One of the main ideas of Service-Oriented Computing (SOC) is the delivery of flexibly composable services provided on world-wide markets. For a successful service discovery, service requests have to be matched with the available service offers. However, in a situation in which no service that completely matches the request can be discovered, the customer may tolerate slight discrepancies between request and offer. Some existing fuzzy matching approaches are able to detect such service variants, but they do not allow to explicitly specify which parts of a request are not mandatory. In this paper, we improve an existing service matching approach based on Visual Contracts leveraging our preliminary work of design pattern detection. Thereby, we support explicit specifications of service variants and realize gradual matching results that can be ranked in order to discover the service offer that matches a customer's request best.

In reverse engineering, dynamic pattern detection is accomplished by collecting execution traces and comparing them to expected behavioral patterns. The traces are collected by manually executing the program under study and therefore represent only part of all relevant program behavior. This can lead to false conclusions about the detected patterns. In this paper, we propose to generate all relevant program traces by using symbolic execution. In order to reduce the created trace data, we allow to limit the trace collection to a user-selectable subset of the statically detected pattern candidates.

Design pattern detection is a reverse engineering methodology that helps software engineers to analyze and understand legacy software by recovering its design and thereby aiding in the preparation of re-engineering activities. We present Reclipse, a reverse engineering tool suite for static and dynamic design pattern detection in combination with a pattern candidate rating used to assess the detection results' reliability.

We present a Tool Suite which supports the (re-)construction of a behavioral model of a legacy component based on a learning approach by exploiting knowledge of known models of the existing component environment. This in turn enables to check whether the legacy component can be integrated correctly into its environment.