Applying Plan Recognition Algorithms to Program Understanding
Steven Woods and
Alex
Quilici and
Qiang Yang.
Abstract
Program understanding is often viewed as the task of extracting
plans and design goals from program source. As such, it is
natural to try to apply standard AI plan recognition techniques
to the program understanding problem. Yet program understanding
researchers have quietly, but consistently, avoided the
use of these plan recognition algorithms.
This paper shows that treating program understanding as plan
recognition is too simplistic, and that traditional AI search
algorithms for plan recognition are not suitable, as is, for program
understanding. In particular, we show (1) that the program
understanding task differs significantly from
the typical general plan recognition task along several key
dimensions, (2) that the program understanding task has particular
properties that make it particularly amenable to constraint
satisfaction techniques, and (3) that augmenting AI plan recognition
algorithms with these techniques can lead to effective solutions
for the program understanding problem.
Awards, Comments
This paper was one of three nominated for consideration as best paper at KBSE
1996.
Copyright Notice
Copyright 1996 IEEE. Published in the Proceedings of the Eleventh IEEE
Knowledge-Based Software Engineering Conference (KBSE-96), September, 1996,
Syracuse, New York, USA. Personal use of this material is permitted. However,
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The Paper