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Solving Regular Tree Grammar Based Constraints
| Book Series | Lecture Notes in Computer Science |
| Publisher | Springer Berlin / Heidelberg |
| ISSN | 0302-9743 (Print) 1611-3349 (Online) |
| Volume | Volume 2126/2001 |
| Book | Static Analysis |
| DOI | 10.1007/3-540-47764-0 |
| Copyright | 2001 |
| ISBN | 978-3-540-42314-0 |
| DOI | 10.1007/3-540-47764-0_13 |
| Pages | 213-233 |
| Subject Collection | Computer Science |
| SpringerLink Date | Monday, January 01, 2001 |
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Solving Regular Tree Grammar Based Constraints
Yanhong A. Liu5 , Ning Li6 and Scott D. Stoller5 
| (5) |
Computer Science Dept., State University of New York, Stony Brook, NY, 11794 |
| (6) |
Computer Science Dept., University of Wisconsin, Madison, WI, 53706 |
Abstract
This paper describes the precise specification, design, analysis, implementation, and measurements of an efficient algorithm
for solving regular tree grammar based constraints. The particular constraints are for dead-code elimination on recursive
data, but the method used for the algorithm design and complexity analysis is general and applies to other program analysis
problems as well. The method is centered around Paige’s finite differencing, i.e., computing expensive set expressions incrementally,
and allows the algorithm to be derived and analyzed formally and implemented easily. We propose higher-level transformations
that make the derived algorithm concise and allow its complexity to be analyzed accurately. Although a rough analysis shows
that the worst-case time complexity is cubic in program size, an accurate analysis shows that it is linear in the number of
live program points and in other parameters, including mainly the arity of data constructors and the number of selector applications
into whose arguments the value constructed at a program point might flow. These parameters explain the performance of the
analysis in practice. Our implementation also runs two to ten times as fast as a previous implementation of an informally
designed algorithm.
This work is supported in part by ONR under grants N00014-99-1-0132, N00014-99-1-0358, and N00014-01-1-0109, by NSF under
grants CCR-9711253 and CCR-9876058, and by a Motorola University Partnership in Research Grant.
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