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Enhancing Feature Model Configuration and Validation Through a Demonstration-based Approach

This research, supported by the NSF CAREER award CCF-1052616, focuses on improving the editing environment for feature models. It addresses how to enable users to easily specify, save, validate, and recover configurations. Scenarios include saving/loading configurations, automatic constraint checking, and error correction. The proposed solution employs a model transformation by demonstration (MTBD), allowing users to infer and generate transformation patterns without needing to understand complex model transformation languages or metamodels, thus enhancing user knowledge exchange and collaboration in software product line (SPL) and model-driven engineering (MDE).

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Enhancing Feature Model Configuration and Validation Through a Demonstration-based Approach

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  1. Contact Profile (1/2) • Yu Sun, University of Alabama at Birmingham • Hyun Cho, University of Alabama • Jeff Gray, University of Alabama • Jules White, Virginia Tech • Model-Driven Engineering, Domain-Specific Modeling, Model Transformation • Feature Model Configuration and Validation This research is supported by NSF CAREER award CCF-1052616

  2. Contact Profile (2) • Objectives / Looking for • New ideas in feature model configuration and validation, successful case studies of SPL • Meeting new collaborators interested in SPL & MDE • Learning new ideas and techniques that influence my research interests, meeting people with similar research interests and building collaborative relationships <Yu Sun, Univ. of Alabama at Birmingham>

  3. Supporting Feature Model Configuration using a Demonstration-based Approach This research is supported by NSF CAREER award CCF-1052616

  4. The Problem <Yu Sun, Univ. of Alabama at Birmingham> In a feature model editing environment, how can we enable users to easily specify and reuse the knowledge related to feature model configuration and validation?

  5. The Problem – Scenario 1 A configuration for LCD32_37 A simplified TV feature model <Yu Sun, Univ. of Alabama at Birmingham> • Configuration Saving / Loading • Users need to specify and save different configurations, as well as reloading them

  6. The Problem – Scenario 2 An incorrect TV configuration <Yu Sun, Univ. of Alabama at Birmingham> • Automatic Constraint Checking • Users need to specify dependency rules and validate them automatically

  7. The Problem – Scenario 3 Fixing the incorrect TV configuration <Yu Sun, Univ. of Alabama at Birmingham> • Automatic Error Correction • Users need to recover erroneous configurations automatically

  8. Current Solution <Yu Sun, Univ. of Alabama at Birmingham> • Feature model configuration and validation can be considered as a model transformation process T • T = <P, A> • P: preconditions to satisfy • A: actions to carry out the transformation

  9. Solution: Model Transformation By Demonstration • A complete model transformation framework • Specify and execute model transformations • Users are fully isolated from MTLs and metamodel definitions • Infer and generate model transformation patterns by demonstrating the transformation of models on concrete examples Add, Remove, Update

  10. Video Demo

  11. Overview of MTBD

  12. Why is it interesting? • MTBD provides an end-user approach to enable users to specify and reuse the desired configuration and validation, improving knowledge exchange and sharing • Users do not need to know Model Transformation Languages or Metamodels • Users do not need to apply formal specifications such as CSP and SAT <Yu Sun, Univ. of Alabama at Birmingham>

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