Detail publikačního výsledku

Multi-objective Genetic Optimization for Noise-Based Testing of Concurrent Software

DUDKA, V.; KŘENA, B.; LETKO, Z.; ŠIMKOVÁ, H.; VOJNAR, T.

Originální název

Multi-objective Genetic Optimization for Noise-Based Testing of Concurrent Software

Anglický název

Multi-objective Genetic Optimization for Noise-Based Testing of Concurrent Software

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt


Testing of multi-threaded programs is
a~demanding work due to the many possible thread interleavings one should
examine. The noise injection technique helps to increase the number of thread
interleavings examined during repeated test executions provided that a~suitable
setting of noise injection heuristics is used. The problem of finding such
a~setting, i.e., the so called test and noise configuration search problem (TNCS
problem), is not easy to solve. In this paper, we show how to apply
a~multi-objective genetic algorithm (MOGA) to the TNCS problem. In particular,
we focus on generation of TNCS solutions that cover a~high number of distinct
interleavings (especially those which are rare) and provide stable results at
the same time. To achieve this goal, we study suitable metrics and ways how to
suppress effects of non-deterministic thread scheduling on the proposed
MOGA-based approach. We also discuss a~choice of a~concrete MOGA and its
parameters suitable for our setting.  Finally, we show on a~set of benchmark
programs that our approach provides better results when compared to the commonly
used random approach as well as to the sooner proposed use of a~single-objective
genetic approach.

Anglický abstrakt


Testing of multi-threaded programs is
a~demanding work due to the many possible thread interleavings one should
examine. The noise injection technique helps to increase the number of thread
interleavings examined during repeated test executions provided that a~suitable
setting of noise injection heuristics is used. The problem of finding such
a~setting, i.e., the so called test and noise configuration search problem (TNCS
problem), is not easy to solve. In this paper, we show how to apply
a~multi-objective genetic algorithm (MOGA) to the TNCS problem. In particular,
we focus on generation of TNCS solutions that cover a~high number of distinct
interleavings (especially those which are rare) and provide stable results at
the same time. To achieve this goal, we study suitable metrics and ways how to
suppress effects of non-deterministic thread scheduling on the proposed
MOGA-based approach. We also discuss a~choice of a~concrete MOGA and its
parameters suitable for our setting.  Finally, we show on a~set of benchmark
programs that our approach provides better results when compared to the commonly
used random approach as well as to the sooner proposed use of a~single-objective
genetic approach.

Klíčová slova

Concurrent programs, testing, noise injection, genetic algorithm, multi-objective

Klíčová slova v angličtině

Concurrent programs, testing, noise injection, genetic algorithm, multi-objective

Autoři

DUDKA, V.; KŘENA, B.; LETKO, Z.; ŠIMKOVÁ, H.; VOJNAR, T.

Rok RIV

2017

Vydáno

25.08.2014

Nakladatel

Springer Verlag

Místo

Heidelberg

ISBN

978-3-319-09939-2

Kniha

SSBSE'14

Edice

Lecture Notes in Computer Science

Svazek

8636

Strany od

107

Strany do

122

Strany počet

16

BibTex

@inproceedings{BUT111622,
  author="Vendula {Dudka} and Bohuslav {Křena} and Zdeněk {Letko} and Hana {Šimková} and Tomáš {Vojnar}",
  title="Multi-objective Genetic Optimization for Noise-Based Testing of Concurrent Software",
  booktitle="SSBSE'14",
  year="2014",
  series="Lecture Notes in Computer Science",
  volume="8636",
  pages="107--122",
  publisher="Springer Verlag",
  address="Heidelberg",
  doi="10.1007/978-3-319-09940-8\{_}8",
  isbn="978-3-319-09939-2"
}