University of Leicester
Browse

Minimizing Characterizing sets

Download (1.19 MB)
journal contribution
posted on 2021-03-23, 14:40 authored by Uraz Turker, Robert Hierons, Guy-Vincent Jourdan
A characterizing set (CS) for a deterministic finite state machine (FSM)Mis a set of input sequences that, between them, separate (distinguish) all of the states of M. CSs are used within several test generation techniques that return test suites with guaranteed fault detection power. The number of input sequences in a CS directly affects the cost of applying the resultant test suite. In this paper, we study the complexity of decision problems associated with deriving a smallest CS from an FSM, showing that checking the existence of a CS with K sequences is PSPACE-complete. We also consider the length of a CS, which is the sum of the lengths of the input sequences in the CS. It transpirest hat the problem of deciding whether there is a CS with length at most KisNP-complete. Motivated by these results, we introduce a heuristic to construct a CS, from a deterministic FSM, with the aim of minimizing the number of input sequences. We evaluated the proposed algorithm by assessing its effect when used within a classical test generation algorithm (the W-method). In the evaluation, we used both randomly generated FSMs and benchmark FSMs. The results are promising, with the proposed algorithm reducing the number of test sequences by 37.3% and decreasing the total length of the test suites by 34.6%on average.

History

Citation

Science of Computer Programming Volume 208, 1 August 2021, 102645

Author affiliation

School of Informatics

Version

  • AM (Accepted Manuscript)

Published in

Science of Computer Programming

Volume

208

Publisher

Elsevier

issn

0167-6423

Acceptance date

2021-03-16

Copyright date

2021

Available date

2022-04-09

Language

en

Usage metrics

    University of Leicester Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC