martes, 10 de febrero de 2009

Tooling Business Family Engineering

Nowadays, we have developed an automated tool support for obtaining the basic structure of a BPMN derived from the commonalities summary into the Business Family Domain Engineering phase. This basic structure is the Core Process Framework. This mapping is based on Automata Theory and Formal Languages, and it has been implemented by means of MDD transformations.

For that purpose, we have performed a transformation between the FeAture Model Analyzer (FAMA) metamodel as source and the Eclipse SOA Tool Platform2 BPMN metamodel as target metamodel using the Atlas Transformation Language (ATL).

Figure presents and screenshot of this tool. It has been published on Eclipse ATL website. ATL code and specification is available in

The transformation was introduced at the following paper:

From Feature Models to Business Processes: I. Montero, J. Peña, A. Ruiz-Cortés. IEEE International Conference on Services Computing (SSC). 2605–608. Honolulu, HI. 2008.

Abstract: The variability level of average-size Business Information Systems (BIS) is highly enough for making the design of this kind of systems a complex task. There is an approach called Process Family Engineering (PFE) that tries to ease the design of BIS using ideas from the Software Product Lines (SPL) field. Roughly speaking, they propose to, first, study the variability of the system without entering into details by means of building a variability model (called feature model), that is used later for building the business process. However, in PFE the process of deriving the business process from the feature model is performed manually.
Authors use feature models with a different meaning that is commonly accepted in SPL. In this paper, we provide a rigorous description for the new meaning of feature models, and a mapping relationship that defines how to use the information in the FM for obtaining the basic structure of the business process. In addition, as a proof of concepts, we have implemented an MDD transformation that provides the expected results.

Quality Levels: Acceptance Rate: 18%, IEEE Core: A

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