@article{AartsAndersonAndersonetal.2015, author = {Aarts, Alexander A. and Anderson, Joanna E. and Anderson, Christopher J. and Attridge, Peter R. and Attwood, Angela and Axt, Jordan and Babel, Molly and Bahnik, Stepan and Baranski, Erica and Barnett-Cowan, Michael and Bartmess, Elizabeth and Beer, Jennifer and Bell, Raoul and Bentley, Heather and Beyan, Leah and Binion, Grace and Borsboom, Denny and Bosch, Annick and Bosco, Frank A. and Bowman, Sara D. and Brandt, Mark J. and Braswell, Erin and Brohmer, Hilmar and Brown, Benjamin T. and Brown, Kristina and Bruening, Jovita and Calhoun-Sauls, Ann and Callahan, Shannon P. and Chagnon, Elizabeth and Chandler, Jesse and Chartier, Christopher R. and Cheung, Felix and Christopherson, Cody D. and Cillessen, Linda and Clay, Russ and Cleary, Hayley and Cloud, Mark D. and Cohn, Michael and Cohoon, Johanna and Columbus, Simon and Cordes, Andreas and Costantini, Giulio and Alvarez, Leslie D. Cramblet and Cremata, Ed and Crusius, Jan and DeCoster, Jamie and DeGaetano, Michelle A. and Della Penna, Nicolas and den Bezemer, Bobby and Deserno, Marie K. and Devitt, Olivia and Dewitte, Laura and Dobolyi, David G. and Dodson, Geneva T. and Donnellan, M. Brent and Donohue, Ryan and Dore, Rebecca A. and Dorrough, Angela and Dreber, Anna and Dugas, Michelle and Dunn, Elizabeth W. and Easey, Kayleigh and Eboigbe, Sylvia and Eggleston, Casey and Embley, Jo and Epskamp, Sacha and Errington, Timothy M. and Estel, Vivien and Farach, Frank J. and Feather, Jenelle and Fedor, Anna and Fernandez-Castilla, Belen and Fiedler, Susann and Field, James G. and Fitneva, Stanka A. and Flagan, Taru and Forest, Amanda L. and Forsell, Eskil and Foster, Joshua D. and Frank, Michael C. and Frazier, Rebecca S. and Fuchs, Heather and Gable, Philip and Galak, Jeff and Galliani, Elisa Maria and Gampa, Anup and Garcia, Sara and Gazarian, Douglas and Gilbert, Elizabeth and Giner-Sorolla, Roger and Gl{\"o}ckner, Andreas and G{\"o}llner, Lars and Goh, Jin X. and Goldberg, Rebecca and Goodbourn, Patrick T. and Gordon-McKeon, Shauna and Gorges, Bryan and Gorges, Jessie and Goss, Justin and Graham, Jesse and Grange, James A. and Gray, Jeremy and Hartgerink, Chris and Hartshorne, Joshua and Hasselman, Fred and Hayes, Timothy and Heikensten, Emma and Henninger, Felix and Hodsoll, John and Holubar, Taylor and Hoogendoorn, Gea and Humphries, Denise J. and Hung, Cathy O. -Y. and Immelman, Nathali and Irsik, Vanessa C. and Jahn, Georg and Jaekel, Frank and Jekel, Marc and Johannesson, Magnus and Johnson, Larissa G. and Johnson, David J. and Johnson, Kate M. and Johnston, William J. and Jonas, Kai and Joy-Gaba, Jennifer A. and Kappes, Heather Barry and Kelso, Kim and Kidwell, Mallory C. and Kim, Seung Kyung and Kirkhart, Matthew and Kleinberg, Bennett and Knezevic, Goran and Kolorz, Franziska Maria and Kossakowski, Jolanda J. and Krause, Robert Wilhelm and Krijnen, Job and Kuhlmann, Tim and Kunkels, Yoram K. and Kyc, Megan M. and Lai, Calvin K. and Laique, Aamir and Lakens, Daniel and Lane, Kristin A. and Lassetter, Bethany and Lazarevic, Ljiljana B. and LeBel, Etienne P. and Lee, Key Jung and Lee, Minha and Lemm, Kristi and Levitan, Carmel A. and Lewis, Melissa and Lin, Lin and Lin, Stephanie and Lippold, Matthias and Loureiro, Darren and Luteijn, Ilse and Mackinnon, Sean and Mainard, Heather N. and Marigold, Denise C. and Martin, Daniel P. and Martinez, Tylar and Masicampo, E. J. and Matacotta, Josh and Mathur, Maya and May, Michael and Mechin, Nicole and Mehta, Pranjal and Meixner, Johannes and Melinger, Alissa and Miller, Jeremy K. and Miller, Mallorie and Moore, Katherine and M{\"o}schl, Marcus and Motyl, Matt and M{\"u}ller, Stephanie M. and Munafo, Marcus and Neijenhuijs, Koen I. and Nervi, Taylor and Nicolas, Gandalf and Nilsonne, Gustav and Nosek, Brian A. and Nuijten, Michele B. and Olsson, Catherine and Osborne, Colleen and Ostkamp, Lutz and Pavel, Misha and Penton-Voak, Ian S. and Perna, Olivia