TY  - JOUR
A1  - Aarts, Alexander A.
A1  - Anderson, Joanna E.
A1  - Anderson, Christopher J.
A1  - Attridge, Peter R.
A1  - Attwood, Angela
A1  - Axt, Jordan
A1  - Babel, Molly
A1  - Bahnik, Stepan
A1  - Baranski, Erica
A1  - Barnett-Cowan, Michael
A1  - Bartmess, Elizabeth
A1  - Beer, Jennifer
A1  - Bell, Raoul
A1  - Bentley, Heather
A1  - Beyan, Leah
A1  - Binion, Grace
A1  - Borsboom, Denny
A1  - Bosch, Annick
A1  - Bosco, Frank A.
A1  - Bowman, Sara D.
A1  - Brandt, Mark J.
A1  - Braswell, Erin
A1  - Brohmer, Hilmar
A1  - Brown, Benjamin T.
A1  - Brown, Kristina
A1  - Bruening, Jovita
A1  - Calhoun-Sauls, Ann
A1  - Callahan, Shannon P.
A1  - Chagnon, Elizabeth
A1  - Chandler, Jesse
A1  - Chartier, Christopher R.
A1  - Cheung, Felix
A1  - Christopherson, Cody D.
A1  - Cillessen, Linda
A1  - Clay, Russ
A1  - Cleary, Hayley
A1  - Cloud, Mark D.
A1  - Cohn, Michael
A1  - Cohoon, Johanna
A1  - Columbus, Simon
A1  - Cordes, Andreas
A1  - Costantini, Giulio
A1  - Alvarez, Leslie D. Cramblet
A1  - Cremata, Ed
A1  - Crusius, Jan
A1  - DeCoster, Jamie
A1  - DeGaetano, Michelle A.
A1  - Della Penna, Nicolas
A1  - den Bezemer, Bobby
A1  - Deserno, Marie K.
A1  - Devitt, Olivia
A1  - Dewitte, Laura
A1  - Dobolyi, David G.
A1  - Dodson, Geneva T.
A1  - Donnellan, M. Brent
A1  - Donohue, Ryan
A1  - Dore, Rebecca A.
A1  - Dorrough, Angela
A1  - Dreber, Anna
A1  - Dugas, Michelle
A1  - Dunn, Elizabeth W.
A1  - Easey, Kayleigh
A1  - Eboigbe, Sylvia
A1  - Eggleston, Casey
A1  - Embley, Jo
A1  - Epskamp, Sacha
A1  - Errington, Timothy M.
A1  - Estel, Vivien
A1  - Farach, Frank J.
A1  - Feather, Jenelle
A1  - Fedor, Anna
A1  - Fernandez-Castilla, Belen
A1  - Fiedler, Susann
A1  - Field, James G.
A1  - Fitneva, Stanka A.
A1  - Flagan, Taru
A1  - Forest, Amanda L.
A1  - Forsell, Eskil
A1  - Foster, Joshua D.
A1  - Frank, Michael C.
A1  - Frazier, Rebecca S.
A1  - Fuchs, Heather
A1  - Gable, Philip
A1  - Galak, Jeff
A1  - Galliani, Elisa Maria
A1  - Gampa, Anup
A1  - Garcia, Sara
A1  - Gazarian, Douglas
A1  - Gilbert, Elizabeth
A1  - Giner-Sorolla, Roger
A1  - Glöckner, Andreas
A1  - Göllner, Lars
A1  - Goh, Jin X.
A1  - Goldberg, Rebecca
A1  - Goodbourn, Patrick T.
A1  - Gordon-McKeon, Shauna
A1  - Gorges, Bryan
A1  - Gorges, Jessie
A1  - Goss, Justin
A1  - Graham, Jesse
A1  - Grange, James A.
A1  - Gray, Jeremy
A1  - Hartgerink, Chris
A1  - Hartshorne, Joshua
A1  - Hasselman, Fred
A1  - Hayes, Timothy
A1  - Heikensten, Emma
A1  - Henninger, Felix
A1  - Hodsoll, John
A1  - Holubar, Taylor
A1  - Hoogendoorn, Gea
A1  - Humphries, Denise J.
A1  - Hung, Cathy O. -Y.
A1  - Immelman, Nathali
A1  - Irsik, Vanessa C.
A1  - Jahn, Georg
A1  - Jaekel, Frank
A1  - Jekel, Marc
A1  - Johannesson, Magnus
A1  - Johnson, Larissa G.
