Publications

@inproceedings{MannM17,
author = {Zolt{\'{a}}n {\'{A}}d{\'{a}}m Mann and
Andreas Metzger},
title = {Optimized Cloud Deployment of Multi-tenant Software Considering Data Protection Concerns},
booktitle = {{IEEE/ACM} 17th International Symposium on Cluster, Cloud and Grid
Computing, CCGrid 2017, Madrid, Spain, 2017},
publisher = {{IEEE} Computer Society},
year = {2017},
}

@Inproceedings{cau34412,
Title = {Challenges in the Evolution of Metamodels: Smells and Anti-Patterns of a Historically-Grown Metamodel},
Author = {Misha Strittmatter and Georg Hinkel and Michael Langhammer and Reiner Jung and Robert Heinrich},
Booktitle = {Proceedings of the 10th Workshop on Models and Evolution},
Editor = {Tanja Mayerhofer and Alfonso Pierantonio and Bernhard Sch{\"a}tz and Dalila Tamzalit},
Year = {2016},
Month = {October},
Pages = {30--39},
Publisher = {{CEUR}},
Url = {http://eprints.uni-kiel.de/34412/},
Volume = {1706},
Abstract = {In model-driven engineering, modeling languages are developed to serve as basis for system design, simulation and code generation. Like any software artifact, modeling languages evolve over time. If, however, the metamodel that
defines the language is badly designed, the effort needed for its maintenance is unnecessarily increased. In this paper, we present bad smells and anti-patterns that we discovered in a thorough metamodel review of the Palladio Component Model (PCM). The PCM is a good representative for big and old metamodels that have grown over time. Thus, these results are meaningful, as they reflect the types of smells that accumulate in such metamodels over time. Related work deals mainly with automatically detectable bad smells, anti-patterns and defects. However, there are smells and anti-patterns, which cannot be detected automatically. They should not be neglected. Thus, in this paper, we focus on both: automatically and non-automatically detectable
smells.},
Keywords = {Metamodel
Metamodelling
Metamodel Semantics
Metamodel Smells}
}

@incollection{heinrich2016f,
title = {Performance Modeling in the Age of Big Data: Some Reflections on Current Limitations},
author = {Robert Heinrich and Holger Eichelberger and Klaus Schmid},
booktitle = {3rd International Workshop on Interplay of Model-Driven and Component-Based Software Engineering},
venue = {Saint Malo, France},
publisher = {CEUR Vol-1723},
ur = {http://ceur-ws.org/Vol-1723},
pd = {http://ceur-ws.org/Vol-1723/6.pdf},
year = {2016},
month = {October},
tags = {refereed}
}

@Phdthesis{cau33602,
Title = {Generator-Composition for Aspect-Oriented Domain-Specific Languages},
Author = {Reiner Jung},
Year = {2016},
Month = {August},
Url = {http://eprints.uni-kiel.de/33602/},
Abstract = {Software systems are complex, as they must cover a diverse set of requirements describing functionality and the environment. Software engineering addresses this complexity with Model-Driven Engineering (MDE). MDE utilizes different models and metamodels to specify views and aspects of a software system. Subsequently, these models must be transformed into code and other artifacts, which is performed by generators.
Information systems and embedded systems are often used over decades. Over time, they must be modified and extended to fulfill new and changed requirements. These alterations can be triggered by the modeling domain and by technology changes in both the platform and programming languages. In MDE these alterations result in changes of syntax and semantics of metamodels, and subsequently of generator implementations.
In MDE, generators can become complex software applications. Their complexity depends on the semantics of source and target metamodels, and the number of involved metamodels. Changes to metamodels and their
semantics require generator modifications and can cause architecture and code degradation. This can result in errors in the generator, which have a negative effect on development costs and time. Furthermore, these errors
can reduce quality and increase costs in projects utilizing the generator.
Therefore, we propose the generator construction and evolution approach {GECO}, which supports decoupling of generator components and their modularization. {GECO} comprises three contributions: (a) a method for metamodel partitioning into views, aspects, and base models together with partitioning along semantic boundaries, (b) a generator composition approach utilizing megamodel patterns for generator fragments, which are generators depending on only one source and one target metamodel, (c) an approach to modularize fragments along metamodel semantics and fragment functionality. All three contributions together support modularization and evolvability of generators.},
Keywords = {Model Transformation
Aspect-Oriented DSL
Megamodel Pattern
Multi-View Modeling
Generator Construction
},
Number = {2016/4},
Publisher = {Department of Computer Science, Kiel University},
Institution = {Faculty of Engineering, Kiel University},
Series = {Kiel Computer Science Series}
}

