Examinando por Materia "ESTRUCTURA DE DATOS"

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  • Ponencia en Congreso
    Aggregation languages for moving object and places of interest
    (2008) Gómez, Leticia Irene; Kuijpers, Bart; Vaisman, Alejandro Ariel
    "We address aggregate queries over GIS data and moving object data, where non-spatial information is stored in a data warehouse. We propose a formal data model and query language to express complex aggregate queries. Next, we study the compression of trajectory data, produced by moving objects, using the notions of stops and moves. We show that stops and moves are expressible in our query language and we consider a fragment of this language, consisting of regular expressions to talk about temporally ordered sequences of stops and moves. This fragment can be used not only for querying, but also for expressing data mining and pattern matching tasks over trajectory data."
  • Ponencia en Congreso
    Analysis of invariants for efficient bounded verification
    (2010-07) Galeotti, Juan Pablo; Rosner, Nicolás; López Pombo, Carlos G.; Frías, Marcelo
    "SAT-based bounded veri cation of annotated code consists of translating the code together with the annotations to a propositional formula, and analyzing the formula for speci cation violations using a SAT-solver. If a violation is found, an execution trace exposing the error is exhibited. Code involving linked data structures with intricate invariants is particularly hard to analyze using these techniques. In this article we present TACO, a prototype tool which implements a novel, general and fully automated technique for the SAT-based analysis of JML-annotated Java sequential programs dealing with complex linked data structures. We instrument code analysis with a symmetry-breaking predicate that allows for the parallel, automated computation of tight bounds for Java elds. Experiments show that the translations to propositional formulas require signi cantly less propositional variables, leading in the experiments we have carried out to an improvement on the e ciency of the analysis of orders of magnitude, compared to the non instrumented SAT-based analysis. We show that, in somecases, our tool can uncover bugs that cannot be detected by state-of-the-art tools based on SAT-solving, model checking or SMT-solving."
  • Artículo de Publicación Periódica
    An evolutionary approach to translating operational specifications into declarative specifications
    (2019-07) Molina, Facundo; Cornejo, César; Degiovanni, Renzo; Regis, Germán; Castro, Pablo; Aguirre, Nazareno; Frías, Marcelo
    "Various tools for program analysis, including run-time assertion checkers and static analyzers such as verification and test generation tools, require formal specifications of the programs being analyzed. Moreover, many of these tools and techniques require such specifications to be written in a particular style, or follow certain patterns, in order to obtain an acceptable performance from the corresponding analyses. Thus, having a formal specification sometimes is not enough for using a particular technique, since such specification may not be provided in the right formalism. In this paper, we deal with this problem in the increasingly common case of having an operational specification, while for analysis reasons requiring a declarative specification. We propose an evolutionary approach to translate an operational specification written in a sequential programming language, into a declarative specification, in relational logic. We perform experiments on a benchmark of data structure implementations, for which operational invariants are available, and show that our evolutionary computation based approach to translating specifications achieves very good precision in this context, and produces declarative specifications that are more amenable to analyses that demand specifications in this style. This is assessed in two contexts: bounded verification of data structure invariant preservation, and instance enumeration using symbolic execution aided by tight bounds."
  • Ponencia en Congreso
    From operational to declarative specifications using a genetic algorithm
    (2018-05) Molina, Facundo; Degiovanni, Renzo; Regis, Germán; Castro, Pablo; Aguirre, Nazareno; Frías, Marcelo
    "In specification-based test generation, sometimes having a formal specification is not sufficient, since the specification may be in a different formalism from that required by the generation approach being used. In this paper, we deal with this problem specifically in the context in which, while having a formal specification in the form of an operational invariant written in a sequential programming language, one needs, for test generation, a declarative invariant in a logical formalism. We propose a genetic algorithm that given a catalog of common properties of invariants, such as acyclicity, sortedness and balance, attempts to evolve a conjunction of these that most accurately approximates an original operational specification. We present some details of the algorithm, and an experimental evaluation based on a benchmark of data structures, for which we evolve declarative logical invariants from operational ones."
  • Artículo de Publicación Periódica
    Time-series-based queries on stable transportation networks equipped with sensors
    (2021) Bollen, Erik; Hendrix, Rik; Kuijpers, Bart; Vaisman, Alejandro Ariel
    "In this paper, we propose a formalism to query transportation networks that are equipped with sensors that produce time-series data. The core of the proposed query mechanism is a logic based language that is capable to return time, value, and time-series outputs, as well as Boolean queries. We can also use the language for node selection and path selection. Furthermore, we propose an implementation of this language in a graph database system and evaluate its working on a fragment of the Flemish river system that is equipped with sensors that measure the water height at regular moments in time."
  • Ponencia en Congreso
    Training binary classifiers as data structure invariants
    (2019-05) Molina, Facundo; Degiovanni, Renzo; Ponzio, Pablo; Regis, Germán; Aguirre, Nazareno; Frías, Marcelo
    "We present a technique to distinguish valid from invalid data structure objects. The technique is based on building an artificial neural network, more precisely a binary classifier, and training it to identify valid and invalid instances of a data structure. The obtained classifier can then be used in place of the data structure’s invariant, in order to attempt to identify (in)correct behaviors in programs manipulating the structure. In order to produce the valid objects to train the network, an assumed-correct set of object building routines is randomly executed. Invalid instances are produced by generating values for object fields that “break” the collected valid values, i.e., that assign values to object fields that have not been observed as feasible in the assumed-correct executions that led to the collected valid instances. We experimentally assess this approach, over a benchmark of data structures.We show that this learning technique produces classifiers that achieve significantly better accuracy in classifying valid/invalid objects compared to a technique for dynamic invariant detection, and leads to improved bug finding."