SOOTBy Joe Palmer

Information taken from http://www.sable.mcgill.ca/soot/tutorial/pldi03/tutorial.pdf

General OverviewGeneral Overview

Developed by Sable Research Group out of McGill University in 1996-1997

Used to optimize Java Bytecode 4 source languages 4 intermediate representations used

Sources LanguagesSources Languages

Primarily takes Java Source as its input Can also take:

SMLSchemeEiffel

I.R.’sI.R.’s Baf:

Streamlined, stack-based representation of bytecode Abstracts type dependent variations of expressions into a single expression

Jimple: Stack-less, typed, 3-Address representation of bytecode Mix between java source and java bytecode Linearization of a single expression into 3 separate statements

Only refers to 3 local vars or conts at once Only 15 jimple instructions are used

Compared to 200 possible instructions in java bytecode! Shimple:

SSA-form version of Jimple Each local var has a single static point of definition (never reassign) Uses Phi-Nodes for control flow

Grimp: Similar to Jimple but allows trees of expressions together with a

representation of a “new” operator Expressions are “aggregated”

main IR used!!

Phases of the OptimizationPhases of the Optimization

AnalysisAnalysis

Tested using 8 SPECjvm98 benchmarks running on JDK 1.2 Showed 8% improvement when optimized bytecode is run using

an interpreter 21% improvement when optimized bytecode is run using a JIT

compiler Used in research with traditional compiler analyses,

analyses for software engineering, analysis for distributed programs, and software verification Ptolemy Project Bandera Canvas Project

Strengths and Future Strengths and Future EnhancementsEnhancements Used as a common infrastructure with which

researchers could compare common analyses Enhancements coming:

Attribute management Attribute legends Improved visual attributes in source Interactive CFGs Growable graphical callgraph Making conversion from Java to Jimple more stable and

complete