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A New Architecture for Transformation-Based Generators
December 2004 (vol. 30 no. 12)
pp. 1036-1054
ASCII Text
x 
 
Ted J. Biggerstaff, "A New Architecture for Transformation-Based Generators," IEEE Transactions on Software Engineering, vol. 30, no. 12, pp. 1036-1054, December, 2004.
 
 
BibTex
x
 
@article{ 10.1109/TSE.2004.89,
author = {Ted J. Biggerstaff},
title = {A New Architecture for Transformation-Based Generators},
journal ={IEEE Transactions on Software Engineering},
volume = {30},
number = {12},
issn = {0098-5589},
year = {2004},
pages = {1036-1054},
doi = {http://doi.ieeecomputersociety.org/10.1109/TSE.2004.89},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Software Engineering
TI - A New Architecture for Transformation-Based Generators
IS - 12
SN - 0098-5589
SP1036
EP1054
EPD - 1036-1054
A1 - Ted J. Biggerstaff,
PY - 2004
KW - Domain-specific architectures
KW - image processing
KW - inference engines
KW - logic programming
KW - optimization
KW - partial evaluation
KW - pattern matching
KW - program synthesis
KW - reusable software
KW - search
KW - program transformations.
VL - 30
JA - IEEE Transactions on Software Engineering
ER -
 
A challenge of many transformation-based generators is that they are trying to achieve three mutually antagonistic goals simultaneously: 1) deeply factored operators and operands to gain the combinatorial programming leverage provided by composition, 2) high-performance code in the generated program, and 3) small (i.e., practical) generation search spaces. The Anticipatory Optimization Generator (AOG) has been built to explore architectures and strategies that address this challenge. The fundamental principle underlying all of AOG's strategies is to solve separate, narrow, and specialized generation problems by strategies that are narrowly tailored to specific problems rather than a single, universal strategy aimed at all problems. A second fundamental notion is the preservation and use of domain-specific information as a way to gain extra leverage on generation problems. This paper will focus on two specific mechanisms: 1) Localization: the generation and merging of implicit control structures and 2) Tag-Directed Transformations: a new control structure for transformation-based optimization that allows differing kinds of retained domain knowledge (e.g., optimization knowledge) to be anticipated, affixed to the component parts in the reuse library, and triggered when the time is right for its use.

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Index Terms:
Domain-specific architectures, image processing, inference engines, logic programming, optimization, partial evaluation, pattern matching, program synthesis, reusable software, search, program transformations.
Citation:
Ted J. Biggerstaff, "A New Architecture for Transformation-Based Generators," IEEE Transactions on Software Engineering, vol. 30, no. 12, pp. 1036-1054, Dec. 2004, doi:10.1109/TSE.2004.89
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