WorldCat Identities

Czarnecki, Krzysztof 1970-

Overview
Works: 39 works in 132 publications in 2 languages and 2,470 library holdings
Genres: Conference papers and proceedings  Academic theses 
Roles: Author, Editor, htt, dgs, Other, Contributor, wpr
Classifications: QA76.624, 005.1
Publication Timeline
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Most widely held works by Krzysztof Czarnecki
Model driven engineering languages and systems : 11th international conference, MoDELS 2008, Toulouse, France, September 28 - October 3, 2008 : proceedings by David Hutchison( )

21 editions published in 2008 in English and held by 543 WorldCat member libraries worldwide

This book constitutes the refereed proceedings of the 11th International Conference on Model Driven Engineering Languages and Systems, MoDELS 2008, held in Toulouse, France, during September 28-October 3, 2008. The 58 revised full papers presented were carefully reviewed and selected from 271 submissions. The book also contains three keynote speeches and contributions to workshops, symposia, tutorials and panels at the conference. The papers are organized in topical sections on Model Transformation: Foundations; Requirements Modeling; Domain-Specific Modeling; Model Transformation: Techniques, Composition and Analysis of Behavioral Models; Model Comprehension; Model Management; Behavioral Conformance and Refinement; Metamodeling and Modularity; Constraints; Model Analysis; Service-Oriented Architectures; Adaptive and Autonomic Systems; Empirical Studies; Evolution and Reverse Engineering; Modeling Language Semantics; Dependability Analysis and Testing; Aspect-Oriented Modeling; Structural Modeling;and Embedded Systems
Generative and component-based software engineering : first international symposium, GCSE'99, Erfurt, Germany, September 28-30, 1999 : revised papers by Krzysztof Czarnecki( )

31 editions published in 2000 in English and held by 480 WorldCat member libraries worldwide

This book constitutes the thoroughly refereed post-proceedings of the First International Symposium on Generative & Component-Based Software Engineering, GCSE'99, held in Erfurt, Germany, in September 1999. The 15 thoroughly revised full papers presented together with an invited paper have gone through two rounds of reviewing & improvement. The book offers topical sections on aspects, generative approaches, language composition, component-oriented language idioms, & domain analysis & component-based development
Software language engineering : 5th international conference, SLE 2012, Dresden, Germany, September 26-28, 2012 ; revised selected papers by Krzysztof Czarnecki( )

15 editions published in 2013 in 3 languages and held by 466 WorldCat member libraries worldwide

This book constitutes the thoroughly refereed post-proceedings of the 5th International Conference on Software Language Engineering, SLE 2012, held in Dresden, Germany, in September 2012. The 17 papers presented together with 2 tool demonstration papers were carefully reviewed and selected from 62 submissions. SLE's foremost mission is to encourage and organize communication between communities that have traditionally looked at software languages from different, more specialized, and yet complementary perspectives. SLE emphasizes the fundamental notion of languages as opposed to any realization in specific technical spaces
Generative programming : methods, tools, and applications by Krzysztof Czarnecki( Book )

20 editions published between 2000 and 2005 in English and held by 327 WorldCat member libraries worldwide

Generative Programming (GP) offers great promise to application developers. It makes the idea of moving from ìone of a kindî software systems to the semi-automated manufacture of wide varieties of software quite real. In short, GP is about recognizing the benefits of automation in software development
MODULARITY'16 : proceedings of the 15th International Conference on Modularity : March 14-17, 2016, Málaga, Spain by International Conference on Modularity( )

2 editions published in 2016 in English and held by 246 WorldCat member libraries worldwide

MODULARITY Companion '16 : companion proceedings of the 15th International Conference on Modularity : March 14-17, 2016, Málaga, Spain by Modularity (Conference)( )

2 editions published in 2016 in English and held by 245 WorldCat member libraries worldwide

Model-driven software development : technology, engineering, management by Thomas Stahl( )

5 editions published between 2006 and 2014 in English and held by 49 WorldCat member libraries worldwide

