Detecting arrays for main effects

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Determining correctness and performance for complex engineered systems necessitates testing the system to determine how its behaviour is impacted by many factors and interactions among them. Of particular concern is to determine which settings of the factors (main effects) impact the behaviour significantly. Detecting arrays for main effects are test suites that ensure that the impact of each main effect is witnessed even in the presence of d or fewer other significant main effects. Separation in detecting arrays dictates the presence of at least a specified number of such witnesses. A new parameter, corroboration, enables the fusion of levels while maintaining the presence of witnesses. Detecting arrays for main effects, having various values for the separation and corroboration, are constructed using error-correcting codes and separating hash families. The techniques are shown to yield explicit constructions with few tests for large numbers of factors.

Original languageEnglish (US)
Title of host publicationAlgebraic Informatics - 8th International Conference, CAI 2019, Proceedings
EditorsMiroslav Ćirić, Manfred Droste, Jean-Éric Pin
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783030213626
StatePublished - 2019
Event8th International Conference on Algebraic Informatics, CAI 2019 - Niš, Serbia
Duration: Jun 30 2019Jul 4 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11545 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th International Conference on Algebraic Informatics, CAI 2019

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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