Uncertainty-Driven Black-Box Test Data Generation

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  title =        "Uncertainty-Driven Black-Box Test Data Generation",
  author =       "Neil Walkinshaw and Gordon Fraser",
  year =         "2016",
  month =        aug # "~10",
  abstract =     "We can never be certain that a software system is
                 correct simply by testing it, but with every additional
                 successful test we become less uncertain about its
                 correctness. In absence of source code or elaborate
                 specifications and models, tests are usually generated
                 or chosen randomly. However, rather than randomly
                 choosing tests, it would be preferable to choose those
                 tests that decrease our uncertainty about correctness
                 the most. In order to guide test generation, we apply
                 what is referred to in Machine Learning as Query
                 Strategy Framework: We infer a behavioural model of the
                 system under test and select those tests which the
                 inferred model is least certain about. Running these
                 tests on the system under test thus directly targets
                 those parts about which tests so far have failed to
                 inform the model. We provide an implementation that
                 uses a genetic programming engine for model inference
                 in order to enable an uncertainty sampling technique
                 known as query by committee, and evaluate it on eight
                 subject systems from the Apache Commons Math framework
                 and JodaTime. The results indicate that test generation
                 using uncertainty sampling outperforms conventional and
                 Adaptive Random Testing.",
  bibsource =    "OAI-PMH server at export.arxiv.org",
  oai =          "oai:arXiv.org:1608.03181",
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 software engineering",
  URL =          "http://arxiv.org/abs/1608.03181",
  notes =        "See \cite{Walkinshaw:2017:ICST}",

Genetic Programming entries for Neil Walkinshaw Gordon Fraser