SoftwareTesting with Active Learning in a Graph

Baskiotis, Nicolas; Sebag, Michèle; Gaudel, Marie-Claude

doi:10.4230/DagSemProc.08351.7

Document

SoftwareTesting with Active Learning in a Graph

Authors Nicolas Baskiotis, Michèle Sebag, Marie-Claude Gaudel

Part of: Volume: Dagstuhl Seminar Proceedings, Volume 8351
Part of: Series: Dagstuhl Seminar Proceedings (DagSemProc)
License: Creative Commons Attribution 4.0 International license
Publication Date: 2009-05-25

PDF

File

PDF

DagSemProc.08351.7.pdf

Filesize: 259 kB
12 pages

Document Identifiers

DOI: 10.4230/DagSemProc.08351.7
URN: urn:nbn:de:0030-drops-20149

Subject Classification

Keywords

Structural Statistical Software Testing
Active Learning
Control Flow Graph
Feaisble Paths
Parikh maps.

Metrics

Access Statistics
Total Accesses (updated on a weekly basis)

0

PDF Downloads

0

Metadata Views

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.

Cite As Get BibTex

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

Author Details

Nicolas Baskiotis

Michèle Sebag

Marie-Claude Gaudel

Questions / Remarks / Feedback

Feedback for Dagstuhl Publishing

Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail