Publication Details
Boosted Decision Trees for Behaviour Mining of Concurrent Programs
Letko Zdeněk, Ing., Ph.D. (CM-SFE)
Křena Bohuslav, Ing., Ph.D. (DITS)
Vojnar Tomáš, prof. Ing., Ph.D. (DITS)
Dudka Vendula, Ing.
Avros Renata (FIT)
Ur Shmuel
Volkovich Zeev (FIT)
Testing, noise injection, classification, AdaBoost, multi-threaded programs
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.
@inproceedings{BUT111631,
author="Hana {Šimková} and Zdeněk {Letko} and Bohuslav {Křena} and Tomáš {Vojnar} and Vendula {Dudka} and Renata {Avros} and Shmuel {Ur} and Zeev {Volkovich}",
title="Boosted Decision Trees for Behaviour Mining of Concurrent Programs",
booktitle="Proceedings of MEMICS'14",
year="2014",
pages="15--27",
publisher="NOVPRESS s.r.o.",
address="Brno",
isbn="978-80-214-5022-6"
}