected resources belonged to Set 1. Fgure 8 presents the test obectve Ob M sharng R1 wth Set 1 (test obectve Ob N ). Four resources are used by Ob M wth an ndstngushable set of two resources (Set 2). Two test cases are also selected by Multple-Clue for dagnoss. In the frst tme, we assume the verdct of T3 executon s fal and the one of T4 s success. Ths nformaton s used to suspect Set 2. Fgure 8: Dagnoss nformaton about "Ob M" In ths stuaton, the method mplements a crosscheckng analyss. Then, the resource R1 s dentfed as the faulty resource n Fgure 9. We can generalze the method descrbed above. Indeed, an algorthmc study of ts prncples mplementaton has been performed consderng AIRBUS context and hypothess ntroduced n secton 2.2. The next secton descrbes these algorthms Algorthmc descrpton Let us defne the followng symbols before descrbng algorthms mplementng the resources dagnosablty mprovement method. R represents the set of resources used by the test obectve TO. α s the set of suspected resources after the dagnoss of TO. Ths set s empty when all resources are correct. β corresponds to the set of unsuspected resources after the dagnoss of TO. It s empty when all resources belong to the same suspected set of ndstngushable resources. UTOL represents the lst of useful test obectves for TO resources dagnosablty mprovement. STO s the set of test obectves defned for the system. In order to descrbe the algorthm of the man functon (RDI: Resources Dagnosablty Improvement), we need to defne two sub functons: SUTO (Search of Useful Test Obectves) and RDR (Refnng of a Dagnoss Result). SUTO ams at searchng other test obectves whch have common resources wth a set of suspected resources α and returns a lst of found test obectves. Its algorthm s descrbed below. Fgure 9: Identfcaton of a faulty resource Now, let us consder the verdct of T3 and T4 are success n Fgure 10 (a). Ths nformaton means that Ob M resources are all correct. As result of the method mplementaton, the number of suspected resources n the test obectve Ob N (Set 1) s reduced from 3 to 2 resources (R4, R5) (see Fgure 10 (b)). Lecture 1.2 SUTO ( α ) L empty(lst) For each If TOk STO do α Rk Then Thread(L,TOk ) End_For INTERNATIONAL TEST CONFERENCE /* There are common resources. */ 5

6 Return L End_SUTO RDR refnes the dagnoss result α of a test obectve TO usng another one TO (already verfed). Its algorthm s descrbed below. RDR ( α, OT ) S empty(set) S 1 empty(set) If = 0 α /*No suspected resource n TO */ Then S α β /*Dfference */ Else If Return S End_RDR \ α α Then S Else S 1 α α If S 1 Then S S 1 Else S α \ β /* Intersecton */ The man functon RDI ams at mprovng the resources dagonsablty for a test obectve TO. We use two other functons for ts descrpton. Is_Verfed(TO ) returns true f all the defned test cases are exercsed and dagnoss s done for TO. Verfy(TO ) uses the verdct of the test cases belonged to the dagnoss set n order to buld the dagnoss tree for TO. The algorthm of the RDI functon can provde four dfferent outputs (nformaton): No need of mprovement: Ths result means that ether all resources used by TO are correct or the faulty resource s already dentfed. Inconsstency result: Ths stuaton means that nformaton provded by useful test obectves ndcates that TO suspected resources are correct. The verdct of test cases has to be checked n order to fx and correct ths nconsstency. Optmal mprovement: Ths result s provded when the algorthm dentfes the faulty resource among the set of suspected ndstngushable ones. Remanng suspected resources: When the algorthm s not able to dentfy the faulty resource usng nformaton provded by the useful test obectves. In ths case, t reduces n the best way the number of suspected resources. DI (TO ) If α = 0 or α = 1 Then ext 1 /* No need of mprovement. */ UTOL RUTO(TO ) Whle UTOL s not empty TO Unthread(UTOL ) If Is_Verfed(TO ) = false Then Verfy(TO ) α RDR( α,to ) If α = 0 Then ext 2 /* Inconsstency result*/ If α = 1 End_Whle Returnα End_DI Then Return α /* optmal mprovement */ /* Remanng suspected resources */ A prototype of these algorthms mplementaton s bult. It defnes the methodology by automatcally buldng a dagnoss tree and proposng nformaton about the faulty resource or gvng nformaton about next test obectves to verfy n order to mprove the dagnosablty when an ndstngushable set s ponted out. The next secton presents ths methodology. 6. Integratng the methodology on the FAL The proposed methodology ams at combnng the Multple-Clue approach mplantaton, dagnoss tree constructon and resources dagnosablty mprovement method durng systems verfcaton on AIRBUS FALs. Fgure 11 represents the systems verfcaton process updated wth the descrbed methodology n order to meet the man challenges wth are: the defnton of relevant and non-redundant test cases durng the test desgn phase and the dentfcaton for certan the faulty resource durng the dagnoss. Lecture 1.2 INTERNATIONAL TEST CONFERENCE 6

