Pit Pietsch

Adaptability of model comparison tools

Modern model-based development methodologies require a large number of efficient, high-quality model comparison tools. They must be carefully adapted to the specific model type, user preferences and application context. Implementing a large number of dedicated, monolithic tools is infeasible, the only viable approach are generic, adaptable tools. Generic tools currently available provide only partial or low-quality solutions to this challenge; their results are not satisfactory for model types such as state machines or block diagrams. This paper presents the SiDiff approach to model comparison which includes a set of highly configurable incremental matchers and a specification language to control their application.

Comparison of BPMN2 Diagrams

Models are compared to identify which elements are unchanged and which were added, removed, or modified. This information is necessary for developers to understand which edit steps were applied between two revisions of a model, to discover differences in concurrently developed models and it is also a fundamental building block for advanced processing steps, e.g. model merging. Hence, model comparison is generally considered as a critical factor for the acceptance and success of model-driven development approaches. Surprisingly however, for many model types only inadequate tool support for comparing models is available. This paper presents the prototype of a similarity-based model comparison tool for BPMN2 diagrams. The algorithms and heuristics of the SiDiff model differencing framework have been configured to the specific characteristics of BPMN2 diagrams. An initial evaluation indicates that the presented prototype produces results of high quality.

Shape-based object retrieval by contour segment matching

In this paper we introduce an approach for object retrieval that uses contour segment matching for shape similarity computation. The object contour is partitioned into segments by skeleton endpoints. Each contour segment is represented by a rotation and scale invariant, 12-dimensional feature vector. The similarity of two objects is determined by matching their contour segments using the Hungarian algorithm. Our method is insensitive to object deformation and outperforms existing shape-based object retrieval algorithms. The most significant scientific contributions of this paper include (i) the introduction of a new feature extraction technique for contour segments as well as (ii) a new similarity measure for contour segments cleverly modelling the human perception and easily adapting to concrete application domains, and (iii) the impressive robustness of the method in an object retrieval scenario.

Generating realistic test models for model processing tools

Test models are needed to evaluate and benchmark algorithms and tools in model driven development. Most model generators randomly apply graph operations on graph representations of models. This approach leads to test models of poor quality. Some approaches do not guarantee the basic syntactic correctness of the created models. Even if so, it is almost impossible to guarantee, or even control, the creation of complex structures, e.g. a subgraph which implements an association between two classes. Such a subgraph consists of an association node, two association end nodes, and several edges, and is normally created by one user command. This paper presents the SiDiff Model Generator, which can generate models, or sets of models, which are syntactically correct, contain complex structures, and exhibit defined statistical characteristics.

Shape-based Object Retrieval and Classification with Supervised Optimisation

In order to enhance the performance of shape retrieval and classification, in this paper, we propose a novel shape descriptor with low computation complexity that can be easily fused with other meaningful descriptors like shape context, etc. This leads to a significant increase in descriptive power of original descriptors without adding to much computation complexity. To make the proposed shape descriptor more practical and general, a supervised optimisation strategy is introduced. The most significant scientific contributions of this paper includes the introduction of a new and simple feature descriptor with supervised optimisation strategy leading to the impressive improvement of the accuracy in object classification and retrieval scenario.

Synthesizing realistic test models

Tools and methods in the context of Model Driven Engineering (MDE) have to be evaluated and tested using appropriate models as test cases. Unfortunately, adequate test models are scarcely available in many application domains and have to be artificially created. In this regard, model generators have been proposed recently. Principally, they generate test models by modifying a base model using a specified set of edit operations. The modification process should be done in a way that the resulting test models are as “realistic” as possible, i.e. the applied changes should resemble the real evolution that one observes in real software systems at the abstraction level of models. Therefore, we have to (1) properly capture the evolution of real software models, (2) statistically model the evolution (changes) and (3) finally properly reproduce it in the generated test models. To this end, we reversed engineered all revisions of nine typical Java systems into their class diagrams (totally 6,559 distinct models). We compared the subsequent models using a state-of-the-art model differencing tool and we computed the changes between them in terms of applied edit operations. We investigated the fitness of 60 promising distributions on the observed frequencies of edit operations in order to statistically model the changes. Four of our candidate distributions were successful to statistically model the changes with very good success rates. Since it was not known how to implement them, i.e. produce their random variates, we developed a practical implementation. The implemented distributions are then used to reproduce the real evolution of software systems in order to synthesize more realistic test models for MDE tools.

Analysis and Prediction of Design Model Evolution Using Time Series

Tools which support Model-Driven Engineering have to be evaluated and tested. In the domain of model differencing and model versioning, sequences of software models (model histories), in which a model is obtained from its immediate predecessor by some modification, are of special interest. Unfortunately, in this application domain adequate real test models are scarcely available and must be artificially created. To this end, model generators were proposed in recent years. Generally, such model generators should be configured in a way that the generated sequences of models are as realistic as possible, i.e. they should mimic the changes that happen in real software models. Hence, it is a necessary prerequisite to analyze and to stochastically model the evolution (changes) of real software systems at the abstraction level of models. In this paper, we present a new approach to statistically analyze the evolution of models. Our approach uses time series as a statistical method to capture the dynamics of the evolution. We applied this approach to several typical projects and we successfully modeled their evolutions. The time series models could predict the future changes of the next revisions of the systems with good accuracies. The obtained time series models are used to create more realistic model histories for model versioning and model differencing tools.

Assessing the Quality of Model Differencing Engines

In recent years many tools and algorithms for model comparison and differencing were proposed. Typically, the main focus of the research laid on being able to compute the difference in the first place. Only very few papers addressed the quality of the delivered differences sufficiently. Hence, this is a general shortcoming in the state-of-the-art. Currently, there are no established community standards how to assess the quality of differences and it is neither possible to compare the quality of different algorithms, nor can developers decide whether or not an algorithm is able to produce adequate results in a given application scenario. We propose a parallel working session to be held to discuss this general problem and its implications. The goal of the working session is to achieve a common understanding of what the crucial factors in assessing the quality of differences are. Furthermore, it is planed to discuss possible solutions that help the research community as whole, e.g. by drafting the design of an initial benchmark corpus which later could be turned into a standardized, openly available benchmark set.

Operation-based Model Differencing meets State-based Model Comparison

The computation of a difference between two models is the most basic function of model versioning tools. Two fundamental approaches to obtain model differences have been proposed: Operation-based differencing and state-based comparison. They are commonly regarded as disjoint and thus evolved mostly independent of each other. Recent advances in model comparison, which lift low-level differences to invocations of complex editing commands, brought both approaches closer together. The overall goal of the CVSM working session proposed in this paper is to analyse common challenges and to derive conceptual and tooling-related issues which offer the potential for future collaborations of researchers originating from working groups of both approaches.

Representation of model differences

Model-driven development requires a full set of development tools. While approaches and tools for constructing most aspects of model driven tool chains are readily available, there is a lack of tools and well evaluated approaches which compute and/or processes model differences. Difference tools for models must be adapted to different types of models, usecases and application contexts. So the tools and approaches must be realized every time completely anew according different algorithms and functions. As a possible solution, we propose a common difference representation and data exchange format as a foundation to integrate and evaluate individual activities. Thus we propose to analyze common and individual requirements and discuss issues of the state-ofthe-art difference representation. With this we can support future collaborations of researchers, integrate approaches, and enhance interoperability leading to more efficient research.