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Modeling variability in the video domain: language and experience report

Abstract

In an industrial project, we addressed the challenge of developing a software-based video generator such that consumers and providers of video processing algorithms can benchmark them on a wide range of video variants. This article aims to report on our positive experience in modeling, controlling, and implementing software variability in the video domain. We describe how we have designed and developed a variability modeling language, called VM, resulting from the close collaboration with industrial partners during 2 years. We expose the specific requirements and advanced variability constructs; we developed and used to characterize and derive variations of video sequences. The results of our experiments and industrial experience show that our solution is effective to model complex variability information and supports the synthesis of hundreds of realistic video variants. From the software language perspective, we learned that basic variability mechanisms are useful but not enough; attributes and multi-features are of prior importance; meta-information and specific constructs are relevant for scalable and purposeful reasoning over variability models. From the video domain and software perspective, we report on the practical benefits of a variability approach. With more automation and control, practitioners can now envision benchmarking video algorithms over large, diverse, controlled, yet realistic datasets (videos that mimic real recorded videos)—something impossible at the beginning of the project.

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Notes

  1. VM stands for Variability Modeling. In essence, VM is a variability language and shares many properties of e.g., feature modeling languages. We chose the acronym VM since it may also stand for Video Modeling, hence the word play.

  2. t-wise methods are widely used in combinatorial testing. Concretely, t-wise methods reduce the number of tests to execute while covering the interactions of t input parameters in a system. Also, the most common value for t is two (pair-wise), which was demonstrated to be valid in a wide range of scenarios (Cohen et al. 2006a)

  3. Some of the VM constructs are actually present in other variability languages while others are additional constructs or adaptations for modeling variability. Section 6.4 compares VM with other variability languages.

  4. https://github.com/ViViD-DiverSE/VM-Source

  5. The cardinalities apply here to a feature, whereas a cardinality-based group (see previous section) applies to a feature group.

  6. See e.g., Section 2.1 of the Choco Solver documentation available here: http://www.lirmm.fr/lazaar/imagina/choco-doc.pdf (August 2012)

  7. The classification of Savolainen et al. (2011) helps to “clarify the intent of a proposed method and [...] help practitioners in clarifying the guiding principles for their feature modeling”. Therefore, it is not a comparison framework; we use it as a means to properly report on our experience. A cross-validation with other empirical studies is an interesting research direction, but out of the scope of this article.

  8. http://www.eclipse.org/Xtext/

  9. Each attribute has a domain size, which corresponds to the number of individual values an attribute can take. We consider deltas (see Section 5.3, page 12) for the computation of domain size. It should be noted that the number of possible configurations is significantly greater than the sum of possible individual values—since a configuration is a combination of individual attribute values.

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Acknowledgements

This work was financed by the project MOTIV of the Direction Générale de l’Armement (DGA) - Ministére de la Défense, France. Also, by the European Commission (FEDER) and by the Spanish government under BELi (TIN2015-70560-R) project. We thank all participants of the project. Special thanks to Pierre Romenteau from InPixal (Rennes, France) for his continuous feedbacks and his joint development for synthesizing video variants.

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Alférez, M., Acher, M., Galindo, J.A. et al. Modeling variability in the video domain: language and experience report. Software Qual J 27, 307–347 (2019). https://doi.org/10.1007/s11219-017-9400-8

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Keywords

  • Variability modeling
  • Feature modeling
  • Software product line engineering
  • Configuration
  • Automated reasoning
  • Domain-specific languages
  • Video testing