@Inproceedings{cau33286,
Title = {{GECO}: A Generator Composition Approach for Aspect-Oriented DSLs},
Author = {Reiner Jung and Robert Heinrich and Wilhelm Hasselbring},
Booktitle = {Theory and Practice of Model Transformations},
Year = {2016},
Month = {July},
Pages = {141--156},
Publisher = {Springer International Publishing},
Series = {Lecture Notes in Computer Science},
Url = {http://eprints.uni-kiel.de/33286/},
Volume = {9765},
Abstract = {Code and model generators that are employed in model-driven engineering usually face challenges caused by complexity and tight coupling of generator implementations, particularly when multiple metamodels are involved. As a consequence maintenance, evolution and reuse of generators is expensive and error-prone.
We address these challenges with a two fold approach for generator composition, called {GECO}, which subdivides generators in fragments and modules. (1) fragments are combined utilizing megamodel patterns. These patterns are based on the relationship between base and aspect metamodel, and define that each fragment relates only to one source and target metamodel. (2) fragments are modularized along transformation aspects, such as model navigation, and metamodel semantics.
We evaluate our approach with two case studies from different domains. The obtained generators are assessed with modularity and complexity metrics, covering architecture and method level. Our results show that the generator modularity is preserved during evolution utilizing {GECO}.},
Keywords = {Generator Composition}
}

@Inproceedings{SEAMS16,
Title = {Learning and Evolution in Dynamic Software Product Lines},
Author = {Amir {Molzam Sharifloo} and Andreas Metzger and Clement Quinton and
Luciano Baresi and Klaus Pohl},
Booktitle = {11th Int'l Symposium on Software Engineering for Adaptive and Self-Managing Systems ({SEAMS}), May 2016, Austin, Texas, USA},
Editor = {Carlo Ghezzi and Sam Malek},
Year = {2016},
Publisher = {{ACM}}
}

@article{heinrich2016b,
author = {Heinrich, Robert},
journal = {ACM SIGMETRICS Performance Evaluation Review},
title = {Architectural Run-time Models for Performance and Privacy Analysis in Dynamic Cloud Applications},
issue_date = {March 2016},
volume = {43},
number = {4},
year = {2016},
issn = {0163-5999},
pages = {13--22},
numpages = {10},
ur = {http://doi.acm.org/10.1145/2897356.2897359},
do = {10.1145/2897356.2897359},
acmid = {2897359},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {Architectural Run-time Model, Palladio Component Model, Performance Model, Privacy, Usage Profile}
}

@Article{cau32894,
Title = {3rd Collaborative Workshop on Evolution and Maintenance of Long-Living Software Systems ({EMLS'16})},
Author = {Robert Heinrich and Reiner Jung and Marco Konersmann and Eric Schmieders},
Year = {2016},
Month = {February},
Number = {1},
Pages = {2--3},
Url = {http://eprints.uni-kiel.de/32894/},
Volume = {36},
Journal = {Softwaretechnik-Trends},
Keywords = {Software Evolution}
}

@Inproceedings{cau31015,
Title = {{iObserve} 2: Integrated Observation and Modeling Techniques to Support Adaptation and Evolution of Software Systems},
Author = {Wilhelm Hasselbring and Robert Heinrich and Reiner Jung and Andreas Metzger and Klaus Pohl and Ralf Reussner and Eric Schmieders},
Booktitle = {Renewal Kickoff Workshop of the DFG Priority Programme 1593},
Year = {2016},
Month = {January},
Url = {http://eprints.uni-kiel.de/31015/},
Keywords = {Observation, Modeling, Adaptation, Evolution, Software Systems}
}

