Type
Text
Type
Dissertation
Advisor
Yanhong A. Liu. | Scott D. Stoller | Rob Johnson | John Field.
Date
2011-05-01
Keywords
Alias Analysis, Dynamic Languages, Incrementalization, Rule Composition, Transformation Systems | Computer Science
Department
Department of Computer Science
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/71609
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
Transformation systems are important for program manipulations such as optimization, instrumentation, and refactoring. Even though not always stated explicitly, these transformations are always driven by invariants, such as maintaining invariants for optimization, checking invariants for verification, and so on. This dissertation describes a system that allows coordinated transformations driven by invariants to be specified declaratively, as invariant rules, and applied automatically. We specially describe our implementation for applying invariant rules to Python and C programs, alias and type analyses developed for applying invariant rules, and a method for composing and optimizing invariant rules. We also describe successful applications of the system in generating efficient implementations from clear and modular specifications, in instrumenting programs for runtime invariant checking, query-based debugging, and profiling, and in code refactoring.
Recommended Citation
Gorbovitski, Michael, "A System for Invariant-Driven Transformations" (2011). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 814.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/814