Team for Research in
Ubiquitous Secure Technology

Citation
B. Hackett, A. Aiken. "How is Aliasing Used in Systems Software?". International Symposium on Foundations of Software Engineering, 69-80, November, 2006.

Abstract
We present a study of all sources of aliasing in over one million lines of C code, identifying in the process the common patterns of aliasing that arise in practice. We nd that aliasing has a great deal of structure in real programs and that just nine programming idioms account for nearly all aliasing in our study. Our study requires an automatic alias analysis that both scales to large systems and has a low false positive rate. To this end, we also present a new context-, ow-, and partially path-sensitive alias analysis that, together with a new technique for object naming, achieves a false aliasing rate of 26.2% on our benchmarks.

Electronic downloads

• http://theory.stanford.edu/~aiken/publications/papers/fse06.pdf

• HTML

  B. Hackett, A. Aiken. <a
  href="http://www.truststc.org/pubs/614.html"
  >How is Aliasing Used in Systems Software?</a>,
  International Symposium on Foundations of Software
  Engineering, 69-80, November, 2006.

• Plain text

  B. Hackett, A. Aiken. "How is Aliasing Used in Systems
  Software?". International Symposium on Foundations of
  Software Engineering, 69-80, November, 2006.

• BibTeX

  @inproceedings{HackettAiken06_HowIsAliasingUsedInSystemsSoftware,
      author = {B. Hackett and A. Aiken},
      title = {How is Aliasing Used in Systems Software?},
      booktitle = {International Symposium on Foundations of Software
                Engineering},
      pages = {69-80},
      month = {November},
      year = {2006},
      abstract = {We present a study of all sources of aliasing in
                over one million lines of C code, identifying in
                the process the common patterns of aliasing that
                arise in practice. We nd that aliasing has a
                great deal of structure in real programs and that
                just nine programming idioms account for nearly
                all aliasing in our study. Our study requires an
                automatic alias analysis that both scales to large
                systems and has a low false positive rate. To this
                end, we also present a new context-, ow-, and
                partially path-sensitive alias analysis that,
                together with a new technique for object naming,
                achieves a false aliasing rate of 26.2% on our
                benchmarks.},
      URL = {http://www.truststc.org/pubs/614.html}
  }
  

Posted by Jessica Gamble on 18 Mar 2009.
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