Applying Plan Recognition Algorithms To Program Understanding

  • Authors:

  • Alex Quilici;Qiang Yang;Steven Woods

  • Affiliations:

  • Department of Electrical Engineering, University of Hawaii at Manoa 2540 Dole St, Holmes 483, Honolulu, HI 96822. E-mail: alex@wiliki.eng.hawaii.edu;School of Computing Science, Simon Fraser University Burnaby, British Columbia V5A 1S6. E-mail: qyang@cs.sfu.ca;Department of Electrical Engineering, University of Hawaii at Manoa 2540 Dole St, Holmes 483, Honolulu, HI 96822. E-mail: sgwoods@spectra.eng.hawaii.edu

  • Venue:
  • Automated Software Engineering
  • Year:
  • 1998

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Abstract

Program understanding is often viewed as thetask of extracting plans and design goals from program source. As such, it isnatural to try to apply standard AI plan recognition techniquesto the program understanding problem. Yet program understandingresearchers have quietly, but consistently, avoided theuse of these plan recognition algorithms. This paper shows that treating program understanding as planrecognition is too simplistic and that traditional AI searchalgorithms for plan recognition are not suitable, as is,for programunderstanding. In particular, we show (1) that the programunderstanding task differs significantly fromthe typical general plan recognition task along several keydimensions, (2) that the program understanding task has particularproperties that make it particularly amenable to constraintsatisfaction techniques, and (3) that augmenting AI plan recognitionalgorithms with these techniques can lead to effective solutionsfor the program understanding problem.