Lahcen Ouarbya

Conditioned slicing supports partition testing

This paper describes the use of conditioned slicing to assist partition testing, illustrating this with a case study. The paper shows how a conditioned slicing tool can be used to provide confidence in the uniformity hypothesis for correct programs, to aid fault detection in incorrect programs and to highlight special cases. Copyright

Formalizing executable dynamic and forward slicing

This paper uses a projection theory of slicing to formalize the definition of executable dynamic and forward program slicing. Previous definitions, when given, have been operational, and previous descriptions have been algorithmic. The projection framework is used to provide a declarative formulation in terms of the different equivalences preserved by the different forms of slicing. The analysis of dynamic slicing reveals that the slicing criterion introduced by Korel and Laski contains three inter-woven criteria. It is shown how these three conceptually distinct criteria can be disentangled to reveal two new criteria. The analysis of dynamic slicing also reveals that the subsumes relationship between static and dynamic slicing is more intricate that previous authors have claimed. Finally, the paper uses the projection theory to investigate theoretical properties of forward slicing. This is achieved by first re-formulating forward slicing to provide an executable forward slice. This definition allows for formal investigation of the relationship between forward and backward slicing

Syntax-Directed Amorphous Slicing

An amorphous slice of a program is constructed with respect to a set of variables. The amorphous slice is an executable program which preserves the behaviour of the original on the variables of interest. Unlike syntax-preserving slices, amorphous slices need not preserve a projection of the syntax of a program. This makes the task of amorphous slice construction harder, but it also often makes the result thinner and thereby preferable in applications where syntax preservation is unimportant. This paper describes an approach to the construction of amorphous slices which is based on the Abstract Syntax Tree of the program to be sliced, and does not require the construction of control flow graphs nor of program dependence graphs. The approach has some strengths and weaknesses which the paper discusses. The amorphous slicer, is part of the GUSTT slicing system, which includes syntax preserving static and conditioned slicers, a side effect removal transformation phase, slicing criterion guidance and for which much of the correctness proofs for transformation steps are mechanically verified. The system handles a subset of WSL, into which more general WSL constructs can be transformed. The paper focuses upon the way in which the GUSTT System uses dependence reduction transformation tactics. Such dependence reduction is at the heart of all approaches to amorphous slicing. The algorithms used are described and their performance is assessed with a simple empirical study of best and worst case execution times for an implementation built on top of the FermaT transformation system for maintenance and re-engineering.

A denotational interprocedural program slicer

This paper extends a previously developed intraprocedural denotational program slicer to handle procedures. Using the denotational approach, slices can be defined in terms of the abstract syntax of the object language without the need of a control flow graph or similar intermediate structure. The algorithm presented here is capable of correctly handling the interplay between function and procedure calls, side-effects, and short-circuit expression evaluation. The ability to deal with these features is required in reverse engineering of legacy systems, where code often contains side-effects.

ConSUS: a scalable approach to conditioned slicing

Conditioned slicing can be applied to reverse engineering problems which involve the extraction of executable fragments of code in the context of some criteria of interest. This paper introduces ConSUS, a conditioner for the Wide Spectrum Language, WSL. The symbolic executor of ConSUS prunes the symbolic execution paths, and its predicate reasoning system uses the FermaT simplify transformation in place of a more conventional theorem prover We show that this combination of pruning and simplification-as-reasoner leads to a more scalable approach to conditioning.

A formal relationship between program slicing and partial evaluation

A formal relationship between program slicing and partial evaluation is established. It is proved that for terminating programs, a residual program produced by partial evaluation is semantically equivalent to a conditioned slice.

An interprocedural amorphous slicer for WSL

This paper presents a simple interprocedural algorithm for amorphous slicing and illustrates the way in which interprocedural amorphous slicing improves upon interprocedural syntax-preserving slicing. The paper also presents results from an empirical study of tin implementation of this algorithm for Wards Wide Spectrum Language, WSL. The implementation uses the FermaT transformation workbench. It combines FermaT transformations with the results produced by a syntax-preserving slicer for WSL. Finally, it is shown that the combination of amorphous slicing and conditioned slicing ran be particularly attractive, by combining results from the amorphous slicer with results from a prototype conditioned slicer for WSL.

Sentiment and stock market volatility predictive modelling — A hybrid approach

The frequent ups and downs are characteristic to the stock market. The conventional standard models that assume that investors act rationally have not been able to capture the irregularities in the stock market patterns for years. As a result, behavioural finance is embraced to attempt to correct these model shortcomings by adding some factors to capture sentimental contagion which may be at play in determining the stock market. This paper assesses the predictive influence of sentiment on the stock market returns by using a non-parametric nonlinear approach that corrects specific limitations encountered in previous related work. In addition, the paper proposes a new approach to developing stock market volatility predictive models by incorporating a hybrid GARCH and artificial neural network framework, and proves the advantage of this framework over a GARCH only based framework. Our results reveal also that past volatility and positive sentiment appear to have strong predictive power over future volatility.

Modeling Dst with Recurrent EM Neural Networks

Recurrent Neural Networks have been used extensively for space weather forecasts of geomagnetospheric disturbances. One of the major drawbacks for reliable forecasts have been the use of training algorithms that are unable to account for model uncertainty and noise in data. We propose a probabilistic training algorithm based on the Expectation Maximization framework for parameterization of the model which makes use of a forward filtering and backward smoothing Expectation step, and a Maximization step in which the model uncertainty and measurement noise estimates are computed. Through numerical experimentation it is shown that the proposed model allows for reliable forecasts and also outperforms other neural time series models trained with the Extended Kalman Filter, and gradient descent learning.

Evolving recurrent neural models of geomagnetic storms

Genetic algorithms for training recurrent neural networks (RNNs) have not yet been considered for modeling the dynamics of magnetospheric plasma. We provide a discussion of the previous state of the art in modeling Dst. Then, a recurrent neural network trained by a genetic algorithm is proposed for geomagnetic storm forecasting. The exogenous inputs to the RNN consist of three parameters, bz, n, and v, which represent the southward and azimuthal components of the interplanetary magnetic field (IMF), the density of electromagnetic particles, and the velocity of the particles respectively. The proposed model is compared to a model used in operational forecasts on a series of geomagnetic storms that so far have been difficult to forecast. It is shown that the proposed evolutionary method of training the RNN outperforms the operational model which was trained by gradient descent.