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@inproceedings{BLM20,
  title={
    Baital: An Adaptive Weighted Sampling Approach for Improved t-wise Coverage
  },
  author={Baranov, Eduard and Legay, Axel and Meel, Kuldeep S.},
  booktitle=FSE,
  month=nov,
  year={2020},
  bib2html_dl_pdf={../Papers/fse20blm.pdf},
  bib2html_pubtype={Refereed Conference},
  bib2html_rescat={Software Engineering},
  abstract={
    The rise of highly configurable complex software and its widespread usage
    requires design of efficient testing methodology. T-wise coverage is a
    leading metric to measure the quality of the testing suite and the
    underlying test generation engine. While uniform sampling based test
    generation is widely believed to be the state of the art approach to achieve
    t-wise coverage in presence of constraints on the set of configurations,
    uniform sampling fails to achieve high t-wise coverage in presence of
    complex constraints.
    In this work, we propose a novel approach Baital, based on adaptive weighted
    sampling using literal weighted functions, to generate test sets with high
    t-wise coverage. We demonstrate that our approach leads to significantly
    high t-wise coverage. The novel usage of literal weighted sampling leaves
    open several interesting directions, empirical as well as theoretical, for
    future research.
  },
}