Description

Analysis to test the similarity of two functions.

This analysis compares two functions and returns a floating point value that represents how different they are from one another. The scale of the returned value depends on the metrics that are used, but generally a difference of zero represents two functions that are equivalent according to the metric, and values larger than zero indicate the presence of differences.

This FunctionSimilarity class provides the following things:

an interface for manipulating categories, and their metrics (defined below).

a container for associating the characteristic values produced by metrics with the functions on which the metrics were applied.

an interface for comparing functions with computed characteristic values.

Definitions

A characteristic value is data that measures some characteristic of a function. This analysis supports two types of characteristic values: floating-point Cartesian points and ordered lists of integers. Floating-point scalar values are a special case of Cartesian points.

A metric is a functor that takes input related to a disassembled function and produces characteristic values. The characteristic data can be either zero or more floating-point Cartesian points (all having the same dimensionality) or an ordered list of zero or more integers. Examples are: (1) a metric that returns a four-dimensional point describing the number of immediate and second-level predecessors and successors for each vertex of the function control flow graph; (2) a metric that returns an ordered list describing local variables detected by a data-flow analysis; (3) a metric that returns a histogram (multi-dimensional point) of the classes of instructions found in the function.

A metric category (or just category) is a collection of characteristic values from compatible metrics (usually just one metric).

Queries

Use this analysis by performing these steps. Steps 2 and 3 can be interleaved.

Create a FunctionSimilarity analysis object
Declare metric categories
Populate metric categories with characteristic data for each function
Query results

Definition at line 60 of file FunctionSimilarity.h.

#include <Rose/BinaryAnalysis/FunctionSimilarity.h>

Collaboration diagram for Rose::BinaryAnalysis::FunctionSimilarity:

[legend]

Classes
class	Exception

Public Types
enum	CValKind { CARTESIAN_POINT , ORDERED_LIST }
	Kinds of characteristic values. More...

enum	Statistic { AVERAGE , MEDIAN , MAXIMUM }
	Ways that values can be combined. More...

typedef std::vector< double >	CartesianPoint
	Characteristic value that's a Cartesian point.

typedef std::vector< int >	OrderedList
	Characteristic value that's an ordered list of integers.

typedef std::vector< CartesianPoint >	PointCloud
	Unordered collection of Cartesian points.

typedef std::vector< OrderedList >	OrderedLists
	Ordered collection of ordered lists of integers.

typedef size_t	CategoryId
	ID number unique within this analysis context.

typedef std::pair< Partitioner2::FunctionPtr, double >	FunctionDistancePair
	Function and distance to some other function.

typedef std::pair< Partitioner2::FunctionPtr, Partitioner2::FunctionPtr >	FunctionPair
	Pair of functions.

typedef Matrix< double >	DistanceMatrix
	Square matrix representing distances.

Public Member Functions
void	clear ()

Rose::Progress::Ptr	progress () const
	Property: Object to which progress reports are made.

CategoryId	declarePointCategory (const std::string &name, size_t dimensionality, bool allowExisting=true)
	Declare a new category of Cartesian points.

CategoryId	declareListCategory (const std::string &name, bool allowExisting=true)
	Declare a new category of ordered lists of integers.

size_t	nCategories () const
	Number of categories.

CategoryId	findCategory (const std::string &name) const
	Find a category by name.

CValKind	categoryKind (CategoryId) const
	Kind of category.

size_t	categoryDimensionality (CategoryId) const
	Dimensionality of Cartesian characteristic points.

void	insertPoint (const Partitioner2::FunctionPtr &, CategoryId, const CartesianPoint &)
	Insert a Cartesian point characteristic value.

void	insertList (const Partitioner2::FunctionPtr &, CategoryId, const OrderedList &)
	Insert an ordered list characteristic value.

size_t	size (const Partitioner2::FunctionPtr &, CategoryId) const
	Number of characteristic points in a category.

const PointCloud &	points (const Partitioner2::FunctionPtr &, CategoryId) const
	Catesian points contained in a category.

const OrderedLists &	lists (const Partitioner2::FunctionPtr &, CategoryId) const
	Ordered lists contained in a category.

double	compare (const Partitioner2::FunctionPtr &, const Partitioner2::FunctionPtr &, double dflt=NAN) const
	Compare two functions.

std::vector< FunctionDistancePair >	compareOneToAll (const Partitioner2::FunctionPtr &) const
	Compare one function with all others.

std::vector< std::vector< double > >	compareManyToMany (const std::vector< Partitioner2::FunctionPtr > &, const std::vector< Partitioner2::FunctionPtr > &) const
	Compare many functions to many others.

