Thesis Details
Application of Genetic Algorithms and Data Mining in Noise-based Testing of Concurrent Software
This thesis proposes an improvement of the efficiency of testing concurrent software by employing data mining techniques and genetic algorithms in the process of testing concurrent software. Concurrent, or multi-threaded, programming has become very popular over the last few years. However, as the concurrent programming is far more demanding the sequential programming, its increased use leads to a significant increase in the number of errors that appear in commercial software due to errors in synchronization. Finding such errors using traditional testing methods is difficult. Moreover, repeated test executions of traditional testing that are performed in the same environment will typically examine similar interleavings only. Hence, the noise-based injection approach is used for influencing the scheduling by injecting various kinds of noise (delays, context switches, and so on) into the common thread behaviour which stress the software and can to show some rare behaviour. However, for the 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 work, there are used data mining methods and genetic algorithms and their combinations to deal with the problem of choosing such noise injection heuristics and values of their parameters. Besides setting up of the goals of the thesis, this proposal also provides a brief summary of the state of the art in application of data mining techniques and genetic algorithms to program testing problems.
testing, concurrent programs, data mining, genetic algorithms, AdaBoost, LASSO algorithm, noise injection
@phdthesis{FITPT776,
author = "Hana \v{S}imkov\'{a}",
type = "Ph.D. thesis",
title = "Application of Genetic Algorithms and Data Mining in Noise-based Testing of Concurrent Software",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2020,
location = "Brno, CZ",
language = "english",
url = "https://www.fit.vut.cz/study/phd-thesis/776/"
}