Comparison of BPMN2 Diagrams

Pietsch, Pit; Wenzel, Sven

doi:10.1007/978-3-642-33155-8_7

Cited by 14 publications

(5 citation statements)

References 21 publications

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“…Support for other model types, e.g. state machines [13] and process models [14], is currently in the focus of research. Still, we were able to identify a group of general matching problems where stateof-the-art model comparison tools deliver differences which are of low quality and sometimes are even unusable from the perspective of the model developer.…”

Section: Motivationmentioning

confidence: 99%

Model Matching Challenge: Benchmarks for Ecore and BPMN Diagrams

Pietsch¹,

Müller²,

Rumpe³

2013

Softwaretechnik-Trends

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In the last couple of years, Model Driven Engineering (MDE) gained a prominent role in the context of software engineering. In the MDE paradigm, models are considered first level artifacts which are iteratively developed by teams of programmers over a period of time. Because of this, dedicated tools for versioning and management of models are needed. A central functionality within this group of tools is model comparison and differencing.In two disjunct research projects, we identified a group of general matching problems where state-ofthe-art comparison algorithms delivered low quality results. In this article, we will present five edit operations which are the cause for these low quality results. The reasons why the algorithms fail, as well as possible solutions, are also discussed. These examples can be used as benchmarks by model developers to assess the quality and applicability of a model comparison tool for a given model type.

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Section: Motivationmentioning

confidence: 99%

Model Matching Challenge: Benchmarks for Ecore and BPMN Diagrams

Pietsch¹,

Müller²,

Rumpe³

2013

Softwaretechnik-Trends

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“…Diff viewers are well-researched and applied in this context, for example in [19,8]. Communicating process changes efficiently for teaching and enablement of employees.…”

Section: See Potential Impact Beforehand (Amount and Business-wise) A...mentioning

confidence: 99%

Reinforcement Learning-supported AB Testing of Business Process Improvements: An Industry Perspective

Kurz¹,

Kampik²,

Pufahl³

et al. 2023

Preprint

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In order to better facilitate the need for continuous business process improvement, the application of DevOps principles has been proposed. In particular, the AB-BPM methodology applies AB testing and reinforcement learning to increase the speed and quality of improvement efforts. In this paper, we provide an industry perspective on this approach, assessing requirements, risks, opportunities, and more aspects of the AB-BPM methodology and supporting tools. Our qualitative analysis combines grounded theory with a Delphi study, including semi-structured interviews and multiple follow-up surveys with a panel of ten business process management experts. The main findings indicate a need for human control during reinforcement learning-driven experiments, the importance of aligning the methodology culturally and organizationally with the respective setting, and the necessity of an integrated process execution platform.

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“…These assumptions are hard-wired into the code. Obviously, this leads to situations where such optimized algorithms perform well for certain model types, but fail for others [10].…”

Section: Motivationmentioning

confidence: 99%

“…These assumptions are hard-wired into the code. Obviously, this leads to situations where such optimized algorithms perform well for certain model types, but fail for others [10].• Adaptable differencing algorithms and engines do not determine properties of differences by themselves; in fact, the quality of the delivered differences depends strongly on the choice of the configuration options. Thus, the responsibility for the quality is passed on to the configurator of the engine, i.e.…”

mentioning

confidence: 99%

Assessing the Quality of Model Differencing Engines

Pietsch¹,

Yazdi²,

Kelter³

et al. 2012

Softwaretechnik-Trends

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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. MOTIVATIONMany different algorithms for model differencing were proposed in recent years; surveys can be found in [5,6,7]. Typically, the main emphasis of these approaches is focused on the algorithms used to compute the difference between two revisions of a model. The evaluation of the algorithms, if conducted at all, is usually based only on sets consisting of few, small and sometimes even specially created, test models. Obviously, such an evaluation is not enough to assess the quality of the algorithms objectively, nor is it possible to compare the quality of different algorithms this way. Hence, developers which must choose a model differencing engine in their day-to-day work can not make informed decisions which tool fits their needs best.The reasons why very few papers proposing model differencing algorithms address the quality of the delivered differences sufficiently are manifold:• Ultimately, the quality of a difference can only be assessed in the context of the use case in which the difference is used. If the difference between two models is to be displayed to developers then understandability and compactness [2] are highly relevant; in the context of merging, the avoidance of merge conflicts is important. If a difference is to be used for internal delta storage or for batch patching of models, none of the above properties are relevant and it is sufficient to simply save the smallest representation of the difference.• Properties such as quality and understandability are not generally defined and must be refined further for each specific model type and paradigm.• Test models where the evolution is sufficiently known and documented are not available for many domains. While model generators [8,9] can be used to create realistic test models synthetically, configuring these tool is time intensive and requires in-depth knowledge of edit processes for a given domain.• Some generic algorithms ma...

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Comparison of BPMN2 Diagrams

Cited by 14 publications

References 21 publications

Model Matching Challenge: Benchmarks for Ecore and BPMN Diagrams

Model Matching Challenge: Benchmarks for Ecore and BPMN Diagrams

Reinforcement Learning-supported AB Testing of Business Process Improvements: An Industry Perspective

Assessing the Quality of Model Differencing Engines

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