DistanceMatrix	compareManyToManyMatrix (std::vector< Partitioner2::FunctionPtr >, std::vector< Partitioner2::FunctionPtr >) const
	Compare many functions to many others.

std::vector< FunctionPair >	findMinimumCostMapping (const std::vector< Partitioner2::FunctionPtr > &list1, const std::vector< Partitioner2::FunctionPtr > &list2) const
	Minimum cost 1:1 mapping.

std::vector< double >	computeDistances (const std::vector< Partitioner2::FunctionPtr > &list1, const std::vector< Partitioner2::FunctionPtr > &list2, size_t nThreads) const
	Compute distances between sets of functions.

void	printCharacteristicValues (std::ostream &) const
	Print characteristic values for this analysis.


Statistic	categoryAccumulatorType () const
	Property: Statistic for combining category distances.

void	categoryAccumulatorType (Statistic s)
	Property: Statistic for combining category distances.


double	categoryWeight (CategoryId) const
	Property: category weight.

void	categoryWeight (CategoryId, double)
	Property: category weight.


CategoryId	declareCfgConnectivity (const std::string &categoryName)
	Control flow graph connectivity.

void	measureCfgConnectivity (CategoryId, const Partitioner2::PartitionerConstPtr &, const Partitioner2::FunctionPtr &, size_t maxPoints=UNLIMITED)
	Control flow graph connectivity.


CategoryId	declareCallGraphConnectivity (const std::string &categoryName)
	Function calls.

void	measureCallGraphConnectivity (CategoryId, const Partitioner2::PartitionerConstPtr &, const Partitioner2::FunctionPtr &)
	Function calls.


CategoryId	declareMnemonicStream (const std::string &categoryName)
	Instruction mnemonic stream.

void	measureMnemonicStream (CategoryId, const Partitioner2::PartitionerConstPtr &, const Partitioner2::FunctionPtr &)
	Instruction mnemonic stream.


template<class FunctionIterator >
std::vector< FunctionDistancePair >	compareOneToMany (const Partitioner2::FunctionPtr &needle, const boost::iterator_range< FunctionIterator > &haystack) const
	Compare one function with many others.

template<class FunctionIterator >
std::vector< FunctionDistancePair >	compareOneToMany (const Partitioner2::FunctionPtr &needle, const FunctionIterator &begin, const FunctionIterator &end) const
	Compare one function with many others.

std::vector< FunctionDistancePair >	compareOneToMany (const Partitioner2::FunctionPtr &needle, const std::vector< Partitioner2::FunctionPtr > &haystack) const
	Compare one function with many others.

Static Public Member Functions
static bool	sortByIncreasingDistance (const FunctionDistancePair &a, const FunctionDistancePair &b)
	Predicate for sorting by ascending distance.

static bool	sortByDecreasingDistance (const FunctionDistancePair &a, const FunctionDistancePair &b)
	Predicate for sorting by descending distance.

static bool	sortByAddress (const FunctionDistancePair &a, const FunctionDistancePair &b)
	Predicate for sorting by function address.

static void	initDiagnostics ()
	Initializes and registers disassembler diagnostic streams.

static double	cartesianDistance (const FunctionSimilarity::CartesianPoint &, const FunctionSimilarity::CartesianPoint &)
	Cartesian distance between two points.

static std::vector< size_t >	findMinimumAssignment (const DistanceMatrix &)
	Find minimum mapping from rows to columns.

static double	totalAssignmentCost (const DistanceMatrix &, const std::vector< size_t > &assignment)
	Total cost of a mapping.

static double	maximumDistance (const DistanceMatrix &)
	Maximum value in the distance matrix.

static double	averageDistance (const DistanceMatrix &)
	Average distance in the matrix.

static double	medianDistance (const DistanceMatrix &)
	Median distance in the matrix.

