Boosted Decision Trees for Behaviour Mining of Concurrent Programs

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Boosted Decision Trees for Behaviour Mining of Concurrent Programs

Popis výsledku v původním jazyce

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).

Název v anglickém jazyce

Boosted Decision Trees for Behaviour Mining of Concurrent Programs

Popis výsledku anglicky

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).

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Concurrency Computation Practice and Experience

ISSN

1532-0626

e-ISSN

1532-0634

Svazek periodika

29

Číslo periodika v rámci svazku

21

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

21

Strana od-do

4268-4289

Kód UT WoS článku

000412299700010

EID výsledku v databázi Scopus

2-s2.0-85028664932