@Inproceedings{cau34061,
Title = {A Tool for Entropy-based Analysis of Design-time and Runtime Models},
Author = {Reiner Jung and Robert Heinrich and Wilhelm Hasselbring},
Booktitle = {Symposium on Software Performance 2015: Joint Developer and Community Meeting of Descartes/Kieker/Palladio},
Year = {2015},
Month = {November},
Url = {http://eprints.uni-kiel.de/34061/},
Abstract = {Software development and operation comprise a wide range of different artifacts, including architecture models, adaptation plans, and source code.
The complexity of any artifact has an impact on the evolution and adaptation of software systems.
Higher complexity and coupling affects the adaptability and evolvability of software systems and can have an impact on various quality attributes, including performance.
Therefore, analysis metrics have been developed to assess these qualities.
However, they are largely based on counting elements, e.g., lines of code, ignoring the interrelation of elements.
Therefore, they do not provide a precise assessment.
On this poster, we present an extensible analysis tool and a metric founded on information theory which allows to take the interrelation of elements and artifacts into account.
},
Keywords = {Information Theory
Entropy
Complexity Metric
Cohesion Metric
Size Metric}
}

@Inproceedings{cau29742,
Title = {Architectural Run-Time Models for Operator-in-the-Loop Adaptation of Cloud Applications},
Author = {Robert Heinrich and Reiner Jung and Eric Schmieders and Andreas Metzger and Wilhelm Hasselbring and Ralf Reussner and Klaus Pohl},
Booktitle = {Proceedings of the 9th {IEEE} Symposium on the Maintenance and Evolution of Service-Oriented Systems and Cloud-Based Environments ({MESOCA 2015})},
Year = {2015},
Month = {September},
Pages = {36--40},
Publisher = {{IEEE} Computer Society},
Url = {http://eprints.uni-kiel.de/29742/},
Abstract = {Building software systems by composing third-party cloud services promises many benefits. However, the increased complexity, heterogeneity, and limited observability of cloud services brings fully automatic adaption to its limits. We pro-pose architectural run-time models as a means for combining automatic and operator-in-the-loop adaptations of cloud services.},
Keywords = {Run-Time Models, Operator-in-the-Loop Adaptation, Cloud Applications}
}

@Inproceedings{cau28033,
Title = {{iObserve}: Integrated Observation and Modeling
Techniques to Support Adaptation and
Evolution of Software Systems},
Author = {Reiner Jung and Robert Heinrich and Eric Schmieders and Wilhelm Hasselbring and Andreas Metzger and Klaus Pohl and Ralf Reussner},
Booktitle = {5th Workshop of the DFG Priority Programme 1593},
Year = {2015},
Month = {March},
Url = {http://eprints.uni-kiel.de/28033/},
Abstract = {Long-living software systems face changes in thier requirements and their environment. Furthermore, they face variations in their utilization and how users interact with software systems. Changing requirements can lead to architecture and code erosion, while utilization changes require adaptations to the deployment and its configuration. The {iObserve} approach addresses both areas by integrating adaptation and evolution in one interlinked process. It provides a model-driven approach covering design-time and run-time, where the design-time application model is reused as run-time model and adapted according to run-time observations. The poste shows the overall approach based on a mega model and special approaches for model-driven instrumentation, monitoring, and analysis, especially of performance and privacy.},
Keywords = {Model-driven monitoring
Mega model
Run-time Models
Design-time Models
software Adaptation
Software Evolution}
}

@Inproceedings{cau26081,
Title = {Evolution of the Palladio Component Model: Process and Modeling Methods},
Author = {Reiner Jung and Misha Strittmatter and Philipp Merkle},
Booktitle = {Symposium on Software Performance (SOSP 2014)},
Year = {2014},
Month = {November},
Url = {http://eprints.uni-kiel.de/26081/},
Abstract = {{The Palladio Component Model (PCM) is nowadays used widely within the performance community. However, its present state hinders the introduction of new features, the combination or use only of a subset of PCM features, and the combination of PCM with third party technologies. Therefore, the PCM must be modularized to support reuse and extendablity.
As a solution to the current problem we present an anlysis of the current state, a modularization approach and an evolution plan so the modifications can be done over time.}},
Keywords = {meta-model evolution
meta-modeling
co-evolution}
}

@Inproceedings{cau27380,
Title = {Integrated Observation and Modeling Techniques to Support Adaptation and Evolution of Software Systems},
Author = {Robert Heinrich and Reiner Jung and Eric Schmieders and Andreas Metzger and Wilhelm Hasselbring and Klaus Pohl and Ralf Reussner},
Booktitle = {DFG Priority Program SPP1593, 4th Workshop},
Year = {2014},
Month = {November},
Url = {http://eprints.uni-kiel.de/27380/},
Abstract = {iObserve is an approach to integrate model-driven monitoring with design time models of software systems and reuse those models at run time to realize analyses based on the design time model. It is assumed that this reduces the effort to be made to interpret analysis results of a software system.}
}