Model-Driven Software Development (MDSD) is currently a highly regarded development paradigm among developers and researchers. With the advent of OMG's MDA and Microsoft's Software Factories, the MDSD approach has moved to the centre of the programmer's attention, becoming the focus of conferences such as OOPSLA, JAOO and OOP. MDSD is about using domain-specific languages to create models that express application structure or behaviour in an efficient and domain-specific way. These models are subsequently transformed into executable code by a sequence of model transformations. This practical guide for software architects and developers is peppered with practical examples and extensive case studies. International experts deliver: * A comprehensive overview of MDSD and how it relates to industry standards such as MDA and Software Factories. * Technical details on meta modeling, DSL construction, model-to-model and model-to-code transformations, and software architecture. * Invaluable insight into the software development process, plus engineering issues such as versioning, testing and product line engineering. * Essential management knowledge covering economic and organizational topics, from a global perspective. Get started and benefit from some practical support along the way!
Proceedings of the 5th Workshop on Variability Modeling of Software-Intensive Systems by Patrick Heymans( )

2 editions published in 2011 in English and held by 18 WorldCat member libraries worldwide

Model transformation languages for domain-specific workbenches by Arif Wider( )

1 edition published in 2015 in English and held by 18 WorldCat member libraries worldwide

Proceedings of the VARiability for You Workshop Variability Modeling Made Useful for Everyone by Øystein Haugen( )

1 edition published in 2012 in English and held by 8 WorldCat member libraries worldwide

Synthesis and exploration of multi-level, multi-perspective architectures of automotive embedded system by Jordan A Ross( )

2 editions published between 2016 and 2017 in English and held by 5 WorldCat member libraries worldwide

In industry, evaluating candidate architectures of automotive embedded systems is routinely done during the design process. Today's engineers, however, are limited in the number of candidates that they are able to evaluate in order to find the optimal architectures. This limitation results from the difficulty in defining the candidates as it is a mostly manual process. In this work, we propose a way to synthesize multi-level, multi-perspective candidate architectures and to explore them across the different layers and perspectives. Using a reference model similar to the EAST-ADL domain model but with a focus on early design, we explore the candidate architectures for two case studies: an automotive power window system and the central door locking system. Further, we provide a comprehensive set of questions, based on the different layers and perspectives, that engineers can ask to synthesize only the candidates relevant to their task at hand. Finally, using the modeling language Clafer, which is supported by automated backend reasoners, we show that it is possible to synthesize and explore optimal candidate architectures for two highly configurable automotive subsystems
Objektorientierte Entwicklung von Software-Produktlinien zur Serienfertigung von Software-Systemen by Kai Böllert( )

1 edition published in 2002 in German and held by 4 WorldCat member libraries worldwide