A1  - Johnson, David J.
A1  - Johnson, Kate M.
A1  - Johnston, William J.
A1  - Jonas, Kai
A1  - Joy-Gaba, Jennifer A.
A1  - Kappes, Heather Barry
A1  - Kelso, Kim
A1  - Kidwell, Mallory C.
A1  - Kim, Seung Kyung
A1  - Kirkhart, Matthew
A1  - Kleinberg, Bennett
A1  - Knezevic, Goran
A1  - Kolorz, Franziska Maria
A1  - Kossakowski, Jolanda J.
A1  - Krause, Robert Wilhelm
A1  - Krijnen, Job
A1  - Kuhlmann, Tim
A1  - Kunkels, Yoram K.
A1  - Kyc, Megan M.
A1  - Lai, Calvin K.
A1  - Laique, Aamir
A1  - Lakens, Daniel
A1  - Lane, Kristin A.
A1  - Lassetter, Bethany
A1  - Lazarevic, Ljiljana B.
A1  - LeBel, Etienne P.
A1  - Lee, Key Jung
A1  - Lee, Minha
A1  - Lemm, Kristi
A1  - Levitan, Carmel A.
A1  - Lewis, Melissa
A1  - Lin, Lin
A1  - Lin, Stephanie
A1  - Lippold, Matthias
A1  - Loureiro, Darren
A1  - Luteijn, Ilse
A1  - Mackinnon, Sean
A1  - Mainard, Heather N.
A1  - Marigold, Denise C.
A1  - Martin, Daniel P.
A1  - Martinez, Tylar
A1  - Masicampo, E. J.
A1  - Matacotta, Josh
A1  - Mathur, Maya
A1  - May, Michael
A1  - Mechin, Nicole
A1  - Mehta, Pranjal
A1  - Meixner, Johannes
A1  - Melinger, Alissa
A1  - Miller, Jeremy K.
A1  - Miller, Mallorie
A1  - Moore, Katherine
A1  - Möschl, Marcus
A1  - Motyl, Matt
A1  - Müller, Stephanie M.
A1  - Munafo, Marcus
A1  - Neijenhuijs, Koen I.
A1  - Nervi, Taylor
A1  - Nicolas, Gandalf
A1  - Nilsonne, Gustav
A1  - Nosek, Brian A.
A1  - Nuijten, Michele B.
A1  - Olsson, Catherine
A1  - Osborne, Colleen
A1  - Ostkamp, Lutz
A1  - Pavel, Misha
A1  - Penton-Voak, Ian S.
A1  - Perna, Olivia
A1  - Pernet, Cyril
A1  - Perugini, Marco
A1  - Pipitone, R. Nathan
A1  - Pitts, Michael
A1  - Plessow, Franziska
A1  - Prenoveau, Jason M.
A1  - Rahal, Rima-Maria
A1  - Ratliff, Kate A.
A1  - Reinhard, David
A1  - Renkewitz, Frank
A1  - Ricker, Ashley A.
A1  - Rigney, Anastasia
A1  - Rivers, Andrew M.
A1  - Roebke, Mark
A1  - Rutchick, Abraham M.
A1  - Ryan, Robert S.
A1  - Sahin, Onur
A1  - Saide, Anondah
A1  - Sandstrom, Gillian M.
A1  - Santos, David
A1  - Saxe, Rebecca
A1  - Schlegelmilch, Rene
A1  - Schmidt, Kathleen
A1  - Scholz, Sabine
A1  - Seibel, Larissa
A1  - Selterman, Dylan Faulkner
A1  - Shaki, Samuel
A1  - Simpson, William B.
A1  - Sinclair, H. Colleen
A1  - Skorinko, Jeanine L. M.
A1  - Slowik, Agnieszka
A1  - Snyder, Joel S.
A1  - Soderberg, Courtney
A1  - Sonnleitner, Carina
A1  - Spencer, Nick
A1  - Spies, Jeffrey R.
A1  - Steegen, Sara
A1  - Stieger, Stefan
A1  - Strohminger, Nina
A1  - Sullivan, Gavin B.
A1  - Talhelm, Thomas
A1  - Tapia, Megan
A1  - te Dorsthorst, Anniek
A1  - Thomae, Manuela
A1  - Thomas, Sarah L.
