Verification Complexity: Definitions, Measurements, and Indicators

Sukhwan Jung; Alejandro Salado

doi:10.65351/hsi24.031

Abstract

While verification is an integral process of systems engineering, there is no consensus on measures for a full-scale verification complexity and what it represents. Verification engineers can rely on their implicit expertise to determine relative complexity differences, but this is resource intensive and scales badly with large systems. This research aims to define the verification complexity in an explicit and mathematical manner, suggest relevant measures, and propose indicators for verification complexity. Data gathering and experiment design have been finished with varying sizes and interconnections. Background research is being conducted on the verification complexity definition and relevant measures. Once finished, machine learning models will be trained on the measures with the proposed definition as a dependent variable. The trained model will then be analyzed to determine accurate, explicit indicators of verification complexity. These are expected to aid more accurate information propagation between system stakeholders, especially engineers, reducing system development costs.

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Citation

BibTeX citation:

@inproceedings{jung2024,
  author = {Jung, Sukhwan and Salado, Alejandro},
  editor = {Masson, Dimitri},
  publisher = {INCOSE},
  title = {Verification {Complexity:} {Definitions,} {Measurements,} and
    {Indicators}},
  booktitle = {Proceedings International Conference on Human Systems
    Integration},
  volume = {3},
  date = {2024-08-29},
  url = {https://hsi-wg.github.io/conferences/HSI2024/proceedings/verification-complexity-definitions-measurements-and-indicators.html},
  doi = {10.65351/hsi24.031},
  langid = {en},
  abstract = {While verification is an integral process of systems
    engineering, there is no consensus on measures for a full-scale
    verification complexity and what it represents. Verification
    engineers can rely on their implicit expertise to determine relative
    complexity differences, but this is resource intensive and scales
    badly with large systems. This research aims to define the
    verification complexity in an explicit and mathematical manner,
    suggest relevant measures, and propose indicators for verification
    complexity. Data gathering and experiment design have been finished
    with varying sizes and interconnections. Background research is
    being conducted on the verification complexity definition and
    relevant measures. Once finished, machine learning models will be
    trained on the measures with the proposed definition as a dependent
    variable. The trained model will then be analyzed to determine
    accurate, explicit indicators of verification complexity. These are
    expected to aid more accurate information propagation between system
    stakeholders, especially engineers, reducing system development
    costs.}
}

For attribution, please cite this work as:

Jung, Sukhwan, and Alejandro Salado. 2024. “Verification Complexity: Definitions, Measurements, and Indicators.” In Proceedings International Conference on Human Systems Integration, edited by Dimitri Masson. Vol. 3. INCOSE. https://doi.org/10.65351/hsi24.031.