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Composing multiple variability artifacts to assemble coherent workflows

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Abstract

The development of scientific workflows is evolving toward the systematic use of service-oriented architectures, enabling the composition of dedicated and highly parameterized software services into processing pipelines. Building consistent workflows then becomes a cumbersome and error-prone activity as users cannot manage such large-scale variability. This paper presents a rigorous and tooled approach in which techniques from Software Product Line (SPL) engineering are reused and extended to manage variability in service and workflow descriptions. Composition can be facilitated while ensuring consistency. Services are organized in a rich catalog which is organized as a SPL and structured according to the common and variable concerns captured for all services. By relying on sound merging techniques on the feature models that make up the catalog, reasoning about the compatibility between connected services is made possible. Moreover, an entire workflow is then seen as a multiple SPL (i.e., a composition of several SPLs). When services are configured within, the propagation of variability choices is then automated with appropriate techniques and the user is assisted in obtaining a consistent workflow. The approach proposed is completely supported by a combination of dedicated tools and languages. Illustrations and experimental validations are provided using medical imaging pipelines, which are representative of current scientific workflows in many domains.

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Notes

  1. In this paper, we consider only FMs in their basic form (Czarnecki et al. 2006). We do not consider other notations nor richer formalisms (e.g., cardinality-based FMs Czarnecki et al. 2005).

  2. In the literature (Segura et al. 2008; Acher et al. 2009), there exists another union mode, which is less restrictive in terms of sets of configurations expected from the resulting FM than the strict union mode defined in this paper.

  3. A core feature is a feature that appears in every configuration of an FM.

  4. The two features Analyze have the same name but are different entities. To avoid ambiguity, we use a qualified feature name including the root feature when needs be (e.g., to distinguish the Analyze feature of MIMoving from the Analyze feature of MIFixed . In this specific case, the merge operator described in Sect. 3.3 makes internally the distinction such that MIMoving.Analyze does not match with MIMoving.Analyze .

  5. Some alignment issues may occur when merging two FMs. For example, a naive aggregation of FMs can lead to an aggregated FM without the structuring feature MIInput , and thus disturbs the merging process. We provide to the user the ability to specify some pre-directives before merging FMs. The FM alignment problem is more general and further discussed in Sect. 6.

  6. It may be for practical reasons (e.g., convention) or for better characterizing the high-level concept for which the FM applies. In the example, rather than always using Medical Image , users prefer to be more precise for describing the kind of medical image associated to an FM. This issue is an instance of the FM alignment problem which is further discussed in Sect. 6

  7. The size of scientific workflows varies depending on the domain (e.g., bioinformatics, medical imaging). In the medical imaging domain, the presence of 24 services can be considered as a large workflow, even though larger workflows have been developed.

  8. The number of initial configurations is computed by considering FMs without inter-/compatibility constraints.

  9. Two features are distinct if their names are distinct.

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  1. I3S Laboratory (CNRS-UNSA), Les Algorithmes, Bât Euclide B, 2000 route des Lucioles, B.P. 121, 06903, Sophia Antipolis Cedex, France

    Mathieu Acher, Philippe Collet, Alban Gaignard, Philippe Lahire & Johan Montagnat

  2. Computer Science Department, Colorado State University, Fort Collins, CO, USA

    Robert B. France

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  1. Mathieu Acher
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Acher, M., Collet, P., Gaignard, A. et al. Composing multiple variability artifacts to assemble coherent workflows. Software Qual J 20, 689–734 (2012). https://doi.org/10.1007/s11219-011-9170-7

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