A multi-level approach for supporting configurations: A new perspective on software product line engineering

Configuration is a common way in many markets to cope with reduc- ing costs and improving customer satisfaction. There are various approaches to represent product configurations, the most common of which is feature model- ing. However, feature models suffer from principal limitations, including ambi- guity and lack of abstraction, increasing maintainability effort and limiting lifecycle support. In this paper, we suggest using a multi-level modeling ap- proach to improve flexibility, reuse, and integrity and demonstrate the ad- vantages of the approach over feature modeling

Investigating styles in variability modeling: Hierarchical vs. constrained styles

Context: A common way to represent product lines is with variability modeling. Yet, there are different ways to extract and organize relevant characteristics of variability. Comprehensibility of these models and the ease of creating models are important for the efficiency of any variability management approach. Objective: The goal of this paper is to investigate the comprehensibility of two common styles to organize variability into models - hierarchical and constrained - where the dependencies between choices are specified either through the hierarchy of the model or as cross-cutting constraints, respectively. Method: We conducted a controlled experiment with a sample of 90 participants who were students with prior training in modeling. Each participant was provided with two variability models specified in Common Variability Language (CVL) and was asked to answer questions requiring interpretation of provided models. The models included 9 to 20 nodes and 8 to 19 edges and used the main variability elements. After answering the questions, the participants were asked to create a model based on a textual description. Results: The results indicate that the hierarchical modeling style was easier to comprehend from a subjective point of view, but there was also a significant interaction effect with the degree of dependency in the models, that influenced objective comprehension. With respect to model creation, we found that the use of a constrained modeling style resulted in higher correctness of variability models. Conclusions: Prior exposure to modeling style and the degree of dependency among elements in the model determine what modeling style a participant chose when creating the model from natural language descriptions. Participants tended to choose a hierarchical style for modeling situations with high dependency and a constrained style for situations with low dependency. Furthermore, the degree of dependency also influences the comprehension of the variability model

Data Impact Analysis in Business Processes

Business processes and their outcomes rely on data whose values are changed during process execution. When unexpected changes occur, e.g., due to last minute changes of circumstances, human errors, or corrections of detected errors in data values, this may have consequences for various parts of the process. This challenges the process participants to understand the full impact of the changes and decide on responses or corrective actions. To tackle this challenge, the paper suggests a semi-automated approach for data impact analysis. The approach entails a trans-formation of business process models to a relational database representation, to which querying is applied, in order to retrieve process elements that are related to a given data change. Specifically, the proposed method receives a data item (an attribute or an object) and information about the current state of process execution (in the form of a trace upon which an unexpected change has occurred). It analyzes the impact of the change in terms of activities, other data items, and gateways that are affected. When evaluating the usefulness of the approach through a case study, it was found that it has the potential to assist experienced process participants, especially when the consequences of the change are extensive, and its locus is in the middle of the process. The approach contributes both to practice with tool-supported guidance on how to handle unexpected data changes, and to research with a set of impact analysis primitives and queries

The Role of Modeling in the Analysis and Design of Sustainable Systems:A Panel Report

Sustainability should become a key concern in the next generation of engineered systems. While this expectation is relatively straightforward, the question of how to get there is less obvious. The multi-dimensional and intricate nature of sustainability poses challenges in designing sustainable systems and analyzing sustainability properties. Finding trade-offs between economic, environmental, societal, and technological aspects of sustainability is a wicked problem and calls for advanced modeling and simulation methods. In this paper, we report on a panel discussion held at the 28th Working Conference on Exploring Modeling Methods for Systems Analysis and Development (EMMSAD) with four esteemed experts representing four complementary and often conflicting perspectives on the role of modeling for sustainability – stakeholders, digitalization, degrowth and IT, and ethics. We report the key arguments of the panelists, discuss the roles of modeling in the analysis and design of sustainable systems, and finally, elaborate the conflicts among the perspectives, their effects, and potential resolutions

Supporting Data Collection in Complex Scenarios with Dynamic Data Collection Processes

Nowadays, companies have to report a large number of data sets (e.g., sustainability data) regarding their products to different legal authorities. However, in today's complex supply chains products are the outcome of the collaboration of many companies. To gather the needed data sets, companies have to employ cross-organizational and long-running data collection processes that imply great variability. To support such scenarios, we have designed a lightweight, automated approach for contextual process configuration. That approach can capture the contextual properties of the respective situations and, based on them, automatically configure a process instance accordingly, even without human involvement. Finally, we implemented our approach and started an industrial evaluation

Enhancing modeling and change support for process families through change patterns

The increasing adoption of process-aware information systems (PAISs), together with the variability of business processes (BPs), has resulted in large collections of related process model variants (i.e., process families). To effectively deal with process families, several proposals (e.g., C-EPC, Provop) exist that extend BP modeling languages with variability-specific constructs. While fostering reuse and reducing modeling efforts, respective constructs imply additional complexity and demand proper support for process designers when creating and modifying process families. Recently, generic and language independent adaptation patterns were successfully introduced for creating and evolving single BP models. However, they are not sufficient to cope with the specific needs for modeling and evolving process families. This paper suggests a complementary set of generic and language-independent change patterns specifically tailored to the needs of process families. 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Do Models Speak for Themselves? -- Experimental Material

This project contains the experimental materials used in our empirical study reported in the paper: Do Models Speak for Themselves? The Impact of Source Code (Un)Availability on Software Comprehensio