DagSemProc.08351.7.pdf
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SoftwareTesting with Active Learning in a Graph
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Dagstuhl Seminar Proceedings, Volume 8351
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2009-05-25
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Nicolas Baskiotis, Michèle Sebag, and Marie-Claude Gaudel. SoftwareTesting with Active Learning in a Graph. In Evolutionary Test Generation. Dagstuhl Seminar Proceedings, Volume 8351, pp. 1-12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)
https://doi.org/10.4230/DagSemProc.08351.7
https://doi.org/10.4230/DagSemProc.08351.7
Abstract
Motivated by Structural Statistical Software Testing (SSST), this paper
is interested in sampling the feasible execution paths in the
control flow graph of the program being tested. For some complex programs,
the fraction of feasible paths becomes tiny, ranging in
$[10^{-10}, 10^{-5}]$. When relying on the uniform sampling of the
program paths, SSST is thus hindered by
the non-Markovian nature of the ``feasible path'' concept, due to the
long-range dependencies between the program nodes.
A divide and generate approach relying on an extended Parikh Map
representation is proposed to address this limitation;
experimental validation on real-world
and artificial problems demonstrates gains of orders of magnitude compared
to the state of the art.
Keywords
- Structural Statistical Software Testing
- Active Learning
- Control Flow Graph
- Feaisble Paths
- Parikh maps.
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