Boosted Decision Trees for Behaviour Mining of Concurrent Programs

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F17%3APU126463" target="_blank" >RIV/00216305:26230/17:PU126463 - isvavai.cz</a>

Result on the web

<a href="http://onlinelibrary.wiley.com/doi/10.1002/cpe.4268/abstract;jsessionid=609089BF58372A54AE23CD0097729CC2.f02t01" target="_blank" >http://onlinelibrary.wiley.com/doi/10.1002/cpe.4268/abstract;jsessionid=609089BF58372A54AE23CD0097729CC2.f02t01</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/cpe.4268" target="_blank" >10.1002/cpe.4268</a>

Result language

angličtina

Original language name

Boosted Decision Trees for Behaviour Mining of Concurrent Programs

Original language description

Testing of concurrent programmes is difficult since the scheduling nondeterminism requires one to test a huge number of different thread interleavings. Moreover, repeated test executions that are performed in the same environment will typically examine similar interleavings only. One possible way how to deal with this problem is to use the noise injection approach, which influences the scheduling by injecting various kinds of noise (delays, context switches, etc) into the common thread behaviour. However, for noise injection to be efficient, one has to choose suitable noise injection heuristics from among the many existing ones as well as to suitably choose values of their various parameters, which is not easy. In this paper, we propose a novel way how to deal with the problem of choosing suitable noise injection heuristics and suitable values of their parameters (as well as suitable values of parameters of the programmes being tested themselves). Here, by suitable, we mean such settings that maximize chances of meeting a given testing goal (such as, eg, maximizing coverage of rare behaviours and thus maximizing chances to find rarely occurring concurrency-related bugs). Our approach is, in particular, based on using data mining in the context of noise-based testing to get more insight about the importance of the different heuristics in a particular testing context as well as to improve fully automated noise-based testing (in combination with both random as well as genetically optimized noise setting).

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Concurrency Computation Practice and Experience

ISSN

1532-0626

e-ISSN

1532-0634

Volume of the periodical

29

Issue of the periodical within the volume

21

Country of publishing house

GB - UNITED KINGDOM

Number of pages

21

Pages from-to

4268-4289

UT code for WoS article

000412299700010

EID of the result in the Scopus database

2-s2.0-85028664932