6,757 research outputs found
Test Case Generation for Object-Oriented Imperative Languages in CLP
Testing is a vital part of the software development process. Test Case
Generation (TCG) is the process of automatically generating a collection of
test cases which are applied to a system under test. White-box TCG is usually
performed by means of symbolic execution, i.e., instead of executing the
program on normal values (e.g., numbers), the program is executed on symbolic
values representing arbitrary values. When dealing with an object-oriented (OO)
imperative language, symbolic execution becomes challenging as, among other
things, it must be able to backtrack, complex heap-allocated data structures
should be created during the TCG process and features like inheritance, virtual
invocations and exceptions have to be taken into account. Due to its inherent
symbolic execution mechanism, we pursue in this paper that Constraint Logic
Programming (CLP) has a promising unexploited application field in TCG. We will
support our claim by developing a fully CLP-based framework to TCG of an OO
imperative language, and by assessing it on a corresponding implementation on a
set of challenging Java programs. A unique characteristic of our approach is
that it handles all language features using only CLP and without the need of
developing specific constraint operators (e.g., to model the heap)
Test Data Generation of Bytecode by CLP Partial Evaluation
We employ existing partial evaluation (PE) techniques developed for Constraint Logic Programming (CLP) in order to automatically generate test-case generators for glass-box testing of bytecode. Our approach consists of two independent CLP PE phases. (1) First, the bytecode is transformed into an equivalent (decompiled) CLP program. This is already a well studied transformation which can be done either by using an ad-hoc decompiler or by specialising a bytecode interpreter by means of existing PE techniques. (2) A second PE is performed in order to supervise the generation of test-cases by execution of the CLP decompiled program. Interestingly, we employ control strategies previously defined in the context of CLP PE in order to capture coverage criteria for glass-box testing of bytecode. A unique feature of our approach is that, this second PE phase allows generating not only test-cases but also test-case generators. To the best of our knowledge, this is the first time that (CLP) PE techniques are applied for test-case generation as well as to generate test-case generators
Verification of Imperative Programs by Constraint Logic Program Transformation
We present a method for verifying partial correctness properties of
imperative programs that manipulate integers and arrays by using techniques
based on the transformation of constraint logic programs (CLP). We use CLP as a
metalanguage for representing imperative programs, their executions, and their
properties. First, we encode the correctness of an imperative program, say
prog, as the negation of a predicate 'incorrect' defined by a CLP program T. By
construction, 'incorrect' holds in the least model of T if and only if the
execution of prog from an initial configuration eventually halts in an error
configuration. Then, we apply to program T a sequence of transformations that
preserve its least model semantics. These transformations are based on
well-known transformation rules, such as unfolding and folding, guided by
suitable transformation strategies, such as specialization and generalization.
The objective of the transformations is to derive a new CLP program TransfT
where the predicate 'incorrect' is defined either by (i) the fact 'incorrect.'
(and in this case prog is not correct), or by (ii) the empty set of clauses
(and in this case prog is correct). In the case where we derive a CLP program
such that neither (i) nor (ii) holds, we iterate the transformation. Since the
problem is undecidable, this process may not terminate. We show through
examples that our method can be applied in a rather systematic way, and is
amenable to automation by transferring to the field of program verification
many techniques developed in the field of program transformation.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455
Constraint Logic Programming for Natural Language Processing
This paper proposes an evaluation of the adequacy of the constraint logic
programming paradigm for natural language processing. Theoretical aspects of
this question have been discussed in several works. We adopt here a pragmatic
point of view and our argumentation relies on concrete solutions. Using actual
contraints (in the CLP sense) is neither easy nor direct. However, CLP can
improve parsing techniques in several aspects such as concision, control,
efficiency or direct representation of linguistic formalism. This discussion is
illustrated by several examples and the presentation of an HPSG parser.Comment: 15 pages, uuencoded and compressed postscript to appear in
Proceedings of the 5th Int. Workshop on Natural Language Understanding and
Logic Programming. Lisbon, Portugal. 199
Probabilistic Constraint Logic Programming
This paper addresses two central problems for probabilistic processing
models: parameter estimation from incomplete data and efficient retrieval of
most probable analyses. These questions have been answered satisfactorily only
for probabilistic regular and context-free models. We address these problems
for a more expressive probabilistic constraint logic programming model. We
present a log-linear probability model for probabilistic constraint logic
programming. On top of this model we define an algorithm to estimate the
parameters and to select the properties of log-linear models from incomplete
data. This algorithm is an extension of the improved iterative scaling
algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm
applies to log-linear models in general and is accompanied with suitable
approximation methods when applied to large data spaces. Furthermore, we
present an approach for searching for most probable analyses of the
probabilistic constraint logic programming model. This method can be applied to
the ambiguity resolution problem in natural language processing applications.Comment: 35 pages, uses sfbart.cl
An assertion language for constraint logic programs
In an advanced program development environment, such as that discussed in the introduction of this book, several tools may coexist which handle both the program and information on the program in different ways. Also, these tools may interact among themselves and with the user. Thus, the different tools and the user need some way to communicate. It is our design principie that such communication be performed in terms of assertions. Assertions are syntactic objects which allow expressing properties of programs. Several assertion languages have been used in the past in different contexts, mainly related to program debugging. In this chapter we propose a general language of assertions which is used in different tools for validation and debugging of constraint logic programs in the context of the DiSCiPl project. The assertion language proposed is parametric w.r.t. the particular constraint domain and properties of interest being used in each different tool. The language proposed is quite general in that it poses few restrictions on the kind of properties which may be expressed. We believe the assertion language we propose is of practical relevance and appropriate for the different uses required in the tools considered
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