research-article

Variability representations in class models: an empirical assessment

Authors:

Daniel Strüber,

Anthony Anjorin,

Thorsten BergerAuthors Info & Claims

MODELS '20: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems

Pages 240 - 251

https://doi.org/10.1145/3365438.3410935

Published: 16 October 2020 Publication History

Abstract

Owing to the ever-growing need for customization, software systems often exist in many different variants. To avoid the need to maintain many different copies of the same model, developers of modeling languages and tools have recently started to provide representations for such variant-rich systems, notably variability mechanisms that support the implementation of differences between model variants. Available mechanisms either follow the annotative or the compositional paradigm, each of them having unique benefits and drawbacks. Language and tool designers select the used variability mechanism often solely based on intuition. A better empirical understanding of the comprehension of variability mechanisms would help them in improving support for effective modeling.

In this paper, we present an empirical assessment of annotative and compositional variability mechanisms for class models. We report and discuss findings from an experiment with 73 participants, in which we studied the impact of two selected variability mechanisms during model comprehension tasks. We find that, compared to the baseline of listing all model variants separately, the annotative technique did not affect developer performance. Use of the compositional mechanism correlated with impaired performance. For two out of three considered tasks, the annotative mechanism is preferred to the compositional one and the baseline. We present actionable recommendations concerning support of flexible, tasks-specific solutions, and the transfer of established best practices from the code domain to models.

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Toledo RAtlee JXiong RLiu MRoychoudhury APaiva AAbreu RStorey M(2024)(Neo4j)^ Browser: Visualizing Variable-Aware Analysis ResultsProceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings10.1145/3639478.3640046(69-73)Online publication date: 14-Apr-2024
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Toledo RAtlee JXiong R(2024)Visualizing Analysis Results for SPL Models - A User Study2024 IEEE Working Conference on Software Visualization (VISSOFT)10.1109/VISSOFT64034.2024.00018(83-95)Online publication date: 6-Oct-2024
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Mahmood WÇalıklı GStrüber DLämmel RMukelabai MBerger T(2024)Virtual Platform: Effective and Seamless Variability Management for Software SystemsIEEE Transactions on Software Engineering10.1109/TSE.2024.340622450:11(2753-2785)Online publication date: 1-Nov-2024
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Index Terms

Variability representations in class models: an empirical assessment
1. Software and its engineering
  1. Software creation and management
    1. Software development techniques
      1. Reusability
        Software product lines
  2. Software organization and properties
    1. Software system structures
      1. Software system models
        Model-driven software engineering

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cover image ACM Conferences

MODELS '20: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems

October 2020

406 pages

ISBN:9781450370196

DOI:10.1145/3365438

General Chairs:
Eugene Syriani
Université de Montréal, Canada
,
Houari Sahraoui
Université de Montréal, Canada

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https://dl.acm.org/doi/10.1145/3639478.3640046
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https://doi.org/10.1109/VISSOFT64034.2024.00018
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https://dl.acm.org/doi/10.1109/TSE.2024.3406224
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