60,744 research outputs found
Static Analysis of Functional Programs
In this paper, the static analysis of programs in the functional programming language Miranda* is described based on two graph models. A new control-flow graph model of Miranda definitions is presented, and a model with four classes of callgraphs. Standard software metrics are applicable to these models. A Miranda front end for Prometrix, ¿, a tool for the automated analysis of flowgraphs and callgraphs, has been developed. This front end produces the flowgraph and callgraph representations of Miranda programs. Some features of the metric analyser are illustrated with an example program. The tool provides a promising access to standard metrics on functional programs
Expressing advanced user preferences in component installation
State of the art component-based software collections - such as FOSS
distributions - are made of up to dozens of thousands components, with complex
inter-dependencies and conflicts. Given a particular installation of such a
system, each request to alter the set of installed components has potentially
(too) many satisfying answers. We present an architecture that allows to
express advanced user preferences about package selection in FOSS
distributions. The architecture is composed by a distribution-independent
format for describing available and installed packages called CUDF (Common
Upgradeability Description Format), and a foundational language called MooML to
specify optimization criteria. We present the syntax and semantics of CUDF and
MooML, and discuss the partial evaluation mechanism of MooML which allows to
gain efficiency in package dependency solvers
A Comparison of Big Data Frameworks on a Layered Dataflow Model
In the world of Big Data analytics, there is a series of tools aiming at
simplifying programming applications to be executed on clusters. Although each
tool claims to provide better programming, data and execution models, for which
only informal (and often confusing) semantics is generally provided, all share
a common underlying model, namely, the Dataflow model. The Dataflow model we
propose shows how various tools share the same expressiveness at different
levels of abstraction. The contribution of this work is twofold: first, we show
that the proposed model is (at least) as general as existing batch and
streaming frameworks (e.g., Spark, Flink, Storm), thus making it easier to
understand high-level data-processing applications written in such frameworks.
Second, we provide a layered model that can represent tools and applications
following the Dataflow paradigm and we show how the analyzed tools fit in each
level.Comment: 19 pages, 6 figures, 2 tables, In Proc. of the 9th Intl Symposium on
High-Level Parallel Programming and Applications (HLPP), July 4-5 2016,
Muenster, German
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