---
res:
  bibo_abstract:
  - "Interprocedural data-flow analyses form an expressive and useful paradigm of
    numerous static analysis applications, such as live variables analysis, alias
    analysis and null pointers analysis. The most widely-used framework for interprocedural
    data-flow analysis is IFDS, which encompasses distributive data-flow functions
    over a finite domain. On-demand data-flow analyses restrict the focus of the analysis
    on specific program locations and data facts. This setting provides a natural
    split between (i) an offline (or preprocessing) phase, where the program is partially
    analyzed and analysis summaries are created, and (ii) an online (or query) phase,
    where analysis queries arrive on demand and the summaries are used to speed up
    answering queries.\r\nIn this work, we consider on-demand IFDS analyses where
    the queries concern program locations of the same procedure (aka same-context
    queries). We exploit the fact that flow graphs of programs have low treewidth
    to develop faster algorithms that are space and time optimal for many common data-flow
    analyses, in both the preprocessing and the query phase. We also use treewidth
    to develop query solutions that are embarrassingly parallelizable, i.e. the total
    work for answering each query is split to a number of threads such that each thread
    performs only a constant amount of work. Finally, we implement a static analyzer
    based on our algorithms, and perform a series of on-demand analysis experiments
    on standard benchmarks. Our experimental results show a drastic speed-up of the
    queries after only a lightweight preprocessing phase, which significantly outperforms
    existing techniques.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Krishnendu
      foaf_name: Chatterjee, Krishnendu
      foaf_surname: Chatterjee
      foaf_workInfoHomepage: http://www.librecat.org/personId=2E5DCA20-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-4561-241X
  - foaf_Person:
      foaf_givenName: Amir Kafshdar
      foaf_name: Goharshady, Amir Kafshdar
      foaf_surname: Goharshady
      foaf_workInfoHomepage: http://www.librecat.org/personId=391365CE-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0003-1702-6584
  - foaf_Person:
      foaf_givenName: Rasmus
      foaf_name: Ibsen-Jensen, Rasmus
      foaf_surname: Ibsen-Jensen
      foaf_workInfoHomepage: http://www.librecat.org/personId=3B699956-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0003-4783-0389
  - foaf_Person:
      foaf_givenName: Andreas
      foaf_name: Pavlogiannis, Andreas
      foaf_surname: Pavlogiannis
      foaf_workInfoHomepage: http://www.librecat.org/personId=49704004-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-8943-0722
  bibo_doi: 10.1007/978-3-030-44914-8_5
  bibo_volume: 12075
  dct_date: 2020^xs_gYear
  dct_identifier:
  - UT:000681656800005
  dct_isPartOf:
  - http://id.crossref.org/issn/0302-9743
  - http://id.crossref.org/issn/1611-3349
  - http://id.crossref.org/issn/9783030449131
  dct_language: eng
  dct_publisher: Springer Nature@
  dct_title: Optimal and perfectly parallel algorithms for on-demand data-flow analysis@
...