http://dl.gi.de/handle/20.500.12116/19972 2026-07-21T13:26:30Z http://dl.gi.de/handle/20.500.12116/763 Performance-Influence Models Siegmund, Norbert; Grebhahn, Alexander; Apel, Sven; Kästner, Christian Knoop, Jens; Zdun, Uwe 2016-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/764 Scaling size and parameter spaces in variability-aware software performance models Kowal, Matthias; Tschaikowski, Max; Tribastone, Mirco; Schaefer, Ina Knoop, Jens; Zdun, Uwe Model-based software performance engineering often requires the analysis of many instances of a model to find optimizations or to do capacity planning. These performance predictions get increasingly more difficult with larger models due to state space explosion as well as large parameter spaces since each configuration has its own performance model and must be analyzed in isolation (product-based (PB) analysis). We propose an efficient family-based (FB) analysis using UML activity diagrams with performance annotations. The FB analysis enables us to analyze all configurations at once using symbolic computation. Previous work has already shown that a FB analysis is significant faster than its PB counterpart. This work is an extension of our previous research lifting several limitations. 2016-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/762 Automated workload characterization for I/O performance analysis in virtualized environments Busch, Axel; Noorshams, Qais; Kounev, Samuel; Koziolek, Anne; Reussner, Ralf; Amrehn, Erich Knoop, Jens; Zdun, Uwe 2016-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/761 Intelligent code completion with Bayesian networks Proksch, Sebastian; Lerch, Johannes; Mezini, Mira Knoop, Jens; Zdun, Uwe Code completion is an integral part of modern Integrated Development Environments (IDEs). Intelligent code completion systems can reduce long lists of type-correct proposals to relevant items. In this work, we replace an existing code completion engine named Best-Matching Neighbor (BMN) by an approach using Bayesian Networks named Pattern-based Bayesian Network (PBN).We use additional context information for more precise recommendations and apply clustering techniques to improve model sizes and to increase speed. We compare the new approach with the existing algorithm and, in addition to prediction quality, we also evaluate model size and inference speed. Our results show that the additional context information we collect improves prediction quality, and that PBN can obtain comparable prediction quality to BMN, while model size and inference speed scale better with large input sizes. 2016-01-01T00:00:00Z