Static Public Attributes
static const CategoryId	NO_CATEGORY = -1
	Invalid category ID.

static Sawyer::Message::Facility	mlog
	Diagnostic streams.

Member Typedef Documentation

◆ CartesianPoint

typedef std::vector<double> Rose::BinaryAnalysis::FunctionSimilarity::CartesianPoint

Characteristic value that's a Cartesian point.

Definition at line 77 of file FunctionSimilarity.h.

◆ OrderedList

typedef std::vector<int> Rose::BinaryAnalysis::FunctionSimilarity::OrderedList

Characteristic value that's an ordered list of integers.

Definition at line 78 of file FunctionSimilarity.h.

◆ PointCloud

typedef std::vector<CartesianPoint> Rose::BinaryAnalysis::FunctionSimilarity::PointCloud

Unordered collection of Cartesian points.

Definition at line 81 of file FunctionSimilarity.h.

◆ OrderedLists

typedef std::vector<OrderedList> Rose::BinaryAnalysis::FunctionSimilarity::OrderedLists

Ordered collection of ordered lists of integers.

Definition at line 82 of file FunctionSimilarity.h.

◆ CategoryId

typedef size_t Rose::BinaryAnalysis::FunctionSimilarity::CategoryId

ID number unique within this analysis context.

Definition at line 87 of file FunctionSimilarity.h.

◆ FunctionDistancePair

typedef std::pair<Partitioner2::FunctionPtr, double > Rose::BinaryAnalysis::FunctionSimilarity::FunctionDistancePair

Function and distance to some other function.

Definition at line 91 of file FunctionSimilarity.h.

◆ FunctionPair

typedef std::pair<Partitioner2::FunctionPtr, Partitioner2::FunctionPtr> Rose::BinaryAnalysis::FunctionSimilarity::FunctionPair

Pair of functions.

Definition at line 94 of file FunctionSimilarity.h.

◆ DistanceMatrix

typedef Matrix<double> Rose::BinaryAnalysis::FunctionSimilarity::DistanceMatrix

Square matrix representing distances.

Definition at line 100 of file FunctionSimilarity.h.

Member Enumeration Documentation

◆ CValKind

enum Rose::BinaryAnalysis::FunctionSimilarity::CValKind

Kinds of characteristic values.

Enumerator
CARTESIAN_POINT	Values are N-dimensional Cartesian points.
ORDERED_LIST	Values are lists of integers.

Definition at line 73 of file FunctionSimilarity.h.

◆ Statistic

enum Rose::BinaryAnalysis::FunctionSimilarity::Statistic

Ways that values can be combined.

Definition at line 85 of file FunctionSimilarity.h.

Constructor & Destructor Documentation

◆ FunctionSimilarity()

Rose::BinaryAnalysis::FunctionSimilarity::FunctionSimilarity ( )

inline

Definition at line 146 of file FunctionSimilarity.h.

Member Function Documentation

◆ clear()

void Rose::BinaryAnalysis::FunctionSimilarity::clear ( )

inline

Definition at line 149 of file FunctionSimilarity.h.

◆ categoryAccumulatorType() [1/2]

Statistic Rose::BinaryAnalysis::FunctionSimilarity::categoryAccumulatorType ( ) const

inline

Property: Statistic for combining category distances.

When computing the distance between two functions, we first compute the distance between the categories by comparing the characteristic values in those categories. The category distance are then combined using the specified statistic to obtain the distance between the functions.

Definition at line 167 of file FunctionSimilarity.h.

◆ categoryAccumulatorType() [2/2]

void Rose::BinaryAnalysis::FunctionSimilarity::categoryAccumulatorType ( Statistic s )

inline

Property: Statistic for combining category distances.

When computing the distance between two functions, we first compute the distance between the categories by comparing the characteristic values in those categories. The category distance are then combined using the specified statistic to obtain the distance between the functions.

Definition at line 168 of file FunctionSimilarity.h.

◆ progress()

Rose::Progress::Ptr Rose::BinaryAnalysis::FunctionSimilarity::progress ( ) const

inline

Property: Object to which progress reports are made.