@Inproceedings{cau25829,
Title = {Integrating Run-Time Observations and Design Component Models for Cloud System Analysis},
Author = {Robert Heinrich and Eric Schmieders and Reiner Jung and Kiana Rostami and Andreas Metzger and Wilhelm Hasselbring and Ralf Reussner and Klaus Pohl},
Booktitle = {Proceedings of the 9th Workshop on {Models@run.time}},
Year = {2014},
Month = {September},
Pages = {41--46},
Publisher = {{CEUR}},
Series = {Workshop Proceedings},
Url = {http://eprints.uni-kiel.de/25829/},
Volume = {1270},
Abstract = {Run-time models have been proven beneficial in the past for predicting upcoming quality flaws in cloud applications. Observation approaches relate measurements to executed code whereas prediction models oriented towards design components are commonly applied to reflect reconfigurations in the cloud. Levels of abstraction differ between code observations and these prediction models. In this position paper, we address the specification of causal relations between observation data and a component-based run-time prediction model. We introduce a meta-model for observation data, based on which we propose a mapping language to (a) bridge divergent levels of abstraction and (b) trigger model updates.},
Keywords = {Run-Time Observations, Design Component Models, Cloud Systems}
}

@Inproceedings{cau26082,
Title = {{GECO}: Generator Composition for Aspect-Oriented Generators},
Author = {Reiner Jung},
Booktitle = {Doctoral Symposium - Models 2014},
Year = {2014},
Month = {September},
Url = {http://eprints.uni-kiel.de/26082/},
Abstract = {Increasing size and complexity of software projects have triggered the use of domain-specific languages (DSL). Multiple DSLs, some with cross-cutting concerns, are used to describe software systems. In context of long-living software systems, requirements change over time causing an evolution of domains and subsequently the corresponding DSLs. Transformations are used to generate models and code from these DSLs combining information from different cross-cutting concerns. Due to the changes, the development and evolution of these generators become cumbersome and error-prone. The proposed {GECO} approach addresses this issue by introducing guidelines and tooling to ease generator composition and evolution. Furthermore, it allows parts of code generators to be developed and evolved separately reducing the overall complexity of code generation. In addition {GECO} fosters the reuse of DSLs and their generators in different projects. },
Keywords = {model transformations
mega model
meta model
generator composition
generator construction}
}

@Inproceedings{cau25295,
Title = {A Method for Aspect-oriented Meta-Model Evolution},
Author = {Reiner Jung and Robert Heinrich and Eric Schmieders and Strittmatter Misha and Wilhelm Hasselbring},
Booktitle = {Proceedings of the 2Nd Workshop on View-Based, Aspect-Oriented and Orthographic Software Modelling},
Year = {2014},
Month = {July},
Pages = {19--22},
Publisher = {{ACM}},
Series = {{VAO'14}},
Url = {http://eprints.uni-kiel.de/25295/},
Abstract = {Long-living systems face many modifications and extensions over time due to changing technology and requirements. This causes changes in the models reflecting the systems, and subsequently in the underlying meta-models, as their structure and semantics are adapted to adhere these changes. Modifying meta-models requires adaptations in all tools realizing their semantics. This is a costly endeavor, especially for complex meta-models.
To solve this problem we propose a method to construct and refactor meta-models to be concise and focused on a small set of concerns. The method results in simpler metamodel modification scenarios and fewer modifications, as new concerns and aspects are encapsulated in separate meta-models. Furthermore, we define design patterns based on the different roles meta-models play in software. Thus, we keep large and complex modeling projects manageable due to the improved adaptability of their meta-model basis.},
Journal = {Proceedings of the 2nd Workshop on View-Based, Aspect-Oriented and Orthographic Software Modelling},
Keywords = {Design Pattern
Aspect Modeling Evolution
Meta-Model Extension}
}