and Pernet, Cyril and Perugini, Marco and Pipitone, R. Nathan and Pitts, Michael and Plessow, Franziska and Prenoveau, Jason M. and Rahal, Rima-Maria and Ratliff, Kate A. and Reinhard, David and Renkewitz, Frank and Ricker, Ashley A. and Rigney, Anastasia and Rivers, Andrew M. and Roebke, Mark and Rutchick, Abraham M. and Ryan, Robert S. and Sahin, Onur and Saide, Anondah and Sandstrom, Gillian M. and Santos, David and Saxe, Rebecca and Schlegelmilch, Rene and Schmidt, Kathleen and Scholz, Sabine and Seibel, Larissa and Selterman, Dylan Faulkner and Shaki, Samuel and Simpson, William B. and Sinclair, H. Colleen and Skorinko, Jeanine L. M. and Slowik, Agnieszka and Snyder, Joel S. and Soderberg, Courtney and Sonnleitner, Carina and Spencer, Nick and Spies, Jeffrey R. and Steegen, Sara and Stieger, Stefan and Strohminger, Nina and Sullivan, Gavin B. and Talhelm, Thomas and Tapia, Megan and te Dorsthorst, Anniek and Thomae, Manuela and Thomas, Sarah L. and Tio, Pia and Traets, Frits and Tsang, Steve and Tuerlinckx, Francis and Turchan, Paul and Valasek, Milan and Van Aert, Robbie and van Assen, Marcel and van Bork, Riet and van de Ven, Mathijs and van den Bergh, Don and van der Hulst, Marije and van Dooren, Roel and van Doorn, Johnny and van Renswoude, Daan R. and van Rijn, Hedderik and Vanpaemel, Wolf and Echeverria, Alejandro Vasquez and Vazquez, Melissa and Velez, Natalia and Vermue, Marieke and Verschoor, Mark and Vianello, Michelangelo and Voracek, Martin and Vuu, Gina and Wagenmakers, Eric-Jan and Weerdmeester, Joanneke and Welsh, Ashlee and Westgate, Erin C. and Wissink, Joeri and Wood, Michael and Woods, Andy and Wright, Emily and Wu, Sining and Zeelenberg, Marcel and Zuni, Kellylynn}, title = {Estimating the reproducibility of psychological science}, series = {Science}, volume = {349}, journal = {Science}, number = {6251}, publisher = {American Assoc. for the Advancement of Science}, address = {Washington}, organization = {Open Sci Collaboration}, issn = {1095-9203}, doi = {10.1126/science.aac4716}, pages = {8}, year = {2015}, abstract = {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.}, language = {en} } @article{GabrysiakGieseSeibel2011, author = {Gabrysiak, Gregor and Giese, Holger and Seibel, Andreas}, title = {Towards next generation design thinking : scenario-based prototyping for designing complex software systems with multiple users}, isbn = {978-3-642-13756-3}, year = {2011}, language = {en} } @article{GabrysiakGieseSeibel2012, author = {Gabrysiak, Gregor and Giese, Holger and Seibel, Andreas}, title = {Towards next-generation design thinking II : virtual muti-user software prototypes}, year = {2012}, language = {en} } @article{SeibelNeumannGiese2010, author = {Seibel, Andreas and Neumann, Stefan and Giese, Holger}, title = {Dynamic hierarchical mega models : comprehensive traceability and its efficient maintenance}, issn = {1619-1366}, doi = {10.1007/s10270-009-0146-z}, year = {2010}, abstract = {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.}, language = {en} } @article{HenklerOberthuerGieseetal.2011, author = {Henkler, Stefan and Oberthuer, Simon and Giese, Holger and Seibel, Andreas}, title = {Model-driven runtime resource predictions for advanced mechatronic systems with dynamic data structures}, series = {Computer systems science and engineering}, volume = {26}, journal = {Computer systems science and engineering}, number = {6}, publisher = {IOP Publ. Ltd.}, address = {Leicester}, issn = {0267-6192}, pages = {505 -- 518}, year = {2011}, abstract = {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.}, language = {en} } @phdthesis{Seibel2012, author = {Seibel, Andreas}, title = {Traceability and model management with executable and dynamic hierarchical megamodels}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-64222}, school = {Universit{\"a}t Potsdam}, year = {2012}, abstract = {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.}, language = {en} }