Definition at line 172 of file FunctionSimilarity.h.

◆ declarePointCategory()

CategoryId Rose::BinaryAnalysis::FunctionSimilarity::declarePointCategory	(	const std::string &	name,
		size_t	dimensionality,
		bool	allowExisting = `true`
	)

Declare a new category of Cartesian points.

All characteristics values of this category have the same specified dimensionality. The order that points are inserted into this category is irrelevant. If allowExisting is false then the specified name must not already exist or else an std::runtime_error is thrown; if allowExisting is true then either a new category is created or the ID of an existing category is returned. Returns a new category ID number, which are consecutive small integers starting at zero.

◆ declareListCategory()

CategoryId Rose::BinaryAnalysis::FunctionSimilarity::declareListCategory	(	const std::string &	name,
		bool	allowExisting = `true`
	)

Declare a new category of ordered lists of integers.

Each characteristic value inserted into this category may be a different length, but the order that they're inserted defines how they're compared when comparing two such categories from different functions. If allowExisting is false then the specified name must not already exist or else an std::runtime_error is thrown; if allowExisting is true then either a new category is created or the ID of an existing category is returned. Returns a new category ID number, which are consecutive small integers starting at zero.

◆ nCategories()

size_t Rose::BinaryAnalysis::FunctionSimilarity::nCategories ( ) const

inline

Number of categories.

Category ID numbers are values zero through one less than the number of categories.

Definition at line 198 of file FunctionSimilarity.h.

◆ findCategory()

CategoryId Rose::BinaryAnalysis::FunctionSimilarity::findCategory ( const std::string & name ) const

inline

Find a category by name.

Returns the ID number for the category with the specified name, or NO_CATEGORY if no such name exists wihtin this analysis context.

Definition at line 204 of file FunctionSimilarity.h.

References NO_CATEGORY.

◆ declareCfgConnectivity()

CategoryId Rose::BinaryAnalysis::FunctionSimilarity::declareCfgConnectivity ( const std::string & categoryName )

Control flow graph connectivity.

This metric creates a point cloud with one Cartesian point for each basic block in the function. The space is four dimensional with the following dimensions:

Number of immediate succesors.
Number of immediate predecessors.
Number of second level successors averaged over the immediate successors.
Number of second level predecessors averaged over the immediate predecessors.

Each coordinate is normalized to be one of the following values:

0.0 if there are no neighbors
1/3 if there is one neighbor
2/3 if there are two neighbors
1.0 if there are more than two neigbors or any neighbor is indeterminate.

If maxPoints is specified, then functions having more than the specified maximum number of CFG vertices will be truncated arbititrarily to limit the number of vertices.

◆ measureCfgConnectivity()

void Rose::BinaryAnalysis::FunctionSimilarity::measureCfgConnectivity	(	CategoryId	,
		const Partitioner2::PartitionerConstPtr &	,
		const Partitioner2::FunctionPtr &	,
		size_t	maxPoints = `UNLIMITED`
	)

Control flow graph connectivity.

This metric creates a point cloud with one Cartesian point for each basic block in the function. The space is four dimensional with the following dimensions:

Number of immediate succesors.
Number of immediate predecessors.
Number of second level successors averaged over the immediate successors.
Number of second level predecessors averaged over the immediate predecessors.

Each coordinate is normalized to be one of the following values:

0.0 if there are no neighbors
1/3 if there is one neighbor
2/3 if there are two neighbors
1.0 if there are more than two neigbors or any neighbor is indeterminate.

If maxPoints is specified, then functions having more than the specified maximum number of CFG vertices will be truncated arbititrarily to limit the number of vertices.

◆ declareCallGraphConnectivity()

CategoryId Rose::BinaryAnalysis::FunctionSimilarity::declareCallGraphConnectivity ( const std::string & categoryName )

Function calls.

This metric creates a point cloud with one Cartesian point for each function called from the specified function.

◆ measureCallGraphConnectivity()

void Rose::BinaryAnalysis::FunctionSimilarity::measureCallGraphConnectivity	(	CategoryId	,
		const Partitioner2::PartitionerConstPtr &	,
		const Partitioner2::FunctionPtr &
	)

Function calls.