7 Arcraft level requrements TR specfcaton TI descrpton Verfy TO Run tests Ths methodology offers nterestng possbltes for testng tme and cost reducton on AIRBUS FALs. In secton 7, we reveal the results of ts applcaton on an ndustral case study. 7. Expermentatons Tests desgn Identfy resources and tests Buld dagnosablty matrx Apply Multple-Clue n order to dentfy for each test obectve: ndstngushable resources Redundant tests Dagnoss sets (mnmum number of tests for dagnoss) Dagnoss Fal Dagnoss Construct the dagnoss tree Identfy the faulty resource f possble Improve the dagnosablty when a set of ndstngushable resources s suspected: Identfy the faulty ndstngushable resource usng nformaton from test obectves already verfed Gve the next verfcaton actvtes n order to dentfy the fault one or reduce the number of suspected resources Verfy TO k Run tests Fal Dagnoss System correct Fgure 11: Enhanced verfcaton process The descrbed methodology n the prevous sectons has been appled to the trackng gears orentaton system. In ths secton, frstly, the results of the Multple-Clue approach applcaton are descrbed and dscussed; secondly, the resources dagnosablty mprovement method s assessed n terms of testng effort reducton. Based on the TI descrpton document of trackng gears orentaton system, the test obectves, test cases and resources are dentfed. Table 1 presents the man characterstcs of the buld dagnosablty matrx for our case study. Table 1: Dagnosablty matrx data Number Test obectves 26 Test cases 169 Resources 100 Resources per test obectve from 2 to 18 Test cases per test obectve from 2 to 18 The table above exhbts the followng nformaton: 26 test obectves are dentfed n the IT descrpton document of the case study; 169 test cases and 100 resources are modeled for these test obectves; For each test obectve, the number of modeled resources goes from 2 to 18 and the number of test case also vares from 2 to 18. In the next step, Multple-Clue approach s appled for each test obectve usng ther dagnosablty matrx. Table 2 below summarzes the Multple-Clue analyss results hghlghtng the mnmum and maxmum values assocated to test obectves. Table 2: Multple-Clue mplementaton result Mn Max Number of redundant test cases (1) 0 6 Number of ndstngushable sets (2) 0 6 Sze of ndstngushable sets (3) 2 7 Sze of dagnoss set (4) 2 6 Informaton presented above can be descrbed as follows (see Table 2). (1): the number of redundant test cases vares between 0 and 6. Indeed, there are test obectves wthout redundant test cases and those whch contan up to 6 redundant test cases. For example, the analyss reveals that one thrd of 18 defned test cases of a test obectve are redundant. (2): the number of sets of ndstngushable resources goes from 0 to 6. Ths means: dependng on the test obectve, we met stuatons where all resources are dstngushable to the one where there are 6 dfferent sets of Lecture 1.2 INTERNATIONAL TEST CONFERENCE 7

8 ndstngushable resources n the same test obectve. (3): the sze of sets of ndstngushable resources also vares from 2 to 7. Indeed, Multple-Clue analyss hghlghts the presence of an ndstngushable set composed of 7 resources n test obectve usng 9 resources. (4): the number of selected test cases for dagnoss s ranged from 2 to 6. We dentfed test obectves whch contan only useful test cases for dagnoss and some whch contan many nonrelevant test cases for dagnoss. For example, only 6 relevant test cases are selected for a test obectve composed of 18. The followng fgure shows an llustraton of a set of ndstngushable resources (see Fgure 12 (a)) and equvalent test cases (see Fgure 12 (b)) dentfed n our case study. Fgure 12: dagnosablty nformaton In Fgure 12 (a), the crcut-breaker and the swtch are ndstngushable. Fgure 12 (b) hghlghts test cases T 3 and T 5 are equvalent or redundant for dagnoss regardng T 1. The three remanng test cases (T 1, T 2 and T 4 ) represent the dagnoss set. Concernng ndstngushable resources nformaton, the Multple-Clue approach applcaton on our case study provdes the followng nformaton: 47 resources among 100 belong to an ndstngushable set and the average of the number of resources ncluded n an ndstngushable set s 4 or 5 resources. The resources dagnosablty mprovement method (see secton 5) s then appled to the case study. Table 3 presents the enhanced results n terms of