Eine Software-Produktlinie umfaßt eine Gruppe von Software-Systemen, die auf Basis gemeinsam genutzter Komponenten entwickelt werden. Die Systeme entstehen aufgrund der Wiederverwendung in kurzer Zeit. Ihre Entwicklung verursacht verhältnismäßig geringe Kosten, und das Ergebnis ist von hoher Qualität. Vorteile, die eine an einzelnen Systemen ausgerichtete Softwareentwicklung nicht in vergleichbarem Maßstab erzielt. Noch einen Schritt weiter geht die generative Programmierung. Mit ihr können Systeme automatisiert aus Produktlinien generiert werden. Dazu überträgt die generative Programmierung Konzepte der aus der Automobilindustrie bekannten Serienfertigung in die Welt des Software-Engineerings. Die Automatisierung verkürzt erneut Entwicklungszeit und -kosten für Systeme aus der Produktlinie. Die bisherigen Methoden zur Entwicklung von Produktlinien berücksichtigen die generative Programmierung nicht. Statt dessen werden aus den Produktlinien manuell Systeme entwickelt. Zur Behebung dieses Umstands leistet die vorliegende Arbeit einen Beitrag. Sie stellt HyperFeatuRSEB vor, eine Methode zur objektorientierten Entwicklung von Produktlinien, aus denen Systeme automatisiert in Serie gefertigt werden können. Dazu nutzt die Methode den Hyperspace-Ansatz, eine Technik der generativen Programmierung. Bevor mit diesem Ansatz entwickelt werden kann, müssen seine abstrakten Konzepte und Begriffe auf Modellierungs- und Programmiersprachen abgebildet werden. Bisher existiert eine solche Abbildung für Java. Um mit dem Ansatz Produktlinien nicht nur zu implementieren, sondern auch zu modellieren, entwickelt diese Arbeit Hyper/UML: eine Abbildung des Hyperspace-Ansatzes auf die UML (Unified Modeling Language). Die Serienfertigung übernimmt ein eigens entworfener, für beliebige mit HyperFeatuRSEB entwickelte Produktlinien einsetzbarer Generator. Die Praxistauglichkeit der Methode untermauert eine größere empirische Fallstudie, in der eine Produktlinie für das Gesellschaftsspiel "Die Siedler von Catan" entwickelt worden ist
Modeling the effects of autosar overhead on automotive application software timing and schedulability by Manish Chauhan( )

1 edition published in 2016 in English and held by 3 WorldCat member libraries worldwide

AUTOSAR (AUTomotive Open System ARchitecture) provides an open and standardized E/E architecture to support modularity, transferability, reusability and scalability of the various components required to implement a function in a vehicle. AUTOSAR has become the de-facto standard for the automotive application development. Safety-critical nature of the automobiles makes the automotive application development challenging, and due to the growing complexity of the software in modern day vehicles, it has become even more challenging. A system is called schedulable when it meets all its real-time requirements under all the possible scenarios. An automotive application should always be schedulable; failing it can have grim consequences. The overhead added by the AUTOSAR stack can significantly change the schedulability of an automotive application. This thesis proposes an overhead-aware method to find a schedulable design configuration for an AUTOSAR application. The method allows measuring the overheads of an AUTOSAR stack implementation and assessing the impacts of the overheads on the timing and schedulability of an application using a timing model of the application. The thesis demonstrates the application of the method on a case study, and finally, it demonstrates the effects of the different types of system overheads on the timing and schedulability on a range of synthetic applications
ClaferMPS : modeling and optimizing automotive electric/electronic architectures using domain-specific languages by Eldar Khalilov( )

1 edition published in 2017 in English and held by 3 WorldCat member libraries worldwide

Modern automotive electric/electronic (E/E) architectures are growing to the point where architects can no longer manually predict the effects of their design decisions. Thus, in addition to applying an architecture reference model to decompose their architectures, they also require tools for synthesizing and evaluating candidate architectures during the design process. Clafer is a modeling language, which has been used to model variable multi-layer, multi-perspective automotive system architectures according to an architecture reference model. Clafer tools allow architects to synthesize optimal candidates and evaluate effects of their design decisions. However, since Clafer is a general-purpose structural modeling language, it does not help the architects in building models conforming to the given architecture reference model. In this work, we present ClaferMPS, a set of extensible languages and IDE for modeling E/E architectures using Clafer. First, we present an E/E architecture domain-specific language (DSL) built on top of Clafer, which embodies the reference model and which guides the architects in correctly applying the reference model. We then evaluate the DSL and its implementation by modeling two existing automotive systems, which were originally modeled in plain Clafer. The evaluation showed that by using the DSL, an evaluator obtained correct models by construction because the DSL helped prevent typical errors that are easy to make in plain Clafer. The evaluator was also able to synthesize and evaluate candidate architectures as with plain Clafer. Finally, we demonstrate extensibility capabilities of ClaferMPS. Our implementation is built on top of the JetBrains Meta Programming System, which supports language modularization and composition, multi-stage transformations and projectional editing. As a result, ClaferMPS allows third parties to seamlessly add extensions to both Clafer and the E/E architecture DSL without invasive changes. To illustrate this approach, we consider the Robot Operating System (ROS) communications infrastructure, a case study, which is outside the scope of the existing reference model. We show how the E/E architecture DSL can be adapted to the new domain using MPS language modularization and composition
Runtime restriction of the operational design domain : a safety concept for automated vehicles by Ian Colwell( )

