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- KonferenzbeitragScaling size and parameter spaces in variability-aware software performance models(Software Engineering 2016, 2016) Kowal, Matthias; Tschaikowski, Max; Tribastone, Mirco; Schaefer, InaModel-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.
- KonferenzbeitragPerformance-Influence Models(Software Engineering 2016, 2016) Siegmund, Norbert; Grebhahn, Alexander; Apel, Sven; Kästner, Christian
- KonferenzbeitragAutomated workload characterization for I/O performance analysis in virtualized environments(Software Engineering 2016, 2016) Busch, Axel; Noorshams, Qais; Kounev, Samuel; Koziolek, Anne; Reussner, Ralf; Amrehn, Erich
- KonferenzbeitragIntelligent code completion with Bayesian networks(Software Engineering 2016, 2016) Proksch, Sebastian; Lerch, Johannes; Mezini, MiraCode 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.
- KonferenzbeitragLehRE: 2. Workshop “Lehre für Requirements Engineering“(Software Engineering 2016, 2016) Weißbach, Rüdiger; Fahsel, Jörn; Herrmann, Andrea; Hoffmann, Anne; Landes, DieterLehRE ist ein Workshop über Lehre und Training für Requirements Engineering. Auf der SE2016 stehen Kompetenzorientierung und Agilität in der Lehre im Vordergrund, außerdem sollen Einsatzmöglichkeiten elektronischer Lehrplattformen in der RE-Lehre in einem Gastvortrag diskutiert werden.
- Editiertes BuchSoftware Engineering 2016(2016)
- KonferenzbeitragFS-MCPS: 2nd workshop on fail safety in medical cyber-physical systems(Software Engineering 2016, 2016) Schlaefer, Alexander; Schupp, Sibylle; Stollenwerk, André
- KonferenzbeitragEMLS16: 3rd collaborative workshop on evolution and maintenance of long-living software systems(Software Engineering 2016, 2016) Heinrich, Robert; Jung, Reiner; Konersmann, Marco; Schmieders, Eric
- KonferenzbeitragATPS 2016: 9. Arbeitstagung Programmiersprachen(Software Engineering 2016, 2016) Krall, Andreas; Schaefer, Ina
- KonferenzbeitragCSE 2016: workshop on continuous software engineering(Software Engineering 2016, 2016) Lichter, Horst; Brügge, Bernd; Riehle, Dirk