University of Paderborn

About: University of Paderborn is a education organization based out in Paderborn, Nordrhein-Westfalen, Germany. It is known for research contribution in the topics: Control reconfiguration & Software. The organization has 6684 authors who have published 16929 publications receiving 323154 citations.

Making decisions in hierarchic and network systems

Abstract: This paper summarises a mathematical theory of the measurement of both tangible and intangible factors, the Analytic Hierarchy Process (AHP) and its generalisation to dependence and feedback, the Analytic Network Process (ANP) and illustrates their application to making complex multicriteria decisions.

FunState-an internal design representation for codesign

Abstract: In this paper, an internal design model called FunState (functions driven by state machines) is presented that enables the representation of different types of system components and scheduling mechanisms using a mixture of functional programming and state machines. It is shown how properties relevant for scheduling and verification of specification models such as Boolean dataflow, cyclostatic dataflow, synchronous dataflow, marked graphs, and communicating state machines as well as Petri nets can be represented in the FunState model of computation. Examples of methods suited for FunState are described, such as scheduling and verification. They are based on the representation of the model's state transitions in the form of a periodic graph. The feasibility of the novel approach is shown with an asynchronous transfer mode switch example.

Boomerang: Demand-Driven Flow- and Context-Sensitive Pointer Analysis for Java

Abstract: Many current program analyses require highly precise pointer information about small, tar- geted parts of a given program. This motivates the need for demand-driven pointer analyses that compute information only where required. Pointer analyses generally compute points-to sets of program variables or answer boolean alias queries. However, many client analyses require richer pointer information. For example, taint and typestate analyses often need to know the set of all aliases of a given variable under a certain calling context. With most current pointer analyses, clients must compute such information through repeated points-to or alias queries, increasing complexity and computation time for them. This paper presents Boomerang, a demand-driven, flow-, field-, and context-sensitive pointer analysis for Java programs. Boomerang computes rich results that include both the possible allocation sites of a given pointer (points-to information) and all pointers that can point to those allocation sites (alias information). For increased precision and scalability, clients can query Boomerang with respect to particular calling contexts of interest. Our experiments show that Boomerang is more precise than existing demand-driven pointer analyses. Additionally, using Boomerang, the taint analysis FlowDroid issues up to 29.4x fewer pointer queries compared to using other pointer analyses that return simpler pointer infor- mation. Furthermore, the search space of Boomerang can be significantly reduced by requesting calling contexts from the client analysis.