This metric creates a point cloud with one Cartesian point for each function called from the specified function.

◆ declareMnemonicStream()

CategoryId Rose::BinaryAnalysis::FunctionSimilarity::declareMnemonicStream ( const std::string & categoryName )

Instruction mnemonic stream.

This metric creates an ordered list of instruction mnemonics for the entire function by ordering the function's basic block by address and then, for each basic block, appending its instruction mnemonic to the list.

◆ measureMnemonicStream()

void Rose::BinaryAnalysis::FunctionSimilarity::measureMnemonicStream	(	CategoryId	,
		const Partitioner2::PartitionerConstPtr &	,
		const Partitioner2::FunctionPtr &
	)

Instruction mnemonic stream.

This metric creates an ordered list of instruction mnemonics for the entire function by ordering the function's basic block by address and then, for each basic block, appending its instruction mnemonic to the list.

◆ insertPoint()

void Rose::BinaryAnalysis::FunctionSimilarity::insertPoint	(	const Partitioner2::FunctionPtr &	,
		CategoryId	,
		const CartesianPoint &
	)

Insert a Cartesian point characteristic value.

Inserts the specified Cartesian point into the collection of characteristic values for the category. The first point inserted into a category defines the dimensionality of all points in that category. An exception is thrown if the point does not match the dimensionality of the category or if the category is not one that stores points.

When comparing two functions, the order of points in the point clouds is irrelevant.

◆ insertList()

void Rose::BinaryAnalysis::FunctionSimilarity::insertList	(	const Partitioner2::FunctionPtr &	,
		CategoryId	,
		const OrderedList &
	)

Insert an ordered list characteristic value.

Inserts the specified ordered list of integers into the collection of characteristic values for the category. Ordered lists may have zero or more integers. The lists within a category need not all be the same length.

When comparing two functions, the order that ordered lists were inserted into the category matters since the distance between two categories is some function of the edit distances between corresponding ordered lists. If you want to insert list-type characteristic values in any order then each list needs to be in its own category.

◆ compare()

double Rose::BinaryAnalysis::FunctionSimilarity::compare	(	const Partitioner2::FunctionPtr &	,
		const Partitioner2::FunctionPtr &	,
		double	dflt = `NAN`
	)		const

Compare two functions.

Compare the two specified functions after having populated their characteristic values. Returns a floating-point distance between the functions where zero indicates that the functions are as similar as possible judging from their characteristic values.

The distance is calculated as follows:

Given the two functions, compute the pair-wise distance between each of their corresponding categories and then combine these distances according to the category weights using maximum, mean, or median.

The distance between two categories containing ordered lists is computed as follows: for each pair of characteristic values (paired according to the order they were inserted into the category), compute the Levenshtein edit distance divided by the longer of the two lists. If one category has more lists than the other then pad the smaller category with empty lists. This gives a list of floating-point values in the closed interval zero to one, which are then combined using maximum, mean, or median.

The distance between two categories containing Cartesian points is computed as follows: If one category has fewer points than the other then temporarily pad the smaller category with points at the origin. Compute the minimum cost 1:1 mapping from points in one category to those in the other and use the total cost as the distance between the categories.

If either function has not been analyzed, or has been analyzed but resulted in no data, then return the dflt value.

◆ compareOneToAll()

std::vector< FunctionDistancePair > Rose::BinaryAnalysis::FunctionSimilarity::compareOneToAll ( const Partitioner2::FunctionPtr & ) const

Compare one function with all others.

Compare the given function with all other functions and return a list of function+cost pairs. The returned list is unsorted.

◆ compareOneToMany() [1/3]

template<class FunctionIterator >

std::vector< FunctionDistancePair > Rose::BinaryAnalysis::FunctionSimilarity::compareOneToMany	(	const Partitioner2::FunctionPtr &	needle,
		const boost::iterator_range< FunctionIterator > &	haystack
	)		const

inline

Compare one function with many others.

Compare the given needle function with all other haystack functions. The returned list is unsorted.

This analysis operates in parallel using multi-threading. It honors the global thread count usually specified with the –threads=N switch.

Definition at line 349 of file FunctionSimilarity.h.

References compareOneToMany().

Referenced by compareOneToMany(), and compareOneToMany().