1 edition published in 2018 in English and held by 3 WorldCat member libraries worldwide

Automated vehicles need to operate safely in a wide range of environments and hazards. The complex systems that make up an automated vehicle must also ensure safety in the event of system failures. This thesis proposes an approach and architectural design for achieving maximum functionality in the case of system failures. The Operational Design Domain (ODD) defines the domain over which the automated vehicle can operate safely. We propose modifying a runtime representation of the ODD based on current system capabilities. This enables the system to react with context-appropriate responses depending on the remaining degraded functionality. In addition to proposing an architectural design, we have implemented the approach to prove its viability. An analysis of the approach also highlights the strengths and weaknesses of the approach and how best to apply it. The proof of concept has shown promising directions for future work and moved our automated vehicle research platform closer to achieving level 4 automation. A ROS-based architecture extraction tool is also presented. This tool helped guide the architectural development and integration of the automated vehicle research platform in use at the University of Waterloo, and improve the visibility of safety and testing procedures for the team
Understanding and enhancing CDCL-based SAT solvers by Edward Zulkoski( )

1 edition published in 2018 in English and held by 3 WorldCat member libraries worldwide

Modern conflict-driven clause-learning (CDCL) Boolean satisfiability (SAT) solvers routinely solve formulas from industrial domains with millions of variables and clauses, despite the Boolean satisfiability problem being NP-complete and widely regarded as intractable in general. At the same time, very small crafted or randomly generated formulas are often infeasible for CDCL solvers. A commonly proposed explanation is that these solvers somehow exploit the underlying structure inherent in industrial instances. A better understanding of the structure of Boolean formulas not only enables improvements to modern SAT solvers, but also lends insight as to why solvers perform well or poorly on certain types of instances. Even further, examining solvers through the lens of these underlying structures can help to distinguish the behavior of different solving heuristics, both in theory and practice. The first issue we address relates to the representation of SAT formulas. A given Boolean satisfiability problem can be represented in arbitrarily many ways, and the type of encoding can have significant effects on SAT solver performance. Further, in some cases, a direct encoding to SAT may not be the best choice. We introduce a new system that integrates SAT solving with computer algebra systems (CAS) to address representation issues for several graph-theoretic problems. We use this system to improve the bounds on several finitely-verified conjectures related to graph-theoretic problems. We demonstrate how our approach is more appropriate for these problems than other off-the-shelf SAT-based tools. For more typical SAT formulas, a better understanding of their underlying structural properties, and how they relate to SAT solving, can deepen our understanding of SAT. We perform a largescale evaluation of many of the popular structural measures of formulas, such as community structure, treewidth, and backdoors. We investigate how these parameters correlate with CDCL solving time, and whether they can effectively be used to distinguish formulas from different domains. We demonstrate how these measures can be used as a means to understand the behavior of solvers during search. A common theme is that the solver exhibits locality during search through the lens of these underlying structures, and that the choice of solving heuristic can greatly influence this locality. We posit that this local behavior of modern SAT solvers is crucial to their performance. The remaining contributions dive deeper into two new measures of SAT formulas. We first consider a simple measure, denoted "mergeability," which characterizes the proportion of input clauses pairs that can resolve and merge. We develop a formula generator that takes as input a seed formula, and creates a sequence of increasingly more mergeable formulas, while maintaining many of the properties of the original formula. Experiments over randomly-generated industrial-like instances suggest that mergeability strongly negatively correlates with CDCL solving time, i.e., as the mergeability of formulas increases, the solving time decreases, particularly for unsatisfiable instances. Our final contribution considers whether one of the aforementioned measures, namely backdoor size, is influenced by solver heuristics in theory. Starting from the notion of learning-sensitive (LS) backdoors, we consider various extensions of LS backdoors by incorporating different branching heuristics and restart policies. We introduce learning-sensitive with restarts (LSR) backdoors and show that, when backjumping is disallowed, LSR backdoors may be exponentially smaller than LS backdoors. We further demonstrate that the size of LSR backdoors are dependent on the learning scheme used during search. Finally, we present new algorithms to compute upper-bounds on LSR backdoors that intrinsically rely upon restarts, and can be computed with a single run of a SAT solver. We empirically demonstrate that this can often produce smaller backdoors than previous approaches to computing LS backdoors
3D online multi-object tracking for autonomous driving by Venkateshwaran Balasubramanian( )

