Learning from Difference: An Automated Approach for Learning Family Models from Software Product Lines

Learning from Difference: An Automated Approach for Learning Family Models from Software Product Lines. Damasceno, C. D. N., Mousavi, M. R., & Simao, A. In Proceedings of the 23rd International Systems and Software Product Line Conference - Volume A, of SPLC '19, pages 52–63, New York, NY, USA, 2019. Association for Computing Machinery.

Paper doi abstract bibtex

Substantial effort has been spent on extending specification notations and their associated reasoning techniques to software product lines (SPLs). Family-based analysis techniques operate on a single artifact, referred to as a family model, that is annotated with variability constraints. This modeling approach paves the way for efficient model-based testing and model checking for SPLs. Albeit reasonably efficient, the creation and maintenance of family models tend to be time consuming and error-prone, especially if there are crosscutting features. To tackle this issue, we introduce FFSMDiff, a fully automated technique to learn featured finite state machines (FFSM), a family-based formalism that unifies Mealy Machines from SPLs into a single representation. Our technique incorporates variability to compare and merge Mealy machines and annotate states and transitions with feature constraints. We evaluate our technique using 34 products derived from three different SPLs. Our results support the hypothesis that families of Mealy machines can be effectively merged into succinct FFSMs with fewer states, especially if there is high feature sharing among products. These indicate that FFSMDiff is an efficient family-based model learning technique.

@inproceedings{Damasceno:2019:LDA:3336294.3336307,
    author = {Damasceno, Carlos Diego Nascimento and Mousavi, Mohammad Reza and Simao, Adenilso},
    title = {Learning from Difference: An Automated Approach for Learning Family Models from Software Product Lines},
    year = {2019},
    isbn = {9781450371384},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3336294.3336307},
    doi = {10.1145/3336294.3336307},
    abstract = {Substantial effort has been spent on extending specification notations and their associated
    reasoning techniques to software product lines (SPLs). Family-based analysis techniques
    operate on a single artifact, referred to as a family model, that is annotated with
    variability constraints. This modeling approach paves the way for efficient model-based
    testing and model checking for SPLs. Albeit reasonably efficient, the creation and
    maintenance of family models tend to be time consuming and error-prone, especially
    if there are crosscutting features. To tackle this issue, we introduce FFSMDiff, a
    fully automated technique to learn featured finite state machines (FFSM), a family-based
    formalism that unifies Mealy Machines from SPLs into a single representation. Our
    technique incorporates variability to compare and merge Mealy machines and annotate
    states and transitions with feature constraints. We evaluate our technique using 34
    products derived from three different SPLs. Our results support the hypothesis that
    families of Mealy machines can be effectively merged into succinct FFSMs with fewer
    states, especially if there is high feature sharing among products. These indicate
    that FFSMDiff is an efficient family-based model learning technique.},
    booktitle = {Proceedings of the 23rd International Systems and Software Product Line Conference - Volume A},
    pages = {52–63},
    numpages = {12},
    keywords = {150% model, family model, software product lines, model learning},
    location = {Paris, France},
    series = {SPLC '19},
    pdf       = {damascenoetal_splc2019.pdf},
    slides    = {damascenoetal_splc2019_slide.pdf},
    website   = {https://github.com/damascenodiego/learningFFSM/tree/master/experiments/splc2019},
    supp      = {damascenoetal_splc2019_artifact.pdf},
    bibtex_show = true,

}

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