A1  - Tio, Pia
A1  - Traets, Frits
A1  - Tsang, Steve
A1  - Tuerlinckx, Francis
A1  - Turchan, Paul
A1  - Valasek, Milan
A1  - Van Aert, Robbie
A1  - van Assen, Marcel
A1  - van Bork, Riet
A1  - van de Ven, Mathijs
A1  - van den Bergh, Don
A1  - van der Hulst, Marije
A1  - van Dooren, Roel
A1  - van Doorn, Johnny
A1  - van Renswoude, Daan R.
A1  - van Rijn, Hedderik
A1  - Vanpaemel, Wolf
A1  - Echeverria, Alejandro Vasquez
A1  - Vazquez, Melissa
A1  - Velez, Natalia
A1  - Vermue, Marieke
A1  - Verschoor, Mark
A1  - Vianello, Michelangelo
A1  - Voracek, Martin
A1  - Vuu, Gina
A1  - Wagenmakers, Eric-Jan
A1  - Weerdmeester, Joanneke
A1  - Welsh, Ashlee
A1  - Westgate, Erin C.
A1  - Wissink, Joeri
A1  - Wood, Michael
A1  - Woods, Andy
A1  - Wright, Emily
A1  - Wu, Sining
A1  - Zeelenberg, Marcel
A1  - Zuni, Kellylynn
T1  - Estimating the reproducibility of psychological science
JF  - Science
N2  - Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.
Y1  - 2015
U6  - https://doi.org/10.1126/science.aac4716
SN  - 1095-9203
SN  - 0036-8075
VL  - 349
IS  - 6251
PB  - American Assoc. for the Advancement of Science
CY  - Washington
ER  - 
TY  - JOUR
A1  - Gabrysiak, Gregor
A1  - Giese, Holger
A1  - Seibel, Andreas
T1  - Towards next generation design thinking : scenario-based prototyping for designing complex software systems with multiple users
Y1  - 2011
SN  - 978-3-642-13756-3
ER  - 
TY  - JOUR
A1  - Gabrysiak, Gregor
A1  - Giese, Holger
A1  - Seibel, Andreas
T1  - Towards next-generation design thinking II : virtual muti-user software prototypes
Y1  - 2012
ER  - 
TY  - JOUR
A1  - Seibel, Andreas
A1  - Neumann, Stefan
A1  - Giese, Holger
T1  - Dynamic hierarchical mega models : comprehensive traceability and its efficient maintenance
N2  - In the world of model-driven engineering (MDE) support for traceability and maintenance of traceability information is essential. On the one hand, classical traceability approaches for MDE address this need by supporting automated creation of traceability information on the model element level. On the other hand, global model management approaches manually capture traceability information on the model level. However, there is currently no approach that supports comprehensive traceability, comprising traceability information on both levels, and efficient maintenance of traceability information, which requires a high-degree of automation and scalability. In this article, we present a comprehensive traceability approach that combines classical traceability approaches for MDE and global model management in form of dynamic hierarchical mega models. We further integrate efficient maintenance of traceability information based on top of dynamic hierarchical mega models. The proposed approach is further outlined by using an industrial case study and by presenting an implementation of the concepts in form of a prototype.
Y1  - 2010
UR  - http://www.springerlink.com/content/109378
U6  - https://doi.org/10.1007/s10270-009-0146-z
SN  - 1619-1366
ER  - 
TY  - JOUR
A1  - Henkler, Stefan
A1  - Oberthuer, Simon
A1  - Giese, Holger
A1  - Seibel, Andreas
T1  - Model-driven runtime resource predictions for advanced mechatronic systems with dynamic data structures
JF  - Computer systems science and engineering
N2  - The next generation of advanced mechatronic systems is expected to enhance their functionality and improve their performance by context-dependent behavior. Therefore, these systems require to represent information about their complex environment and changing sets of collaboration partners internally. This requirement is in contrast to the usually assumed static structures of embedded systems. In this paper, we present a model-driven approach which overcomes this situation by supporting dynamic data structures while still guaranteeing that valid worst-case execution times can be derived. It supports a flexible resource manager which avoids to operate with the prohibitive coarse worst-case boundaries but instead supports to run applications in different profiles which guarantee different resource requirements and put unused resources in a profile at other applications' disposal. By supporting the proper estimation of worst case execution time (WCET) and worst case number of iteration (WCNI) at runtime, we can further support to create new profiles, add or remove them at runtime in order to minimize the over-approximation of the resource consumption resulting from the dynamic data structures required for the outlined class of advanced systems.
