Assumption Mining

Assumption Mining is a formal method in assertion-based verification that identifies and extracts implicit system-level assumptions from hardware or software designs to refine verification scopes and enhance property generation.

5

Publications

224

Citations

15

Authors

7

Institutions

Publications per year

2017–2026

1

Authors

15

  1. SA
    Sanjit A. Seshia

    University of California, Berkeley, United States

    2 publications · 124 citations

    2

    h-index

  2. CM
    Claudio Menghi

    University of Luxembourg, Luxembourg

    1 publications · 17 citations

    1

    h-index

  3. GP
    Gunnar Peterson

    Activated Research Company (United States), United States

    1 publications · 16 citations

    1

    h-index

  4. DW
    David Wolfe

    Space Telescope Science Institute, United States

    1 publications · 17 citations

    1

    h-index

  5. LB
    Lionel Briand

    University of Luxembourg, Luxembourg

    1 publications · 17 citations

    1

    h-index

1–5 of 15

Venues

  1. IEEE Security & Privacy

    1 publications · 16 citations

    1

    h-index

  2. Formal Methods in System Design

    1 publications · 67 citations

    1

    h-index

1–2