203,333 research outputs found
Posterior Contraction and Testing for Multivariate Isotonic Regression
We consider the nonparametric regression problem with multiple predictors and
an additive error, where the regression function is assumed to be
coordinatewise nondecreasing. We propose a Bayesian approach to make an
inference on the multivariate monotone regression function, obtain the
posterior contraction rate, and construct a universally consistent Bayesian
testing procedure for multivariate monotonicity. To facilitate posterior
analysis, we set aside the shape restrictions temporarily, and endow a prior on
blockwise constant regression functions with heights independently normally
distributed. The unknown variance of the error term is either estimated by the
marginal maximum likelihood estimate or is equipped with an inverse-gamma
prior. Then the unrestricted block heights are a posteriori also independently
normally distributed given the error variance, by conjugacy. To comply with the
shape restrictions, we project samples from the unrestricted posterior onto the
class of multivariate monotone functions, inducing the "projection-posterior
distribution", to be used for making an inference. Under an
-metric, we show that the projection-posterior based on
independent samples contracts around the true monotone regression function at
the optimal rate . Then we construct a Bayesian test for
multivariate monotonicity based on the posterior probability of a shrinking
neighborhood of the class of multivariate monotone functions. We show that the
test is universally consistent, that is, the level of the Bayesian test goes to
zero, and the power at any fixed alternative goes to one. Moreover, we show
that for a smooth alternative function, power goes to one as long as its
distance to the class of multivariate monotone functions is at least of the
order of the estimation error for a smooth function
Specifying Reusable Components
Reusable software components need expressive specifications. This paper
outlines a rigorous foundation to model-based contracts, a method to equip
classes with strong contracts that support accurate design, implementation, and
formal verification of reusable components. Model-based contracts
conservatively extend the classic Design by Contract with a notion of model,
which underpins the precise definitions of such concepts as abstract
equivalence and specification completeness. Experiments applying model-based
contracts to libraries of data structures suggest that the method enables
accurate specification of practical software
Testing M2T/T2M Transformations
Presentado en: 16th International Conference on Model Driven Engineering Languages and Systems (MODELS 2013). Del 29 de septiembre al 4 de octubre. Miami, EEUU.Testing model-to-model (M2M) transformations is becoming a prominent topic in the current Model-driven Engineering landscape. Current approaches for transformation testing, however, assume having explicit model representations for the input domain and for the output domain of the transformation. This excludes other important transformation kinds, such as model-to-text (M2T) and text-to-model (T2M) transformations, from being properly tested since adequate model representations are missing either for the input domain or for the output domain. The contribution of this paper to overcome this gap is extending Tracts, a M2M transformation testing approach, for M2T/T2M transformation testing. The main mechanism we employ for reusing Tracts is to represent text within a generic metamodel. By this, we transform the M2T/T2M transformation specification problems into equivalent M2M transformation specification problems. We demonstrate the applicability of the approach by two examples and present how the approach is implemented for the Eclipse Modeling Framework (EMF). Finally, we apply the approach to evaluate code generation capabilities of several existing UML tools.Universidad de MĂĄlaga. Campus de Excelencia Internacional AndalucĂa Tech. Proyecto TIN2011-2379
Automated verification of model transformations based on visual contracts
The final publication is available at Springer via http://dx.doi.org/10.1007/s10515-012-0102-yModel-Driven Engineering promotes the use of models to conduct the different phases of the software development. In this way, models are transformed between different languages and notations until code is generated for the final application. Hence, the construction of correct Model-to-Model (M2M) transformations becomes a crucial aspect in this approach.
Even though many languages and tools have been proposed to build and execute M2M transformations, there is scarce support to specify correctness requirements for such transformations in an implementation-independent way, i.e., irrespective of the actual transformation language used.
In this paper we fill this gap by proposing a declarative language for the specification of visual contracts, enabling the verification of transformations defined with any transformation language. The verification is performed by compiling the contracts into QVT to detect disconformities of transformation results with respect to the contracts. As a proof of concept, we also report on a graphical modeling environment for the specification of contracts, and on its use for the verification of transformations in several case studies.This work has been funded by the Austrian Science Fund (FWF) under grant P21374-N13,
the Spanish Ministry of Science under grants TIN2008-02081 and TIN2011-24139, and the
R&D programme of the Madrid Region under project S2009/TIC-1650
Metamodel-based model conformance and multiview consistency checking
Model-driven development, using languages such as UML and BON, often makes use of multiple diagrams (e.g., class and sequence diagrams) when modeling systems. These diagrams, presenting different views of a system of interest, may be inconsistent. A metamodel provides a unifying framework in which to ensure and check consistency, while at the same time providing the means to distinguish between valid and invalid models, that is, conformance. Two formal specifications of the metamodel for an object-oriented modeling language are presented, and it is shown how to use these specifications for model conformance and multiview consistency checking. Comparisons are made in terms of completeness and the level of automation each provide for checking multiview consistency and model conformance. The lessons learned from applying formal techniques to the problems of metamodeling, model conformance, and multiview consistency checking are summarized
Stateful Testing: Finding More Errors in Code and Contracts
Automated random testing has shown to be an effective approach to finding
faults but still faces a major unsolved issue: how to generate test inputs
diverse enough to find many faults and find them quickly. Stateful testing, the
automated testing technique introduced in this article, generates new test
cases that improve an existing test suite. The generated test cases are
designed to violate the dynamically inferred contracts (invariants)
characterizing the existing test suite. As a consequence, they are in a good
position to detect new errors, and also to improve the accuracy of the inferred
contracts by discovering those that are unsound. Experiments on 13 data
structure classes totalling over 28,000 lines of code demonstrate the
effectiveness of stateful testing in improving over the results of long
sessions of random testing: stateful testing found 68.4% new errors and
improved the accuracy of automatically inferred contracts to over 99%, with
just a 7% time overhead.Comment: 11 pages, 3 figure
Reasoning and Improving on Software Resilience against Unanticipated Exceptions
In software, there are the errors anticipated at specification and design
time, those encountered at development and testing time, and those that happen
in production mode yet never anticipated. In this paper, we aim at reasoning on
the ability of software to correctly handle unanticipated exceptions. We
propose an algorithm, called short-circuit testing, which injects exceptions
during test suite execution so as to simulate unanticipated errors. This
algorithm collects data that is used as input for verifying two formal
exception contracts that capture two resilience properties. Our evaluation on 9
test suites, with 78% line coverage in average, analyzes 241 executed catch
blocks, shows that 101 of them expose resilience properties and that 84 can be
transformed to be more resilient
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