NDLI: Automated inference of classifications and dependencies for combinatorial testing

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Automated inference of classifications and dependencies for combinatorial testing

Content Provider	ACM Digital Library
Author	Nguyen, Cu Duy Tonella, Paolo
Abstract	Even for small programs, the input space is huge - often unbounded. Partition testing divides the input space into disjoint equivalence classes and combinatorial testing selects a subset of all possible input class combinations, according to criteria such as pairwise coverage. The down side of this approach is that the partitioning of the input space into equivalence classes (input classification) is done manually. It is expensive and requires deep domain and implementation understanding. In this paper, we propose a novel approach to classify test inputs and their dependencies automatically. Firstly, random (or automatically generated) input vectors are sent to the system under test (SUT). For each input vector, an observed "hit vector" is produced by monitoring the execution of the SUT. Secondly, hit vectors are grouped into clusters using machine learning. Each cluster contains similar hit vectors, i.e., similar behaviors, and from them we obtain corresponding clusters of input vectors. Input classes are then extracted for each input parameter straightforwardly. Our experiments with a number of subjects show good results as the automatically generated classifications are the same or very close to the expected ones.
Starting Page	622
Ending Page	627
Page Count	6
File Format	PDF
ISBN	9781479902156
DOI	10.1109/ASE.2013.6693123
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-11-11
Access Restriction	Subscribed
Subject Keyword	Invariant inference Combinatorial testing Automated input classifications
Content Type	Text
Resource Type	Article

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