The IEEE 1687 standard describes reconfigurable structures allowing to flexibly access the instruments existing within devices (e.g., to support test, debug, calibration, etc.), by the use of configurable modules acting as controllable switches. The increasing adoption of this standard requires the availability of algorithms and tools to automate its usage. Since the resulting networks could inevitably be affected by defects which may prevent their correct usage, solutions allowing not only to test against these defects, but also to diagnose them (i.e., to identify the location of possible faults) are of uttermost importance. This paper proposes a method to automatically generate suitable test stimuli: by applying them and observing the output of the network one can not only detect possible faults, but also identify the fault responsible for the misbehavior. Experimental results gathered on a set of benchmark networks with a prototypical tool implementing the proposed techniques show the feasibility and provide a first idea about the length of the required input stimuli.

Automatic generation of stimuli for fault diagnosis in IEEE 1687 networks / Cantoro, Riccardo; Montazeri, M.; SONZA REORDA, Matteo; Zadegan, F. Ghani; Larsson, E.. - STAMPA. - (2016), pp. 167-172. (Intervento presentato al convegno 2016 IEEE 22nd International Symposium on On-Line Testing and Robust System Design (IOLTS) nel 4-6 July 2016) [10.1109/IOLTS.2016.7604692].

Automatic generation of stimuli for fault diagnosis in IEEE 1687 networks

CANTORO, RICCARDO;SONZA REORDA, Matteo;
2016

Abstract

The IEEE 1687 standard describes reconfigurable structures allowing to flexibly access the instruments existing within devices (e.g., to support test, debug, calibration, etc.), by the use of configurable modules acting as controllable switches. The increasing adoption of this standard requires the availability of algorithms and tools to automate its usage. Since the resulting networks could inevitably be affected by defects which may prevent their correct usage, solutions allowing not only to test against these defects, but also to diagnose them (i.e., to identify the location of possible faults) are of uttermost importance. This paper proposes a method to automatically generate suitable test stimuli: by applying them and observing the output of the network one can not only detect possible faults, but also identify the fault responsible for the misbehavior. Experimental results gathered on a set of benchmark networks with a prototypical tool implementing the proposed techniques show the feasibility and provide a first idea about the length of the required input stimuli.
2016
978-1-5090-1507-8
978-1-5090-1507-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2654310
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