Entry Duesterwald:1997:PFD from toplas.bib

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BibTeX entry

@Article{Duesterwald:1997:PFD,
  author =       "Evelyn Duesterwald and Rajiv Gupta and Mary Lou
                 Soffa",
  title =        "A practical framework for demand-driven
                 interprocedural data flow analysis",
  journal =      j-TOPLAS,
  volume =       "19",
  number =       "6",
  pages =        "992--1030",
  month =        nov,
  year =         "1997",
  CODEN =        "ATPSDT",
  ISSN =         "0164-0925 (print), 1558-4593 (electronic)",
  ISSN-L =       "0164-0925",
  bibdate =      "Wed Mar 11 18:11:48 MST 1998",
  bibsource =    "http://www.acm.org/pubs/toc/;
                 http://www.math.utah.edu/pub/tex/bib/toplas.bib",
  URL =          "http://www.acm.org:80/pubs/citations/journals/toplas/1997-19-6/p992-duesterwald/",
  abstract =     "The high cost and growing importance of
                 interprocedural data flow analysis have led to an
                 increased interest in demand-driven algorithms. In this
                 article, we present a general framework for developing
                 demand-driven interprocedural data flow analyzers and
                 report our experience in evaluating the performance of
                 this approach. A demand for data flow information is
                 modeled as a set of queries. The framework includes a
                 generic demand-driven algorithm that determines the
                 response to query by iteratively applying a system of
                 query propagation rules. The propagation rules yield
                 precise responses for the class of distributive finite
                 data flow problems. We also describe a two-phase
                 framework variation to accurately handle
                 nondistributive problems. A performance evaluation of
                 our demand-driven approach is presented for two data
                 flow problems, namely, reaching-definitions and copy
                 constant propagation. Our experiments show that
                 demand-driven analysis performs well in practice,
                 reducing both time and space requirements when compared
                 with exhaustive analysis.",
  acknowledgement = ack-nhfb,
  fjournal =     "ACM Transactions on Programming Languages and
                 Systems",
  keywords =     "algorithms; experimentation; performance",
  subject =      "{\bf D.3.4} Software, PROGRAMMING LANGUAGES,
                 Processors, Compilers. {\bf D.2.2} Software, SOFTWARE
                 ENGINEERING, Design Tools and Techniques. {\bf H.3.4}
                 Information Systems, INFORMATION STORAGE AND RETRIEVAL,
                 Systems and Software, Question-answering (fact
                 retrieval) systems**.",
}

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