License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.07161.9
URN: urn:nbn:de:0030-drops-13875
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2008/1387/
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Baskiotis, Nicolas ; Sebag, Michele

Structural Sampling for Statistical Software Testing

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07161.SebagMichele.Paper.1387.pdf (0.2 MB)

Abstract

Structural Statistical Software Testing exploits the control flow
graph of the program being tested to construct test cases.
While test cases can easily be extracted from {em feasible paths} in the control
flow graph, that is, paths which are actually exerted for some
values of the program input, the feasible path region is a tiny fraction
of the graph paths (less than $10^{-5}]$ for medium size programs).
The S4T algorithm presented in this paper aims to address this limitation;
as an Active Relational Learning Algorithm, it uses the few feasible paths
initially available to sample new feasible paths. The difficulty comes
from the non-Markovian nature of the feasible path concept, due to the
long-range dependencies between the nodes in the control flow graph.
Experimental validation on real-world and artificial problems
demonstrates significant improvements compared to the state of the art.


BibTeX - Entry

@InProceedings{baskiotis_et_al:DagSemProc.07161.9,
  author =	{Baskiotis, Nicolas and Sebag, Michele},
  title =	{{Structural Sampling for Statistical Software Testing}},
  booktitle =	{Probabilistic, Logical and Relational Learning - A Further Synthesis},
  pages =	{1--13},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{7161},
  editor =	{Luc de Raedt and Thomas Dietterich and Lise Getoor and Kristian Kersting and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2008/1387},
  URN =		{urn:nbn:de:0030-drops-13875},
  doi =		{10.4230/DagSemProc.07161.9},
  annote =	{Keywords: Active Relational Learning, Software Testing, Autonomic Computing, Parikh Maps}
}

Keywords: Active Relational Learning, Software Testing, Autonomic Computing, Parikh Maps
Collection: 07161 - Probabilistic, Logical and Relational Learning - A Further Synthesis
Issue Date: 2008
Date of publication: 06.03.2008

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