Reverse Engineering Heterogeneous Applications

Nowadays a large majority of software systems are built using various technologies that in turn rely on different languages (e.g. Java, XML, SQL etc.). We call such systems heterogeneous applications (HAs). By contrast, we call software systems that are written in one language homogeneous applications. In HAs the information regarding the structure and the behaviour of the system is spread across various components and languages and the interactions between different application elements could be hidden. In this context applying existing reverse engineering and quality assurance techniques developed for homogeneous applications is not enough. These techniques have been created to measure quality or provide information about one aspect of the system and they cannot grasp the complexity of HAs. In this dissertation we present our approach to support the analysis and evolution of HAs based on: (1) a unified first-class description of HAs and, (2) a meta-model that reifies the concept of horizontal and vertical dependencies between application elements at different levels of abstraction. We implemented our approach in two tools, MooseEE and Carrack. The first is an extension of the Moose platform for software and data analysis and contains our unified meta-model for HAs. The latter is an engine to infer derived dependencies that can support the analysis of associations among the heterogeneous elements composing HA. We validate our approach and tools by case studies on industrial and open-source JEAs which demonstrate how we can handle the complexity of such applications and how we can solve problems deriving from their heterogeneous nature

Link Prediction in Complex Networks: A Survey

Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.Comment: 44 pages, 5 figure

Entity Relationship Approach to Knowledge Base Systems.

A unified framework for knowledge base systems is proposed based on Entity-Relationship (ER) approach. Following the analysis and the specification of the real-world using Entity-Relationship approach, the knowledge base is implemented as a first-order logic system, a production system, or a frame-based system by mapping the appropriate symbolic data structures. An approach for analyzing and specifying real-world perceptions must provide appropriate semantic primitives. Therefore, a justification is provided for the semantic primitives proposed in Entity-Relationship approach by considering the fundamental issues in perception. A notation that allows Entity-Relationship approach to be used as a holistic representation is presented. Translation rules are provided for the conversion of ER-diagrams into symbolic data structures of first-order logic systems, production systems, and frame-based systems. The feasibility of using Entity-Relationship approach to support a natural language front-end of a knowledge base system is examined by analyzing the representation of surface and deep structures of a sentence in Entity-Relationship approach

Barry Smith an sich

Festschrift in Honor of Barry Smith on the occasion of his 65th Birthday. Published as issue 4:4 of the journal Cosmos + Taxis: Studies in Emergent Order and Organization. Includes contributions by Wolfgang Grassl, Nicola Guarino, John T. Kearns, Rudolf Lüthe, Luc Schneider, Peter Simons, Wojciech Żełaniec, and Jan Woleński

The notion of specialization in the i*framework

This thesis provides a formal proposal for the specialization relationship in the i* framework that allows its use in a well-defined manner. I root my proposal over existing works in different areas that are interested in representing knowledge: knowledge representation from Artificial Intelligence and conceptual modeling and object-oriented programming languages from Software Development. Also, I use the results of a survey conducted in the i* community that provides some insights about what i* modelers expect from specialization. As a consequence of this twofold analysis, I identify three specialization operations: extension, refinement and redefinition. For each of them, I: - motivate its need and provide some rationale; - distinguish the several cases that can occur in each operation; - define the elements involved in each of these cases and the correctness conditions that must be fulfilled; - demonstrate by induction the fulfilment of the conditions identified for preserving satisfaction; - provide some illustrative examples in the context of an exemplar about travel agencies and travelers. The specialization relationship is offered by the i* framework through the is-a construct defined over actors (a subactor is-a superactor) since it was first released. Although the overall meaning of this construct is highly intuitive, its effects at the level of intentional elements and dependencies are not always clear, hampering seriously its appropriate use. In order to be able to reason about correctness and satisfaction, I define previously the conditions that must be preserved when a specialization takes place. In addition, I provide a methodology with well-defined steps that contextualize the formal aspects of this thesis in a development process. As a conclusion, this thesis is making possible the use of the specialization relationship in i* in a precise, non-ambiguous manner

Toward Online Linguistic Surveillance of Threatening Messages

Threats are communicative acts, but it is not always obvious what they communicate or when they communicate imminent credible and serious risk. This paper proposes a research- and theory-based set of over 20 potential linguistic risk indicators that may discriminate credible from non-credible threats within online threat message corpora. Two prongs are proposed: (1) Using expert and layperson ratings to validate subjective scales in relation to annotated known risk messages, and (2) Using the resulting annotated corpora for automated machine learning with computational linguistic analyses to classify non-threats, false threats, and credible threats. Rating scales are proposed, existing threat corpora are identified, and some prospective computational linguistic procedures are identified. Implications for ongoing threat surveillance and its applications are explored