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. When used in combination with existing adaptation patterns, change patterns for process families will enable the modeling and evolution of process families at a high-level of abstraction. Further, they will serve as reference for implementing tools or comparing proposals managing process families. © 2013 Springer-Verlag.This work has been developed with the support of MICINN under the Project EVERYWARE TIN2010-18011.Ayora Esteras, C.; Torres Bosch, MV.; Weber, B.; Reichert, M.; Pelechano Ferragud, V. (2013). Enhancing modeling and change support for process families through change patterns. En Enterprise, Business-Process and Information Systems Modeling, BPMDS 2013. Springer Verlag. 246-260. https://doi.org/10.1007/978-3-642-38484-4_18S246260van der Aalst, W.M.P., ter Hofstede, A.H.M., Barros, B.: Workflow Patterns. Distributed and Parallel Databases 14(1), 5–51 (2003)Aghakasiri, Z., Mirian-Hosseinabadi, S.H.: Workflow change patterns: Opportunities for extension and reuse. In: Proc. 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Comprehending Feature Models Expressed in CVL

Feature modeling is a common way to present and manage variability of software and systems. As a prerequisite for effective variability management is comprehensible representation, the main aim of this paper is to investigate difficulties in understanding feature models. In particular, we focus on the comprehensibility of feature models as expressed in Common Variability Language (CVL), which was recommended for adoption as a standard by the Architectural Board of the Object Management Group. Using an experimental approach with participants familiar and unfamiliar with feature modeling, we analyzed comprehensibility in terms of comprehension score, time spent to complete tasks, and perceived difficulty of different feature modeling constructs. The results showed that familiarity with feature modeling did not influence the comprehension of mandatory, optional, and alternative features, although unfamiliar modelers perceived these elements more difficult than familiar modelers. OR relations were perceived as difficult regardless of the familiarity level, while constraints were significantly better understood by familiar modelers. The time spent to complete tasks was higher for familiar modelers.acceptedVersio

Comparing the comprehensibility of requirements models expressed in Use Case and Tropos: Results from a family of experiments

[Context] Over the years, several modeling languages for requirements have been proposed. These languages employ different conceptual approaches, including scenario-based and goal-oriented ones. Empirical studies providing evidence about requirements model comprehensibility are rare, especially when addressing languages that belong to different modeling approaches. [Objective] This work aims to compare the comprehensibility of requirements models expressed in different but comparable modeling approaches from a requirements analysts’ perspective. In particular, in this paper we compare the comprehensibility of requirements models expressed in two visual modeling languages: Use Case, which is scenario-based, and Tropos, which exploits goal-oriented modeling. We further compare the effort required for comprehending the different models, and the derived productivity in each case. [Method] Requirements model comprehensibility is measured here in the context of three types of tasks that analysts usually perform, namely mapping between textual description and the model elements, reading and understanding the model irrespectively of the original textual description, and modifying the model. This experimental evaluation has been conducted within a family of controlled experiments aiming at comparing the comprehensibility of Use Case and Tropos requirements models. Three runs of the experiment were performed, including a first experiment and two replications, involving 79 subjects overall (all of which were information systems students). The data for each experiment was separately analyzed, followed by a meta-analysis of the three experiments. [Results] The experimental results show that Tropos models seem to be more comprehensible with respect to the three types of requirements analysis tasks, although more time consuming than Use Case models. Conclusions Measuring model comprehensibility by means of controlled experiments is feasible and provides a basis for comparing Tropos and Use Case models, although these languages belong to different modeling approaches. Specifically, Tropos outperformed Use Case in terms of comprehensibility, but required more effort leading to a similar productivity of the two languages