1 edition published in 2019 in English and held by 3 WorldCat member libraries worldwide

This research work focuses on exploring a novel 3D multi-object tracking architecture: 'FANTrack: 3D Multi-Object Tracking with Feature Association Network' for autonomous driving, based on tracking by detection and online tracking strategies using deep learning architectures for data association. The problem of multi-target tracking aims to assign noisy detections to a-priori unknown and time-varying number of tracked objects across a sequence of frames. A majority of the existing solutions focus on either tediously designing cost functions or formulating the task of data association as a complex optimization problem that can be solved effectively. Instead, we exploit the power of deep learning to formulate the data association problem as inference in a CNN. To this end, we propose to learn a similarity function that combines cues from both image and spatial features of objects. The proposed approach consists of a similarity network that predicts the similarity scores of the object pairs and builds a local similarity map. Another network formulates the data association problem as inference in a CNN by using the similarity scores and spatial information. The model learns to perform global assignments in 3D purely from data, handles noisy detections and a varying number of targets, and is easy to train. Experiments on the challenging Kitti dataset show competitive results with the state of the art. The model is finally implemented in ROS and deployed on our autonomous vehicle to show the robustness and online tracking capabilities. The proposed tracker runs alongside the object detector utilizing the resources efficiently
Autonomous driving : a multi-objective deep reinforcement learning approach by Changjian Li( )

1 edition published in 2019 in English and held by 3 WorldCat member libraries worldwide

Autonomous driving is a challenging domain that entails multiple aspects: a vehicle should be able to drive to its destination as fast as possible while avoiding collision, obeying traffic rules and ensuring the comfort of passengers. It's representative of complex reinforcement learning tasks humans encounter in real life. The aim of this thesis is to explore the effectiveness of multi-objective reinforcement learning for such tasks characterized by autonomous driving. In particular, it shows that: 1. Multi-objective reinforcement learning is effective at overcoming some of the difficulties faced by scalar-reward reinforcement learning, and a multi-objective DQN agent based on a variant of thresholded lexicographic Q-learning is successfully trained to drive on multi-lane roads and intersections, yielding and changing lanes according to traffic rules. 2. Data efficiency of (multi-objective) reinforcement learning can be significantly improved by exploiting the factored structure of a task. Specifically, factored Q functions learned on the factored state space can be used as features to the original Q function to speed up learning. 3. Inclusion of history-dependent policies enables an intuitive exact algorithm for multi-objective reinforcement learning with thresholded lexicographic order
Meta-learning performance prediction of highly configurable systems : a cost-oriented approach by Atri Sarkar( )

1 edition published in 2016 in English and held by 3 WorldCat member libraries worldwide

A key challenge of the development and maintenance of configurable systems is to predict the performance of individual system variants based on the features selected. It is usually infeasible to measure the performance of all possible variants, due to feature combinatorics. Previous approaches predict performance based on small samples of measured variants, but it is still open how to dynamically determine an ideal sample that balances prediction accuracy and measurement effort. In this work, we adapt two widely-used sampling strategies for performance prediction to the domain of configurable systems and evaluate them in terms of sampling cost, which considers prediction accuracy and measurement effort simultaneously. To generate an initial sample, we develop two sampling algorithms. One based on a traditional method of t-way feature coverage, and another based on a new heuristic of feature-frequencies. Using empirical data from six real-world systems, we evaluate the two sampling algorithms and discuss trade-offs. Furthermore, we conduct extensive sensitivity analysis of the cost model metric we use for evaluation, and analyze stability of learning behavior of the subject systems
Machine learning for SAT solvers by Jia Hui Liang( )

