Contributions in Merlin

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Requirements models for large systems typically cannot be developed in a single step, but evolve in a sequence of iterations. We have developed such an iterative modeling process which is based on the interactive simulation of yet incomplete and semi-formal models. Missing parts are completed interactively by the user simulating the model. We start by modeling type scenarios (i.e. use cases) and simulate these interactively before having specified any system behavior. Such simulation runs yield exemplary system behavior in form of message sequence charts (MSCs). The modeler can then generalize this recorded partial behavior into statecharts. The resulting model is simulated again, (i) for validating that the modeled behavior matches the previously recorded behavior, and (ii) for recording new yet unspecified behavior in a next iteration step. Thus, recording MSCs by playing-through the scenarios and transforming MSCs to statecharts stimulate and drive each other. In this paper we focus on two elements of our approach: firstly, we describe the syntax and semantics of our scenario language. Secondly, we give an example how our modeling process works.

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An algorithm for estimating Lyapunov exponents of an unknown dynamical system is designed. The algorithm estimates not only the largest but all Lyapunov exponents of the unknown system. The estimation is carried out by a multivariate feedforward network estimation technique. We focus our attention on deterministic as well as noisy system estimation. The performance of the algorithm is very satisfactory in the presence of noise as well as with limited number of observations.

We present Attempto Controlled English — a user- friendly first-order logic language with a rich English syntax — and its associated tools, and demonstrate how they meet the challenges of knowledge representation, reasoning, interoperability and user interfaces created by large software projects like the semantic web.

This paper develops a wavelet (spectral) approach to testing the presence of a unit root in a stochastic process. The wavelet approach is appealing, since it is based directly on the different behavior of the spectra of a unit root process and that of a short memory stationary process. By decomposing the variance (energy) of the underlying process into the variance (energy) of its low frequency components and that of its high frequency components via the discrete wavelet transformation (DWT), we design unit root tests which have substantial power against near unit root alternatives. Since DWT is an energy preserving transformation and able to disbalance energy across high and low frequency components of a series, it is possible to isolate the most persistent component of a series in a small number of scaling coefficients. Our tests utilize the wavelet coefficients of the coarsest scale. We generalize our unit root tests to residual based tests for cointegration and to the maximum overlap DWT (MODWT), demonstrate their size and power properties through Monte Carlo simulations, and apply them to financial time series.

We develop a dynamic framework to identify aggregate market fears ahead of a major market crash through the skewness premium of European options. Our methodology is based on measuring the distribution of a skewness premium through a q-Gaussian density and a maximum entropy principle. Our findings indicate that the October 19th, 1987 crash was predictable from the study of the skewness premium of deepest out-of-the-money options about two months prior to the crash.

The standard principal agent model considers monetary incentives only. It is assumed that money is more efficient than other forms of material, non-monetary compensation. Awards in the form of titles, orders, medals and honors (prizes)– though almost omnipresent – have so far escaped the attention of economists. They present extrinsic, non-monetary incentives that operate through the innate desire of human beings for recognition and status. In this article, we analyse the differences between monetary incentives and awards: in general, awards are cheap, lead to interpersonal relationships, are not directly related to performance and have a signalling value. In addition, they support intrinsic motivation, may increase social welfare and are exempt from taxation. Awards present an important additional instrument to be considered in principal agent theory. In many contexts they are superior to monetary compensation.

Similarity analysis of source code is helpful during development to provide, for instance, better support for code reuse. Consider a development environment that analyzes code while typing and that suggests similar code examples or existing implementations from a source code repository. Mining software repositories by means of similarity measures enables and enforces reusing existing code and reduces the developing effort needed by creating a shared knowledge base of code fragments. In information retrieval similarity measures are often used to find documents similar to a given query document. This paper extends this idea to source code repositories. It introduces our approach to detect similar Java classes in software projects using tree similarity algorithms. We show how our approach allows to find similar Java classes based on an evaluation of three tree-based similarity measures in the context of five user-defined test cases as well as a preliminary software evolution analysis of a medium-sized Java project. Initial results of our technique indicate that it (1) is indeed useful to identify similar Java classes, (2) successfully identifies the ex ante and expost versions of refactored classes, and (3) provides some interesting insights into within-version and between-version dependencies of classes within a Java project.