7,645 research outputs found
How to Knit Your Own Markov Blanket
Hohwy (Hohwy 2016, Hohwy 2017) argues there is a tension between the free energy principle and leading depictions of mind as embodied, enactive, and extended (so-called ‘EEE1 cognition’). The tension is traced to the importance, in free energy formulations, of a conception of mind and agency that depends upon the presence of a ‘Markov blanket’ demarcating the agent from the surrounding world. In what follows I show that the Markov blanket considerations do not, in fact, lead to the kinds of tension that Hohwy depicts. On the contrary, they actively favour the EEE story. This is because the Markov property, as exemplified in biological agents, picks out neither a unique nor a stationary boundary. It is this multiplicity and mutability– rather than the absence of agent-environment boundaries as such - that EEE cognition celebrates
SINVAD: Search-based Image Space Navigation for DNN Image Classifier Test Input Generation
The testing of Deep Neural Networks (DNNs) has become increasingly important
as DNNs are widely adopted by safety critical systems. While many test adequacy
criteria have been suggested, automated test input generation for many types of
DNNs remains a challenge because the raw input space is too large to randomly
sample or to navigate and search for plausible inputs. Consequently, current
testing techniques for DNNs depend on small local perturbations to existing
inputs, based on the metamorphic testing principle. We propose new ways to
search not over the entire image space, but rather over a plausible input space
that resembles the true training distribution. This space is constructed using
Variational Autoencoders (VAEs), and navigated through their latent vector
space. We show that this space helps efficiently produce test inputs that can
reveal information about the robustness of DNNs when dealing with realistic
tests, opening the field to meaningful exploration through the space of highly
structured images
Piping classification to metamorphic testing: an empirical study towards better effectiveness for the identification of failures in mesh simplification programs
Mesh simplification is a mainstream technique to render graphics responsively in modern graphical software. However, the graphical nature of the output poses a test oracle problem in testing. Previous work uses pattern classification to identify failures. Although such an approach may be promising, it may conservatively mark the test result of a failure-causing test case as passed. This paper proposes a methodology that pipes the test cases marked as passed by the pattern classification component to a metamorphic testing component to look for missed failures. The empirical study uses three simple and general metamorphic relations as subjects, and the experimental results show a 10 percent improvement of effectiveness in the identification of failures. © 2007 IEEE.Link_to_subscribed_fulltextThis research is supported in part by a grant of the Research Grants Council of Hong Kong (project no. 714504), a grant of City
University of Hong Kong (project no. 200079), and a grant of The University of Hong Kong
The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study
Context: Machine learning (ML) may enable effective automated test
generation.
Objective: We characterize emerging research, examining testing practices,
researcher goals, ML techniques applied, evaluation, and challenges.
Methods: We perform a systematic mapping on a sample of 102 publications.
Results: ML generates input for system, GUI, unit, performance, and
combinatorial testing or improves the performance of existing generation
methods. ML is also used to generate test verdicts, property-based, and
expected output oracles. Supervised learning - often based on neural networks -
and reinforcement learning - often based on Q-learning - are common, and some
publications also employ unsupervised or semi-supervised learning.
(Semi-/Un-)Supervised approaches are evaluated using both traditional testing
metrics and ML-related metrics (e.g., accuracy), while reinforcement learning
is often evaluated using testing metrics tied to the reward function.
Conclusion: Work-to-date shows great promise, but there are open challenges
regarding training data, retraining, scalability, evaluation complexity, ML
algorithms employed - and how they are applied - benchmarks, and replicability.
Our findings can serve as a roadmap and inspiration for researchers in this
field.Comment: Under submission to Software Testing, Verification, and Reliability
journal. (arXiv admin note: text overlap with arXiv:2107.00906 - This is an
earlier study that this study extends
Using Machine Learning to Generate Test Oracles: A Systematic Literature Review
Machine learning may enable the automated generation of test oracles. We have
characterized emerging research in this area through a systematic literature
review examining oracle types, researcher goals, the ML techniques applied, how
the generation process was assessed, and the open research challenges in this
emerging field.
Based on a sample of 22 relevant studies, we observed that ML algorithms
generated test verdict, metamorphic relation, and - most commonly - expected
output oracles. Almost all studies employ a supervised or semi-supervised
approach, trained on labeled system executions or code metadata - including
neural networks, support vector machines, adaptive boosting, and decision
trees. Oracles are evaluated using the mutation score, correct classifications,
accuracy, and ROC. Work-to-date show great promise, but there are significant
open challenges regarding the requirements imposed on training data, the
complexity of modeled functions, the ML algorithms employed - and how they are
applied - the benchmarks used by researchers, and replicability of the studies.
We hope that our findings will serve as a roadmap and inspiration for
researchers in this field.Comment: Pre-print. Article accepted to 1st International Workshop on Test
Oracles at ESEC/FSE 202
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