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CS590Z
Problem Statement
Suppose a program P’ is created by modifying P. Determine the difference between P and P’. For an artifact c’ in P’, decide if c’ belongs to the difference, if not, find the correspondence of c’ in P.
• Static mapping• Non-trivial
Name comparison? What if
• Clone analysis, comparison checking
CS590Z
Motivations
Validate compiler transformations
Facilitate regression testing
Reverse obfuscation
Information propagation
Debugging
Code plagiarism detection
Information Assurance
CS590Z
Approaches
Static Approaches• Entity name based• String based (MOSS)• AST based (DECKARD)• CFG based (JDIFF)• PDG based (PDIFF)• Binary based (BMAT)• Log based (editor plugin, comparison checking)
Dynamic Approaches (not today)
CS590Z
Static Approaches
Entity name matching• Model a function/field as tuples• Coarse grained matching
String matching• Diff (CVS, Subservion)• Longest common subsequence (LCS)
Available operations are addition and deletion Matched pairs can not cross one another Programs are far more complicated than strings
Copy, paste, move• CP-Miner (scale to linux kernel clone detection)
Frequent subsequence mining
CS590Z
MOSS
Code plagiarism detection• It also handles other digital contents
Challenges• White space (variable name)• Noise (“the”, “int i”);• Order scrambling (paragraph reorders)
Problem statement• Given a set of documents, identify substring matches that
satisfy two properties: If there is a substring match at least as long as the guarantee threshold
t, then this match is detected; Do not detect any matches shorter than the noise threshold, k.
CS590Z
MOSS
Incremental hashing• Hashing strings of length k is expensive for large k.• “rolling” hash function
The (i+1)th k-gram hash = F (the ith k-gram hash, …)
CS590Z
MOSS
Fingerprint selection• A subset of hash values
• Our goals: find all matching substrings >t; ignore matchings <k)
• One of every tth hash values• 0 mod p
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MOSS
Winnowing• Observation: given a sequence of hashes h1,…hn, if n>t-k,
then at least one of the hi must be chosen• Have a sliding window with size w=t-k+1• In each window select the minimum hash value, break ties
by select the rightmost occurrence.
CS590Z
MOSS
Algorithm• Build an index mapping fingerprints to locations for all
documents.• Each document is fingerprinted a second time and the
selected fingerprints are looked up in the index; this gives the list of all matching fingerprints for each document.
• Sort (d,d1,fx), (d, d2,fy) by the first two elements. • Matches between documents are rank-ordered by size
(number of fingerprints)
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MOSS
Advantages• Guarantee to detect any >t substring matches
Limitations• Minor edits fail MOSS.
x= a*b + c vs. z= c + a*b• Insertion, deletion
CS590Z
AST based matching
[YANG, 1991, Software Practice and Experience]• Given two functions, build the ASTs• Match the roots• If so, apply LCS to align subtrees• Continue recursively
Fragile
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DECKARD
Advantages• Scalability• Insensitive to minor structural changes such as reordering,
insertion, deletion
Limitations• Structural similarity only• Insertion that incurs structure change.
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CFG matching
Hammock graph (JDIFF ,ASE 2004)• Match classes by names• Match fields by types• Match methods by signatures• Match instruction in methods by hammock graphs
A hammock is a single entry single exit subgraph of a CFG.
CS590Z
CFG matching
Pros• Orthogonal
Can be combined with other matching techniques• Simple
Cons• Coarse grained matching only
Not good at clone detection• In case of code transformation
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Semantic Based
Pros• Non-contiguous, intertwined, reordered• Insensitive to code transformations.
Cons• Scalability
Points-to analysis• Starting from a matching pair seems to be a problem
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