ndstngushable resources. Table 3: Dagnosablty mprovement results Mn Max Number of ndstngushable sets (5) 0 4 Sze of ndstngushable sets (6) 2 4 The table below hghlghts the followng nformaton: (5): the maxmum number of sets of ndstngushable resources dentfed n a test obectve s decreased from 6 (see Table 2) to 4. (6): the maxmum sze of sets of ndstngushable resources also s decreased from 7 (see Table 2) to 4. As a result, 33 modeled resources among 100 resources reman ndstngushable; 14 resources become dstngushable thanks to the mprovement method and the average of the number of resources ncluded n an ndstngushable set s reduced to 2 or 3 (aganst 4 or 5 before the mprovement method mplementaton). These experment results, reducng the number of ndstngushable resources, confrm that the dagnosablty mprovement approach s an nterestng method for systems testng process on AIRBUS FAL. Table 4 summarzes the results of the applcaton of our methodology. In ths representaton, Before means: data relyng on AIRBUS current methodology based on engneerng udgment and After means: data provded by the methodology descrbed n ths paper (see secton 6). Table 4: Quanttatve benefts Number Before After Selected test cases Redundant test cases unknown 30 Indstngushable sets Average sze of sets ndstngushable resources 4 or 5 2 or 3 Consderng test cases defnton stage, our approach hghlghts the followng useful nformaton: Redundant test cases dentfcaton: 30 test cases are ponted out as redundant or equvalent for dagnoss. We had no ndcaton about redundant test cases before ths expermentaton. Ths nformaton can be exploted by test desgners n order to check f these test cases are also redundant from a functonal pont of vew. Indstngushable sets dentfcaton: ths nformaton, proposed n general after dagnoss, s now avalable n the test cases defnton phase. A possble use of ths nformaton s that test desgners can decde to defne addtonal test cases or to foresee approprate test tools n order to mprove performance n the dagnoss stage. Lecture 1.2 INTERNATIONAL TEST CONFERENCE 8

9 Selected test cases or dagnoss set: From test cases defnton phase, expermented methodology dentfes relevant test cases (80 among 169) for dagnoss. Ths nformaton allows test desgners to functonally analyze the remanng test cases n order to take a decson regardng ther utlty. For the dagnoss stage, our approach also provdes nformaton about: Faulty resource dentfcaton: for each test obectve, the selected dagnoss set allows the constructon of the dagnoss tree (see Fgure 2) helpng faulty dstngushable resource dentfcaton. As a result, the number of test case executon results to consder for dagnoss s reduced and ths contrbutes to troubleshootng effort reducton. Resources dagnosablty mprovement: The suspcon of a set of ndstngushable resources s common durng systems verfcaton on FAL. Informaton gven by our approach can be used to gude next test obectves to verfy n order to dentfy the faulty resource or reduce the number of suspected resources. 8. Concluson and Future work Ths paper exposes a methodology supportng system testng process on AIRBUS FALs. It provdes nformaton about dagnoss effort from the test desgn phase. Ths methodology contrbutes to reduce testng effort: helpng the defnton of relevant and nonredundant test cases and gudng faulty resources and ndstngushable sets dentfcaton (troubleshootng) actvtes. Our future work wll focus on three man obectves: consoldatng ths approach through new experments n order to confrm the frst results and to check ts scalablty, check the ablty of end-users to use the tool prototype n operatonal condtons and, ultmately, ntegrate the dagnosablty assessment prototype n operatonal systems testng tools used on AIRBUS FALs. References [1] Voas, J.M., Software testablty measurement for asserton placement and fault localzaton. In: 2nd Internatonal Workshop on Automated and Algorthmc Debuggng (AADEBUG'95), Sant-Malo, France, [2] J. Sheppard and W. Smpson., System Test and Dagnoss. In: Kluwer Academc Publshers [3] Manley, D. Eklow, B., A Model Based Automated Debug Process. In: IEEE Board Test Workshop [4] Khall, M., Le Traon, Y., Robach, C., Automated strateges for software dagnoss. In: Proceedngs of the IEEE Internatonal Symposum on Software Relablty Engneerng (ISSRE'98), Paderborn, Germany, November [5] Robach C. and Wodey P. Lnkng Desgn And Test Tools: An mplementaton, IEEE Transacton on ndustral electropncs Vol 36, No. 2, May pp [6] Gallagher K.B. and Lyle J. R. Usng Program Slcng In Software Mantenance, IEEE Transacton on Software Engneerng, Vol. 17, No. 8, Aug, Lecture 1.2 INTERNATIONAL TEST CONFERENCE 9