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  • package opalj

    OPAL is a Scala-based framework for the static analysis, manipulation and creation of Java bytecode.

    OPAL is a Scala-based framework for the static analysis, manipulation and creation of Java bytecode. OPAL is designed with performance, scalability and adaptability in mind.

    Its main components are:

    • a library (Common) which provides generally useful data-structures and algorithms for static analyses.
    • a framework for implementing lattice based static analyses (Static Analysis Infrastructure)
    • a framework for parsing Java bytecode (Bytecode Infrastructure - org.opalj.bi) that can be used to create arbitrary representations.
    • a library to create a one-to-one in-memory representation of Java bytecode (Bytecode Disassembler - org.opalj.da).
    • a library to convert this representation to Java class files (Bytecode Creator - org.opalj.bc).
    • a library to create a representation of Java bytecode that facilitates writing simple static analyses (Bytecode Representation - org.opalj.br).
    • a library to create a stackless, three-address code representation of Java bytecode that facilitates writing complex static analyses (Three Address Code - org.opalj.tac).
    • a scalable, easily customizable framework for the abstract interpretation of Java bytecode (Abstract Interpretation Framework - org.opalj.ai).
    • a library to extract dependencies between code elements (Dependencies Extraction - org.opalj.de) and to facilitate checking architecture definitions (Architecture Validation - org.opalj.av).
    • a library for the lightweight manipulation and creation of Java bytecode (Bytecode Assembler - org.opalj.ba).
    • a library for parsing Android packages (APK - org.opalj.apk).
    • libraries for writing static analyses using the interprocedural finite distributive subset (IFDS - org.opalj.ifds) and interprocedural distributive environment (IDE - org.opal.ide) algorithms.

    General Design Decisions

    Thread Safety

    Unless explicitly noted, OPAL is thread safe. I.e., the classes defined by OPAL can be considered to be thread safe unless otherwise stated. (For example, it is possible to read and process class files concurrently without explicit synchronization on the client side.)

    No null Values

    Unless explicitly noted, OPAL does not null values I.e., fields that are accessible will never contain null values and methods will never return null. If a method accepts null as a value for a parameter or returns a null value it is always explicitly documented. In general, the behavior of methods that are passed null values is undefined unless explicitly documented.

    No Typecasts for Collections

    For efficiency reasons, OPAL sometimes uses mutable data-structures internally. After construction time, these data-structures are generally represented using their generic interfaces (e.g., scala.collection.{Set,Map}). However, a downcast (e.g., to add/remove elements) is always forbidden as it would effectively prevent thread-safety.

    Assertions

    OPAL makes heavy use of Scala's Assertion Facility to facilitate writing correct code. Hence, for production builds (after thorough testing(!)) it is highly recommend to build OPAL again using -Xdisable-assertions.

    Definition Classes
    org
  • package si
    Definition Classes
    opalj
  • package flowanalysis

    Definition Classes
    si
  • AcyclicRegionType
  • Block
  • Case
  • CyclicRegionType
  • DataFlowAnalysis
  • DataFlowEnvironment
  • FlowGraphNode
  • GlobalEntry
  • GlobalExit
  • IfThen
  • IfThenElse
  • Improper
  • NaturalLoop
  • Proper
  • Region
  • RegionType
  • SelfLoop
  • Statement
  • StructuralAnalysis
  • WhileLoop
c

org.opalj.si.flowanalysis

DataFlowAnalysis

class DataFlowAnalysis[Data, Environment <: DataFlowEnvironment[Data, Environment]] extends AnyRef

Performs structural data flow analysis based on the results of a StructuralAnalysis. In more detail, this means that the control tree produced by the StructuralAnalysis is traversed recursively in a depth-first manner. Individual regions are processed by piping an Environment through their nodes and joining the environments where paths meet up. Thus, the individual flow functions defined at the statement PCs of a method are combined using region-type-specific patterns to effectively act as a flow function of the entire region, which is then processed itself due to the recursive nature of the algorithm.

Source
DataFlowAnalysis.scala
See also

StructuralAnalysis, Environment

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Instance Constructors

  1. new DataFlowAnalysis(controlTree: ControlTree, superFlowGraph: SuperFlowGraph, highSoundness: Boolean)

    controlTree

    The control tree from the structural analysis.

    superFlowGraph

    The super flow graph from the structural analysis

    highSoundness

    Whether to use high soundness mode or not. Currently, this influences the handling of loops, i.e. whether they are approximated by one execution of the loop body (low soundness) or via a top value on all variables in the method (high soundness).

Type Members

  1. type FlowFunction = (Environment) => Environment

Value Members

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  6. def compute(flowFunctionByPc: Map[Int, FlowFunction])(startEnv: Environment): Environment

    Computes the resulting environment after the data flow analysis.

    Computes the resulting environment after the data flow analysis.

    flowFunctionByPc

    A mapping from PC to the flow functions to be used

    startEnv

    The base environment which is piped into the first region to be processed.

    returns

    The resulting Environment after execution of all flow functions using the region hierarchy given in the control tree.

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