◆ compareOneToMany() [2/3]

template<class FunctionIterator >

std::vector< FunctionDistancePair > Rose::BinaryAnalysis::FunctionSimilarity::compareOneToMany	(	const Partitioner2::FunctionPtr &	needle,
		const FunctionIterator &	begin,
		const FunctionIterator &	end
	)		const

inline

Compare one function with many others.

Compare the given needle function with all other haystack functions. The returned list is unsorted.

This analysis operates in parallel using multi-threading. It honors the global thread count usually specified with the –threads=N switch.

Definition at line 359 of file FunctionSimilarity.h.

References compareOneToMany().

◆ compareOneToMany() [3/3]

std::vector< FunctionDistancePair > Rose::BinaryAnalysis::FunctionSimilarity::compareOneToMany	(	const Partitioner2::FunctionPtr &	needle,
		const std::vector< Partitioner2::FunctionPtr > &	haystack
	)		const

Compare one function with many others.

Compare the given needle function with all other haystack functions. The returned list is unsorted.

This analysis operates in parallel using multi-threading. It honors the global thread count usually specified with the –threads=N switch.

◆ compareManyToMany()

std::vector< std::vector< double > > Rose::BinaryAnalysis::FunctionSimilarity::compareManyToMany	(	const std::vector< Partitioner2::FunctionPtr > &	,
		const std::vector< Partitioner2::FunctionPtr > &
	)		const

Compare many functions to many others.

Given two ordered lists of functions, calculate the distances from all functions of the first list to all functions of the second list. The return value is a rectangular matrix whose rows are indexed by the functions of the first list and whose columns are indexed by the functions of the second list. See also, compareManyToManyMatrix, which returns a square matrix of function distances.

This analysis operates in parallel using multi-threading. It honors the global thread count usually specified with the –threads=N switch.

◆ compareManyToManyMatrix()

DistanceMatrix Rose::BinaryAnalysis::FunctionSimilarity::compareManyToManyMatrix	(	std::vector< Partitioner2::FunctionPtr >	,
		std::vector< Partitioner2::FunctionPtr >
	)		const

Compare many functions to many others.

Given two ordered lists of functions, temporarily pad the shorter list with null functions to make both lists equal in length. Then calculate the distances from all functions of the first list to all functions of the second list, returning a square distance matrix. See also, compareManyToMany, which may be much faster if the two function lists have wildly different sizes.

This analysis operates in parallel using multi-threading. It honors the global thread count usually specified with the –threads=N switch.

◆ findMinimumCostMapping()

std::vector< FunctionPair > Rose::BinaryAnalysis::FunctionSimilarity::findMinimumCostMapping	(	const std::vector< Partitioner2::FunctionPtr > &	list1,
		const std::vector< Partitioner2::FunctionPtr > &	list2
	)		const

Minimum cost 1:1 mapping.

Compute the minimum cost 1:1 mapping of functions in the first list to those in the second. The algorithm first calls compareManyToManyMatrix to obtain a square matrix of all functions compared with all other functions (null functions are added as necessary to make the result square). It then calls findMinimumAssignment to find a 1:1 mapping between the two (padded) lists of functions. The return value represents the 1:1 mapping.

Since findMinimumAssignment only works if ROSE is configured with dlib support, this function throws an Exception if that support is missing.

◆ computeDistances()

std::vector< double > Rose::BinaryAnalysis::FunctionSimilarity::computeDistances	(	const std::vector< Partitioner2::FunctionPtr > &	list1,
		const std::vector< Partitioner2::FunctionPtr > &	list2,
		size_t	nThreads
	)		const

Compute distances between sets of functions.

This is a low-level function to compute the distance between all pairs of functions from list1 and list2 in parallel. The return value contains the distances so that the distance between the function list1[i] and list2[j] is at index i * list2.size() + j in the return value.

◆ sortByIncreasingDistance()

static bool Rose::BinaryAnalysis::FunctionSimilarity::sortByIncreasingDistance	(	const FunctionDistancePair &	a,
		const FunctionDistancePair &	b
	)

inlinestatic

Predicate for sorting by ascending distance.

Definition at line 419 of file FunctionSimilarity.h.

