Anytime diagnosis for reconfiguration

Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FLEXDIAG which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FLEXDIAG helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy

Configuring Release Plans

Release planning takes place (1) on the strategic level where the overall goal is to prioritize (high-level) requirements and (2) on the operational level where the major focus is to define more detailed implementation plans, i.e., the assignment of requirements to specific releases and often the assignment of stakeholders to requirements. In this paper, we show how release planning can be represented as a configuration task and how re-configuration tasks can be supported. Thus we advance the state-of-the-art in software release planning by introducing technologies that support the handling of inconsistencies in already existing plans.Peer reviewe

A Parallelized Variant of Junker’s QUICKXPLAIN Algorithm

Conflict detection is used in many scenarios ranging from interactive decision making to the diagnosis of potentially faulty hardware components or models. In these scenarios, the efficient identification of conflicts is crucial. Junker’s QUICKXPLAIN is a divide-and-conquer based algorithm for the determination of preferred minimal conflicts. Motivated by the increasing size and complexity of knowledge bases, we propose a parallelization of the original algorithm that helps to significantly improve runtime performance especially in complex knowledge bases. In this paper, we introduce a parallelized version of QUICKXPLAIN that is based on the idea of predicting and executing parallel consistency checks needed by QUICKXPLAIN.Ministerio de Economía y Competitividad RTI2018-101204-B-C22Ministerio de Economía, Industria y Competitividad TIN2017-90644-RED

DirectDebug: A software package for the automated testing and debugging of feature models

Complex and large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we propose the DirectDebug algorithm that supports the automated testing and debugging of variability models. Our approach assists software engineers in identifying an adaptation hint (diagnosis) that makes all test cases consistent with the knowledge base. We also develop the software package so-called d2bug_eval to evaluate the DirectDebug’s performance. The software package can be re-produced thoroughly to evaluate consistency-based algorithms.Austrian Research Promotion Agency ParXCel project 880657Ministerio de Economía y Competitividad RTI2018-101204-B-C2