The compaction of test programs for processor-based systems is of utmost practical importance: Software-Based Self-Test (SBST) is nowadays increasingly adopted, especially for in-field test of safety-critical applications, and both the size and the execution time of the test are critical parameters. However, while compacting the size of binary test sequences has been thoroughly studied over the years, the reduction of the execution time of test programs is still a rather unexplored area of research. This paper describes a family of algorithms able to automatically enhance an existing test program, reducing the time required to run it and, as a side effect, its size. The proposed solutions are based on instruction removal and restoration, which is shown to be computationally more efficient than instruction removal alone. Experimental results demonstrate the compaction capabilities, and allow analyzing computational costs and effectiveness of the different algorithms.

New Techniques to Reduce the Execution Time of Functional Test Programs / Gaudesi, Marco; Pomeranz, Irith; SONZA REORDA, Matteo; Squillero, Giovanni. - In: IEEE TRANSACTIONS ON COMPUTERS. - ISSN 0018-9340. - STAMPA. - 66:7(2017), pp. 1268-1273. [10.1109/TC.2016.2643663]

New Techniques to Reduce the Execution Time of Functional Test Programs

SONZA REORDA, Matteo;SQUILLERO, GIOVANNI
2017

Abstract

The compaction of test programs for processor-based systems is of utmost practical importance: Software-Based Self-Test (SBST) is nowadays increasingly adopted, especially for in-field test of safety-critical applications, and both the size and the execution time of the test are critical parameters. However, while compacting the size of binary test sequences has been thoroughly studied over the years, the reduction of the execution time of test programs is still a rather unexplored area of research. This paper describes a family of algorithms able to automatically enhance an existing test program, reducing the time required to run it and, as a side effect, its size. The proposed solutions are based on instruction removal and restoration, which is shown to be computationally more efficient than instruction removal alone. Experimental results demonstrate the compaction capabilities, and allow analyzing computational costs and effectiveness of the different algorithms.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2666242
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