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Using formal methods to support testing
Formal methods and testing are two important approaches that assist in the development of high quality software. While traditionally these approaches have been seen as rivals, in recent
years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing
TypEx : a type based approach to XML stream querying
We consider the topic of query evaluation over semistructured information streams, and XML data streams in particular. Streaming evaluation methods are necessarily eventdriven, which is in tension with high-level query models; in general, the more expressive the query language, the harder it is to translate queries into an event-based implementation with finite resource bounds
CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis
We propose and evaluate a new technique for learning hybrid automata
automatically by observing the runtime behavior of a dynamical system. Working
from a sequence of continuous state values and predicates about the
environment, CHARDA recovers the distinct dynamic modes, learns a model for
each mode from a given set of templates, and postulates causal guard conditions
which trigger transitions between modes. Our main contribution is the use of
information-theoretic measures (1)~as a cost function for data segmentation and
model selection to penalize over-fitting and (2)~to determine the likely causes
of each transition. CHARDA is easily extended with different classes of model
templates, fitting methods, or predicates. In our experiments on a complex
videogame character, CHARDA successfully discovers a reasonable
over-approximation of the character's true behaviors. Our results also compare
favorably against recent work in automatically learning probabilistic timed
automata in an aircraft domain: CHARDA exactly learns the modes of these
simpler automata.Comment: 7 pages, 2 figures. Accepted for IJCAI 201
A Model-Derivation Framework for Software Analysis
Model-based verification allows to express behavioral correctness conditions
like the validity of execution states, boundaries of variables or timing at a
high level of abstraction and affirm that they are satisfied by a software
system. However, this requires expressive models which are difficult and
cumbersome to create and maintain by hand. This paper presents a framework that
automatically derives behavioral models from real-sized Java programs. Our
framework builds on the EMF/ECore technology and provides a tool that creates
an initial model from Java bytecode, as well as a series of transformations
that simplify the model and eventually output a timed-automata model that can
be processed by a model checker such as UPPAAL. The framework has the following
properties: (1) consistency of models with software, (2) extensibility of the
model derivation process, (3) scalability and (4) expressiveness of models. We
report several case studies to validate how our framework satisfies these
properties.Comment: In Proceedings MARS 2017, arXiv:1703.0581
A Model-Derivation Framework for Software Analysis
Model-based verification allows to express behavioral correctness conditions
like the validity of execution states, boundaries of variables or timing at a
high level of abstraction and affirm that they are satisfied by a software
system. However, this requires expressive models which are difficult and
cumbersome to create and maintain by hand. This paper presents a framework that
automatically derives behavioral models from real-sized Java programs. Our
framework builds on the EMF/ECore technology and provides a tool that creates
an initial model from Java bytecode, as well as a series of transformations
that simplify the model and eventually output a timed-automata model that can
be processed by a model checker such as UPPAAL. The framework has the following
properties: (1) consistency of models with software, (2) extensibility of the
model derivation process, (3) scalability and (4) expressiveness of models. We
report several case studies to validate how our framework satisfies these
properties.Comment: In Proceedings MARS 2017, arXiv:1703.0581
Automated Game Design Learning
While general game playing is an active field of research, the learning of
game design has tended to be either a secondary goal of such research or it has
been solely the domain of humans. We propose a field of research, Automated
Game Design Learning (AGDL), with the direct purpose of learning game designs
directly through interaction with games in the mode that most people experience
games: via play. We detail existing work that touches the edges of this field,
describe current successful projects in AGDL and the theoretical foundations
that enable them, point to promising applications enabled by AGDL, and discuss
next steps for this exciting area of study. The key moves of AGDL are to use
game programs as the ultimate source of truth about their own design, and to
make these design properties available to other systems and avenues of inquiry.Comment: 8 pages, 2 figures. Accepted for CIG 201
Adaptable transition systems
We present an essential model of adaptable transition systems inspired by white-box approaches to adaptation and based on foundational models of component based systems. The key feature of adaptable transition systems are control propositions, imposing a clear separation between ordinary, functional behaviours and adaptive ones. We instantiate our approach on interface automata yielding adaptable interface automata, but it may be instantiated on other foundational models of component-based systems as well. We discuss how control propositions can be exploited in the specification and analysis of adaptive systems, focusing on various notions proposed in the literature, like adaptability, control loops, and control synthesis
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