Computer Science ›› 2018, Vol. 45 ›› Issue (9): 75-80.doi: 10.11896/j.issn.1002-137X.2018.09.011

• NASAC 2017 • Previous Articles     Next Articles

Modeling in Multiple Views and Industrial Case Study of Automatic Test for Hardware System

MENG Han, WU Ji, HU Jing-hui, LIU Chao, YANG Hai-yan, SUN Xin-ying   

  1. School of Computer Science and Engineering,Beihang University,Beijing 100191,China
  • Received:2017-10-07 Online:2018-09-20 Published:2018-10-10

Abstract: The development of ATE(Automatic Test Equipment) of hardwore system is a tedious task.It requires developers to understand various information about external ports,signals,test procedures and signal inspections of mea-sured equipment from specialists,thus comfirming the demand of development.In this process,the most complicated issue for developers is the lack of a normative model,which can describe the test information from various collaborators.This issue results in several problems of verboseness of test documents,difficulty of understanding and excess of errors.This paper proposed anATE domain-oriented multi-angle views modeling method.This model can normalize the test information of ATE and support the consistency checking.In the meantime,this paper gave an industrial case to demonstrate the effectiveness of the model.

Key words: ATE, Test documents, Multi-angle views modeling method, Consistency

CLC Number: 

  • TP311
[1] ISARD M.Autopilot:automatic data center management[J].Acm Sigops Operating Systems Review,2007,41(2):60-67.
[2] MJ V D L,DUDOIT S,POLLARD K S.Augmentation procedures for control of the generalized family-wise error rate and tail probabilities for the proportion of false positives[J].Statistical Applications in Genetics & Molecular Biology,2004,3(1):63-68.
[3] YANG M.Application of Standardized Hypertext and Info Database to Auto Test Equipment Software[J].Computer Mea-surement & Control,2009,25(Suppl 1):131-138.
[4] DRENKOW G.Future Test System Architecture[C]∥IEEEAutotestcon.New York: IEEE Press,2004:49-56.
[5] ROSS W A.The Impact of Nest Generation Test Technology on Aviation Maintenance[C]∥IEEE Autotestcon.New York:IEEE Press,2003:59-66.
[6] IEEE-SA Standards Board.IEEE trial-use standard for a broadbased environment for test(ABBET):IEEE Std 1226-1998[S].New York:IEEE,1993.
[7] WENG W,JIANU O A.A Smart Sensing Unit for Vibration Measurement and Monitoring[J].IEEE/ASME Transactiona on Mechatronica,2010,2(15):70-78.
[8] LI E H,LIANG X.Software design for supporting parallel test of automatic test system[J].Measurement and control technology,2008,27(3):7-9.
[9] OMG (Object Management Group).Object Constraint Lan-guage 2.3.1[J].Cmis.brighton.ac.uk,2012,91(44Part2):137-145.
[10] RICHTERS M,GOGOLLA M.Validating UML Models andOCL Constraints[J].Lecture Notes in Computer Science,2000,1939:265-277.
[11] AKEHURST D H,BORDBAR B.On Querying UML DataModels with OCL[C]∥International Conference on the Unified Modeling Language,Modeling Languages,Concepts,and TOOLS.Springer-Verlag,2001:91-103.
[12] SUMATHI S,SUREKHA P.LabVIEW based Advanced In-strumentation Systems[M].Berlin:Springer Berlin Heidelberg,2007.
[13] KLINGER T.Image Processing with LabVIEW and IMAQ Vision[C]∥Pearson Education.2003:87-99.
[14] BEIZER B.Black-box testing: techniques for functional testing of software and systems[M].New Jersey:John Wiley & Sons,Inc.,1995:98-117.
[15] JORGENSEN A,WHITTAKER J A.An API testing method[C]∥Proceedings of the International Conference on Software Testing Analysis & Review (STAREAST 2000).2000.
[16] VIERIA M,DIAS M,RICHARDSON D.Object-Oriented Specification-Based Testing Using UML Statechart Diagrams[M].New York: ACM Press,2000:246-247.
[17] POORE J H.Introduction to the special issue on: model-based statistical testing of software intensive systems[J].Information and Software Technology,2000,42(12):797-799.
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