◆ sortByDecreasingDistance()

static bool Rose::BinaryAnalysis::FunctionSimilarity::sortByDecreasingDistance	(	const FunctionDistancePair &	a,
		const FunctionDistancePair &	b
	)

inlinestatic

Predicate for sorting by descending distance.

Definition at line 424 of file FunctionSimilarity.h.

◆ sortByAddress()

static bool Rose::BinaryAnalysis::FunctionSimilarity::sortByAddress	(	const FunctionDistancePair &	a,
		const FunctionDistancePair &	b
	)

inlinestatic

Predicate for sorting by function address.

Definition at line 429 of file FunctionSimilarity.h.

◆ initDiagnostics()

static void Rose::BinaryAnalysis::FunctionSimilarity::initDiagnostics ( )

static

Initializes and registers disassembler diagnostic streams.

See Diagnostics::initialize().

◆ printCharacteristicValues()

void Rose::BinaryAnalysis::FunctionSimilarity::printCharacteristicValues ( std::ostream & ) const

Print characteristic values for this analysis.

This is a multi-line output intended for debugging.

◆ findMinimumAssignment()

static std::vector< size_t > Rose::BinaryAnalysis::FunctionSimilarity::findMinimumAssignment ( const DistanceMatrix & )

static

Find minimum mapping from rows to columns.

Finds a 1:1 mapping from rows to columns of the specified square matrix such that the total cost is minimized. Returns a vector V such that V[i] = j maps rows i to columns j.

This function will only work if ROSE has been compiled with dlib support. Otherwise it throws an Exception.

◆ totalAssignmentCost()

static double Rose::BinaryAnalysis::FunctionSimilarity::totalAssignmentCost	(	const DistanceMatrix &	,
		const std::vector< size_t > &	assignment
	)

static

Total cost of a mapping.

Given a square matrix and a 1:1 mapping from rows to columns, return the total cost of the mapping. The assignment is like the value returned by findMinimumAssignment.

Member Data Documentation

◆ NO_CATEGORY

const CategoryId Rose::BinaryAnalysis::FunctionSimilarity::NO_CATEGORY = -1

static

Invalid category ID.

Definition at line 88 of file FunctionSimilarity.h.

Referenced by findCategory().

◆ mlog

Sawyer::Message::Facility Rose::BinaryAnalysis::FunctionSimilarity::mlog

static

Diagnostic streams.

Definition at line 97 of file FunctionSimilarity.h.

The documentation for this class was generated from the following file:

FunctionSimilarity.h

Description

Definitions

Queries

Classes

Public Types

Public Member Functions

Static Public Member Functions

Static Public Attributes

Member Typedef Documentation

◆ CartesianPoint

◆ OrderedList

◆ PointCloud

◆ OrderedLists

◆ CategoryId

◆ FunctionDistancePair

◆ FunctionPair

◆ DistanceMatrix

Member Enumeration Documentation

◆ CValKind

◆ Statistic

Constructor & Destructor Documentation

◆ FunctionSimilarity()

Member Function Documentation

◆ clear()

◆ categoryAccumulatorType() [1/2]

◆ categoryAccumulatorType() [2/2]

◆ progress()

◆ declarePointCategory()

◆ declareListCategory()

◆ nCategories()

◆ findCategory()

◆ declareCfgConnectivity()

◆ measureCfgConnectivity()

◆ declareCallGraphConnectivity()

◆ measureCallGraphConnectivity()

◆ declareMnemonicStream()

◆ measureMnemonicStream()

◆ insertPoint()

◆ insertList()

◆ compare()

◆ compareOneToAll()

◆ compareOneToMany() [1/3]

◆ compareOneToMany() [2/3]

◆ compareOneToMany() [3/3]

◆ compareManyToMany()

◆ compareManyToManyMatrix()

◆ findMinimumCostMapping()

◆ computeDistances()

◆ sortByIncreasingDistance()

◆ sortByDecreasingDistance()

◆ sortByAddress()

◆ initDiagnostics()

◆ printCharacteristicValues()

◆ findMinimumAssignment()

◆ totalAssignmentCost()

Member Data Documentation

◆ NO_CATEGORY

◆ mlog