649 research outputs found
METTLE: a METamorphic testing approach to assessing and validating unsupervised machine LEarning systems
Unsupervised machine learning is the training of an artificial intelligence
system using information that is neither classified nor labeled, with a view to
modeling the underlying structure or distribution in a dataset. Since
unsupervised machine learning systems are widely used in many real-world
applications, assessing the appropriateness of these systems and validating
their implementations with respect to individual users' requirements and
specific application scenarioscontexts are indisputably two important
tasks. Such assessment and validation tasks, however, are fairly challenging
due to the absence of a priori knowledge of the data. In view of this
challenge, we develop a amorphic esting approach to
assessing and validating unsupervised machine arning systems,
abbreviated as METTLE. Our approach provides a new way to unveil the (possibly
latent) characteristics of various machine learning systems, by explicitly
considering the specific expectations and requirements of these systems from
individual users' perspectives. To support METTLE, we have further formulated
11 generic metamorphic relations (MRs), covering users' generally expected
characteristics that should be possessed by machine learning systems. To
demonstrate the viability and effectiveness of METTLE we have performed an
experiment involving six commonly used clustering systems. Our experiment has
shown that, guided by user-defined MR-based adequacy criteria, end users are
able to assess, validate, and select appropriate clustering systems in
accordance with their own specific needs. Our investigation has also yielded
insightful understanding and interpretation of the behavior of the machine
learning systems from an end-user software engineering's perspective, rather
than a designer's or implementor's perspective, who normally adopts a
theoretical approach
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
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
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
Metamorphic exploration of an unsupervised clustering program
Machine learning has been becoming increasingly popular and widely-used in various industry domains. The presence of the oracle problem, however, makes it difficult to ensure the quality of this kind of software. Furthermore, the popularity of machine learning and its application has attracted many users who are not experts in this field. In this paper, we report on using a recently introduced method called metamorphic exploration where we proposed a set of hypothesized metamorphic relations for an unsupervised clustering program, Weka, to enhance understanding of the system and its better use
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