Wizard Templates 1 TPS Wizard n n n

Wizard Templates 1

TPS Wizard™ n n n n Delivered to OEMs as part of LM-STAR® SDK on JSF. Simplifies task of creating Test. Stand™ sequences. Implements JSF LM-STAR® TPS Style Guide. Reduces development time. Provides a repeatable process. Allows engineers to concentrate on test requirements. Groups all Test. Stand related. information in a single tool. Hides Test. Stand™ complexity. 2

Test. Stand™ Sequence Structure 3

TPS Wizard™ Architecture 4

TPS Wizard™ User Interface 5

Autotestcon Configuration 6

Custom Steps n n n Provide a simple Dialog GUI interface to configure Instrument Parameters. Implements Range Checking for Parameters. Implements on-line help and “tooltips”. Information stored within Step Properties. Execute retrieves information from Step Properties and calls Instrument Handler. 7

Future TPS Wizard™ Enhancements n Generate TDML data files. n Capability to view test data as TDML fragments. n Capability to configure Custom Step Properties. n Integrate P 1641 within TPS Wizard™. n Generate Lab. Windows™/CVI™ Test Wrappers. 8

Intelligent ATS/TPS Architecture n AI-ESTATE (Artificial Intelligence Exchange and Service Tie to All Test Environments) compliant system. TM n AI-ESTATE is IEEE Standard 1232 -2002. n The standard defines a set of specifications “for exchanging 1 diagnostic knowledge and constructing diagnostic reasoners”. User Interface Diagnostic Reasoner(s) Application Executive Test and Maintenance Information Management Center (TMIC) Test Controller(s) 1 Introduction to IEEE Std 1232 -2002. 9

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Common Development Framework (CDF) n n n LM STS developed and maintained product. J 2 EE-based application framework. Provides a high level of decoupling between architectural layers. All workflow processes are described and configured using XML. Used to maintain session information and to direct messages between the Client, Reasoner, Database and Test Executive. 11

Bayesian Network n n n Provides a basis for determining the likelihood of all possible outcomes, taking into account what is known (observed data). Example scenario: “Given the fact that BIT Code 010011 is reported, Tests 1 through 5 have passed, and Test 6 has failed: What is the probability that A 1 is the faulty component? What is the probability that A 3 is the faulty component? Relationships and probability distribution are generated in an off-line “sustaining engineering” process. 12