KW  - Model-Driven Engineering
KW  - Safety Critical Systems
KW  - Dynamic Data Structures
KW  - Flexible Resource Manager
KW  - Runtime WCET Analysis
Y1  - 2011
SN  - 0267-6192
VL  - 26
IS  - 6
SP  - 505
EP  - 518
PB  - IOP Publ. Ltd.
CY  - Leicester
ER  - 
TY  - THES
A1  - Seibel, Andreas
T1  - Traceability and model management with executable and dynamic hierarchical megamodels
T1  - Traceability und Modell Management mit ausführbaren und dynamischen Megamodellen
N2  - Nowadays, model-driven engineering (MDE) promises to ease software development by decreasing the inherent complexity of classical software development. In order to deliver on this promise, MDE increases the level of abstraction and automation, through a consideration of domain-specific models (DSMs) and model operations (e.g. model transformations or code generations). DSMs conform to domain-specific modeling languages (DSMLs), which increase the level of abstraction, and model operations are first-class entities of software development because they increase the level of automation. Nevertheless, MDE has to deal with at least two new dimensions of complexity, which are basically caused by the increased linguistic and technological heterogeneity. The first dimension of complexity is setting up an MDE environment, an activity comprised of the implementation or selection of DSMLs and model operations. Setting up an MDE environment is both time-consuming and error-prone because of the implementation or adaptation of model operations. The second dimension of complexity is concerned with applying MDE for actual software development. Applying MDE is challenging because a collection of DSMs, which conform to potentially heterogeneous DSMLs, are required to completely specify a complex software system. A single DSML can only be used to describe a specific aspect of a software system at a certain level of abstraction and from a certain perspective. Additionally, DSMs are usually not independent but instead have inherent interdependencies, reflecting (partial) similar aspects of a software system at different levels of abstraction or from different perspectives. A subset of these dependencies are applications of various model operations, which are necessary to keep the degree of automation high. This becomes even worse when addressing the first dimension of complexity. Due to continuous changes, all kinds of dependencies, including the applications of model operations, must also be managed continuously. This comprises maintaining the existence of these dependencies and the appropriate (re-)application of model operations. The contribution of this thesis is an approach that combines traceability and model management to address the aforementioned challenges of configuring and applying MDE for software development. The approach is considered as a traceability approach because it supports capturing and automatically maintaining dependencies between DSMs. The approach is considered as a model management approach because it supports managing the automated (re-)application of heterogeneous model operations. In addition, the approach is considered as a comprehensive model management. Since the decomposition of model operations is encouraged to alleviate the first dimension of complexity, the subsequent composition of model operations is required to counteract their fragmentation. A significant portion of this thesis concerns itself with providing a method for the specification of decoupled yet still highly cohesive complex compositions of heterogeneous model operations. The approach supports two different kinds of compositions - data-flow compositions and context compositions. Data-flow composition is used to define a network of heterogeneous model operations coupled by sharing input and output DSMs alone. Context composition is related to a concept used in declarative model transformation approaches to compose individual model transformation rules (units) at any level of detail. In this thesis, context composition provides the ability to use a collection of dependencies as context for the composition of other dependencies, including model operations. In addition, the actual implementation of model operations, which are going to be composed, do not need to implement any composition concerns. The approach is realized by means of a formalism called an executable and dynamic hierarchical megamodel, based on the original idea of megamodels. This formalism supports specifying compositions of dependencies (traceability and model operations). On top of this formalism, traceability is realized by means of a localization concept, and model management by means of an execution concept.
