Packages

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  Definition Classes

  root

•  package org

  Definition Classes

  root

•  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) that can be used to create arbitrary representations.
  • a library to create a one-to-one in-memory representation of Java bytecode (Bytecode Disassembler).
  • a library to create a representation of Java bytecode that facilitates writing simple static analyses (Bytecode Representation - org.opalj.br).
  • 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 and to facilitate checking architecture definitions.
  • a library for the lightweight manipulation and creation of Java bytecode (Bytecode Assembler).

  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 ai

  Implementation of an abstract interpretation (ai) framework – also referred to as OPAL.

  Implementation of an abstract interpretation (ai) framework – also referred to as OPAL.

  Please note that OPAL/the abstract interpreter just refers to the classes and traits defined in this package (ai). The classes and traits defined in the sub-packages (in particular in domain) are not considered to be part of the core of OPAL/the abstract interpreter.

  Definition Classes

  opalj

  Note

  This framework assumes that the analyzed bytecode is valid; i.e., the JVM's bytecode verifier would be able to verify the code. Furthermore, load-time errors (e.g., LinkageErrors) are – by default – completely ignored to facilitate the analysis of parts of a project. In general, if the presented bytecode is not valid, the result is undefined (i.e., OPAL may report meaningless results, crash or run indefinitely).

  See also

  org.opalj.ai.AI - Implements the abstract interpreter that processes a methods code and uses an analysis-specific domain to perform the abstract computations.

  org.opalj.ai.Domain - The core interface between the abstract interpretation framework and the abstract domain that is responsible for performing the abstract computations.

•  package fpcf

  Definition Classes

  ai

•  package analyses

  Definition Classes

  fpcf

• DomainBasedFPCFAnalysisScheduler
• EagerL0BaseAIAnalysis
• EagerLBFieldValuesAnalysis
• EagerLBMethodReturnValuesAnalysis
• FieldValuesAnalysis
• L0BaseAIResultAnalysis
• L0BaseAIResultAnalysisScheduler
• LBFieldValuesAnalysis
• LBMethodReturnValuesAnalysis
• LazyL0BaseAIAnalysis

org.opalj.ai.fpcf.analyses

LBMethodReturnValuesAnalysis