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%2F14%3APU112036" target="_blank" >RIV/00216305:26230/14:PU112036 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Boosted Decision Trees for Behaviour Mining of Concurrent Programs

Popis výsledku v původním jazyce

Testing of concurrent programs is difficult since the scheduling non-determinism requires one to test a huge number of different thread interleavings. Moreover, a simple repetition of test executions will typically examine similar interleavings only. One popular way how to deal with thisproblem is to use the noise injection approach, which is, however, parameterized with many parameters whose suitable values are difficult to find. In this paper,we propose a novel application of classification-based data mining for this purpose. Our approach can identify which test and noise parameters are the most influential for a given program and a given testing goal and which values (orranges of values) of these parameters are suitable for meeting this goal. We present experiments that show that our approach can indeed fully automaticallyimprove noise-based testing of particular programs with a~particular testing goal. At the same time, we use it to obtain new general insights into noise-based testing as well.

Název v anglickém jazyce

Boosted Decision Trees for Behaviour Mining of Concurrent Programs

Popis výsledku anglicky

Testing of concurrent programs is difficult since the scheduling non-determinism requires one to test a huge number of different thread interleavings. Moreover, a simple repetition of test executions will typically examine similar interleavings only. One popular way how to deal with thisproblem is to use the noise injection approach, which is, however, parameterized with many parameters whose suitable values are difficult to find. In this paper,we propose a novel application of classification-based data mining for this purpose. Our approach can identify which test and noise parameters are the most influential for a given program and a given testing goal and which values (orranges of values) of these parameters are suitable for meeting this goal. We present experiments that show that our approach can indeed fully automaticallyimprove noise-based testing of particular programs with a~particular testing goal. At the same time, we use it to obtain new general insights into noise-based testing as well.

Druh

D - Stať ve sborníku

CEP obor

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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)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of MEMICS'14

ISBN

978-80-214-5022-6

ISSN

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e-ISSN

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Počet stran výsledku

12

Strana od-do

15-27

Název nakladatele

NOVPRESS s.r.o.

Místo vydání

Brno

Místo konání akce

Telč

Datum konání akce

17. 10. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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