N2  - Die modellgetriebene Softwareentwicklung (MDE) verspricht heutzutage, durch das Verringern der inhärenten Komplexität der klassischen Softwareentwicklung, das Entwickeln von Software zu vereinfachen. Um dies zu erreichen, erhöht MDE das Abstraktions- und Automationsniveau durch die Einbindung domänenspezifischer Modelle (DSMs) und Modelloperationen (z.B. Modelltransformationen oder Codegenerierungen). DSMs sind konform zu domänenspezifischen Modellierungssprachen (DSMLs), die dazu dienen das Abstraktionsniveau der Softwareentwicklung zu erhöhen. Modelloperationen sind essentiell für die Softwareentwicklung da diese den Grad der Automatisierung erhöhen. Dennoch muss MDE mit Komplexitätsdimensionen umgehen die sich grundsätzlich aus der erhöhten sprachlichen und technologischen Heterogenität ergeben. Die erste Komplexitätsdimension ist das Konfigurieren einer Umgebung für MDE. Diese Aktivität setzt sich aus der Implementierung und Selektion von DSMLs sowie Modelloperationen zusammen. Eine solche Aktivität ist gerade durch die Implementierung und Anpassung von Modelloperationen zeitintensiv sowie fehleranfällig. Die zweite Komplexitätsdimension hängt mit der Anwendung von MDE für die eigentliche Softwareentwicklung zusammen. Das Anwenden von MDE ist eine Herausforderung weil eine Menge von heterogenen DSMs, die unterschiedlichen DSMLs unterliegen, erforderlich sind um ein komplexes Softwaresystem zu spezifizieren. Individuelle DSMLs werden verwendet um spezifische Aspekte eines Softwaresystems auf bestimmten Abstraktionsniveaus und aus bestimmten Perspektiven zu beschreiben. Hinzu kommt, dass DSMs sowie DSMLs grundsätzlich nicht unabhängig sind, sondern inhärente Abhängigkeiten besitzen. Diese Abhängigkeiten reflektieren äquivalente Aspekte eines Softwaresystems. Eine Teilmenge dieser Abhängigkeiten reflektieren Anwendungen diverser Modelloperationen, die notwendig sind um den Grad der Automatisierung hoch zu halten. Dies wird erschwert wenn man die erste Komplexitätsdimension hinzuzieht. Aufgrund kontinuierlicher Änderungen der DSMs, müssen alle Arten von Abhängigkeiten, inklusive die Anwendung von Modelloperationen, kontinuierlich verwaltet werden. Dies beinhaltet die Wartung dieser Abhängigkeiten und das sachgerechte (wiederholte) Anwenden von Modelloperationen. Der Beitrag dieser Arbeit ist ein Ansatz, der die Bereiche Traceability und Model Management vereint. Das Erfassen und die automatische Verwaltung von Abhängigkeiten zwischen DSMs unterstützt Traceability, während das (automatische) wiederholte Anwenden von heterogenen Modelloperationen Model Management ermöglicht. Dadurch werden die zuvor erwähnten Herausforderungen der Konfiguration und Anwendung von MDE überwunden. Die negativen Auswirkungen der ersten Komplexitätsdimension können gelindert werden indem Modelloperationen in atomare Einheiten zerlegt werden. Um der implizierten Fragmentierung entgegenzuwirken, erfordert dies allerdings eine nachfolgende Komposition der Modelloperationen. Der Ansatz wird als erweitertes Model Management betrachtet, da ein signifikanter Anteil dieser Arbeit die Kompositionen von heterogenen Modelloperationen behandelt. Unterstützt werden zwei unterschiedliche Arten von Kompositionen. Datenfluss-Kompositionen werden verwendet, um Netzwerke von heterogenen Modelloperationen zu beschreiben, die nur durch das Teilen von Ein- und Ausgabe DSMs komponiert werden. Kontext-Kompositionen bedienen sich eines Konzepts, das von deklarativen Modelltransformationen bekannt ist. Dies ermöglicht die Komposition von unabhängigen Transformationsregeln auf unterschiedlichsten Detailebenen. Die in dieser Arbeit eingeführten Kontext-Kompositionen bieten die Möglichkeit eine Menge von unterschiedlichsten Abhängigkeiten als Kontext für eine Komposition zu verwenden -- unabhängig davon ob diese Abhängigkeit eine Modelloperation repräsentiert. Zusätzlich müssen die Modelloperationen, die komponiert werden, selber keine Kompositionsaspekte implementieren, was deren Wiederverwendbarkeit erhöht. Realisiert wird dieser Ansatz durch einen Formalismus der Executable and Dynamic Hierarchical Megamodel genannt wird und auf der originalen Idee der Megamodelle basiert. Auf Basis dieses Formalismus' sind die Konzepte Traceability (hier Localization) und Model Management (hier Execution) umgesetzt.
KW  - Traceability
KW  - Modell Management
KW  - Megamodell
KW  - Modellgetriebene Entwicklung
KW  - Komposition
KW  - Traceability
KW  - Model Management
KW  - Megamodel
KW  - Model-Driven Engineering
KW  - Composition
Y1  - 2012
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-64222
ER  -