1 edition published in 2018 in English and held by 3 WorldCat member libraries worldwide

Boolean SAT solvers are indispensable tools in a variety of domains in computer science and engineering where efficient search is required. Not only does this relieve the burden on the users of implementing their own search algorithm, they also leverage the surprising effectiveness of modern SAT solvers. Thanks to many decades of cumulative effort, researchers have made persistent improvements to SAT technology to the point where nowadays the best solvers are routinely used to solve extremely large instances with millions of variables. Even though our current paradigm of SAT solvers runs in worst-case exponential time, it appears that the techniques and heuristics embedded in these solvers avert the worst-case exponential time in practice. The implementations of these various solver heuristics and techniques are vital to the solvers effectiveness in practice. The state-of-the-art heuristics and techniques gather data during the run of the solver to inform their choices like which variable to branch on next or when to invoke a restart. The goal of these choices is to minimize the solving time. The methods in which these heuristics and techniques process the data generally do not have theoretical underpinnings. Consequently, understanding why these heuristics and techniques perform so well in practice remains a challenge and systematically improving them is rather difficult. This goes to the heart of this thesis, that is to utilize machine learning to process the data as part of an optimization problem to minimize solving time. Research in machine learning exploded over the past decade due to its success in extracting useful information out of large volumes of data. Machine learning outclasses manual handcoding in a wide variety of complex tasks where data are plentiful. This is also the case in modern SAT solvers where propagations, conflict analysis, and clause learning produces plentiful of data to be analyzed, and exploiting this data to the fullest is naturally where machine learning comes in. Many machine learning techniques have a theoretical basis that makes them easy to analyze and understand why they perform well. The branching heuristic is the first target for injecting machine learning. First we studied extant branching heuristics to understand what makes a branching heuristics good empirically. The fundamental observation is that good branching heuristics cause lots of clause learning by triggering conflicts as quickly as possible. This suggests that variables that cause conflicts are a valuable source of data. Another important observation is that the state-of-the-art VSIDS branching heuristic internally implements an exponential moving average. This highlights the importance of accounting for the temporal nature of the data when deciding to branch. These observations led to our proposal of a series of machine learning-based branching heuristics with the common goal of selecting the branching variables to increase probability of inducing conflicts. These branching heuristics are shown empirically to either be on par or outcompete the current state-of-the art. The second area of interest for machine learning is the restart policy. Just like in the branching heuristic work, we first study restarts to observe why they are effective in practice. The important observation here is that restarts shrink the assignment stack as conjectured by other researchers. We show that this leads to better clause learning by lowering the LBD of learnt clauses. Machine learning is used to predict the LBD of the next clause, and a restart is triggered when the LBD is excessively high. This policy is shown to be on par with state-of-the-art. The success of incorporating machine learning into branching and restarts goes to show that machine learning has an important role in the future of heuristic and technique design for SAT solvers
 
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Model driven engineering languages and systems : 11th international conference, MoDELS 2008, Toulouse, France, September 28 - October 3, 2008 : proceedings
Covers
Generative and component-based software engineering : first international symposium, GCSE'99, Erfurt, Germany, September 28-30, 1999 : revised papersSoftware language engineering : 5th international conference, SLE 2012, Dresden, Germany, September 26-28, 2012 ; revised selected papersGenerative programming : methods, tools, and applicationsModel-driven software development : technology, engineering, management
Alternative Names
Czarnecki, Krzystof

Czarnecki, Krzysztof

チャルネッキ, クシシュトフ

Languages
English (108)

German (2)