@Inproceedings{cau22655,
Title = {Model-driven Instrumentation with Kieker and Palladio to forecast Dynamic Applications},
Author = {Reiner Jung and Robert Heinrich and Eric Schmieders},
Booktitle = {Proceedings Symposium on Software Performance: Joint Kieker/Palladio Days 2013 (KPDAYS 2013)},
Year = {2013},
Month = {November},
Pages = {99--108},
Publisher = {{CEUR}},
Series = {{CEUR} Workshop Proceedings},
Url = {http://eprints.uni-kiel.de/22655/},
Volume = {1083},
Abstract = {Providing applications in stipulated qualities is a challenging task in today's
cloud environments. The dynamic nature of the cloud requires special runtime models
that reflect changes in the application structure and their deployment. These runtime
models are used to forecast the application performance in order to carry out mitiga-
tive actions proactively. Current runtime models do not evolve with the application
structure and quickly become outdated. Further, they do not support the derivation of
probing information that is required to gather the data for evolving the runtime model.
In this paper, we present the initial results of our research on a forecasting approach
that combines Kieker and Palladio in order to forecast the application performance
based on a dynamic runtime model. To be specific, we present two instrumentation
languages to specify Kieker monitoring probes based on structural information of the
application specified in Palladio component models. Moreover, we sketch a concept
to forward the monitored data to our PCM-based runtime model. This will empower
Palladio to carry out performance forecasts of applications deployed in dynamic envi-
ronments, which is to be tackled in future research steps.},
Journal = {Symposium on Software Performance: Joint Kieker/Palladio Days 2013},
Keywords = {model-driven instrumentation
domain-specific language
meta-modeling}
}

@Inproceedings{cau19435,
Title = {{iObserve}: Integrated Observation and Modeling Techniques to Support Adaptation and Evolution of Software Systems},
Author = {Wilhelm Hasselbring},
Booktitle = {KoSSE-Symposium Application Performance Management (Kieker Days 2012)},
Year = {2012},
Month = {October},
Url = {http://eprints.uni-kiel.de/19435/},
Abstract = {The new DFG Priority Program (SPP1593) on ?Design For Future ? Managed Software Evolution? has been established to develop fundamentally new approaches in software engineering with a determined focus on long-living software systems.
The increased adoption of service-oriented technologies and cloud computing creates new challenges for the adaptation and evolution of long-living software systems. Software services and cloud platforms are owned and maintained by independent parties. Software engineers and system operators of long-living software systems only have limited visibility and control over those third-party elements. Traditional monitoring provides software engineers and system operators with execution observation data which are used as basis to detect anomalies. If the services and the cloud platform are not owned and controlled by the engineers of the software systems, monitoring the execution of the software system is not straightforward.
As part of the DFG Priority Program, the project {iObserve} will develop and validate new advanced techniques which empower the system engineers to observe and detect anomalies of the execution of software systems they do not fully own and control. It will extend and integrate previous work on adaptive monitoring, online testing and benchmarking and will use models@runtime as means to adjust the observation and anomaly detection techniques during system operation.}
}

@Techreport{cau22077,
Title = {{iObserve}: Integrated Observation and Modeling Techniques to Support Adaptation and Evolution of Software Systems},
Author = {Wilhelm Hasselbring and Robert Heinrich and Reiner Jung and Andreas Metzger and Klaus Pohl and Ralf Reussner and Eric Schmieders},
Institution = {Christian-Albrechts-Universit{\"a}t Kiel},
Year = {2013},
Month = {October},
Type = {Research Report},
Url = {http://eprints.uni-kiel.de/22077/},
Abstract = {The goal of {iObserve} is to develop methods and tools to support evolution and adaptation of long-lived software systems. Future long-living software systems will be engineered using third-party software services and infrastructures. Key challenges for such systems will be caused by dynamic changes of deployment options on cloud platforms. Third-party services and infrastructures are neither owned nor controlled by the users and developers of service-based systems. System users and developers are thus only able to observe third-party services and infrastructures via their interface, but are not able to look into the software and infrastructure that provides those services.
In this technical report, we summarize our results of four activities to realize a complete tooling around Kieker, Palladio, and MAMBA, supporting performance and cost prediction, and the evaluation of data privacy in context of geo-locations. Furthermore, the report illustrates our efforts to extend Palladio.},
Address = {Kiel, Germany},
Keywords = {instrumentation, performance prediction, geo-location, data privacy policy, domain specific languages },
Publisher = {Department of Computer Science},
Series = {Technical Report}
}