By the same authors

Influential Nuisance Factors on a Decision of Sufficient Testing

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Publication details

Title of host publicationAlgorithms and Architectures for Parallel Processing
DatePublished - 1 Nov 2015
Pages819-828
Number of pages10
PublisherSpringer
EditorsGuojun Wang, Albert Zomaya, Gregorio Martinez Perez, Kenli Li
Original languageUndefined/Unknown
ISBN (Electronic)978-3-319-27161-3
ISBN (Print)978-3-319-27160-6

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume9532
ISSN (Electronic)0302-9743

Abstract

Testing of safety-critical embedded systems is an important and costly endeavour. To date work has been mainly focusing on the design and application of diverse testing strategies. However, they have left an open research issue of when to stop testing a system. In our previous work, we proposed a convergence algorithm that informs the tester when the current testing strategy does not seem to be revealing new insight into the worst-case timing properties of system tasks, hence, should be stopped. This algorithm was shown to be successful while being applied across task sets having similar characteristics. For the convergence algorithm to become robust, it is important that it holds even if the task set characteristics here called nuisance factors, vary. Generally speaking, there might be either the main factors under analysis, called design factors, or nuisance factors that influence the performance of a process or system. Nuisance factors are not typically of interest in the context of the analysis. However, they vary from system to system and may have large effects on the performance, hence, being very important to be accounted for. Consequently, the current paper looks into a set of nuisance factors that affect our proposed convergence algorithm performance. More specifically, it is interested in situations when the convergence algorithm performance significantly degrades influencing its reliability. The work systematically analyzes each nuisance factor effect using a well-known statistical method, further, derives the most influential factors.

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