Unsupervised Machine Learning for clone detection
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This document discusses unsupervised machine learning techniques for clone detection in source code. It begins by defining different types of code clones and describing current state-of-the-art clone detection tools. It then argues that machine learning approaches, such as using kernel methods to compare abstract syntax trees, can provide more computationally efficient and accurate clone detection compared to traditional text-, token-, and syntax-based techniques. The document provides examples of using kernel functions to compute similarities between code structure representations like ASTs to enable unsupervised machine learning for clone detection.
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Unsupervised Machine Learning for clone detection
- 1. UNSUPERVISED MACHINE LEARNING FOR CLONE DETECTION Valerio Maggio, Ph.D. June 25, 2013 valerio.maggio@unina.it
- 2. General Disclaimer: All the Maths appearing in the next slides is only intended to better introduce the considered case studies. Speakers are not responsible for any possible disease or “brain consumption” caused by too much formulas. So BEWARE; use this information at your own risk! It's intention is solely educational. We would strongly encourage you to use this information in cooperation with a medical or health professional. AwfulMaths
- 3. Number one in the stink parade is duplicated code. If you see the same code structure in more than one place, you can be sure that your program will be better if you find a way to unify them.
- 5. CPYTHON2.5.1
- 7. PROBL EM S T A T E M E N T CLONE DETECTION Software clones are fragments of code that are similar according to some predefined measure of similarity I.D. Baxter, 1998
- 8. PROBL EM S T A T E M E N T CLONE DETECTION
- 9. PROBL EM S T A T E M E N T CLONE DETECTION Clones Textual Similarity
- 10. PROBL EM S T A T E M E N T CLONE DETECTION Clones Functional Similarity
- 11. PROBL EM S T A T E M E N T CLONE DETECTION Clones affect the reliability of the system! Sneaky Bug!
- 13. THE ORIGINAL ONE # Original Fragment def do_something_cool_in_Python(filepath, marker='---end---'): ! lines = list() ! with open(filepath) as report: ! ! for l in report: ! ! ! if l.endswith(marker): ! ! ! ! lines.append(l) # Stores only lines that ends with "marker" ! return lines #Return the list of different lines
- 14. TYPE 1: Exact Copy • Identical code segments except for differences in layout, whitespace, and comments
- 15. def do_something_cool_in_Python (filepath, marker='---end---'): ! lines = list() # This list is initially empty ! with open(filepath) as report: ! ! for l in report: # It goes through the lines of the file ! ! ! if l.endswith(marker): ! ! ! ! lines.append(l) ! return lines TYPE 1: Exact Copy • Identical code segments except for differences in layout, whitespace, and comments # Original Fragment def do_something_cool_in_Python(filepath, marker='---end---'): ! lines = list() ! with open(filepath) as report: ! ! for l in report: ! ! ! if l.endswith(marker): ! ! ! ! lines.append(l) # Stores only lines that ends with "marker" ! return lines #Return the list of different lines
- 16. TYPE 2: Parameter Substituted • Structurally identical segments except for differences in identifiers, literals, layout, whitespace, and comments
- 17. # Type 2 Clone def do_something_cool_in_Python(path, end='---end---'): ! targets = list() ! with open(path) as data_file: ! ! for t in datae: ! ! ! if l.endswith(end): ! ! ! ! targets.append(t) # Stores only lines that ends with "marker" ! #Return the list of different lines ! return targets # Original Fragment def do_something_cool_in_Python(filepath, marker='---end---'): ! lines = list() ! with open(filepath) as report: ! ! for l in report: ! ! ! if l.endswith(marker): ! ! ! ! lines.append(l) # Stores only lines that ends with "marker" ! return lines #Return the list of different lines TYPE 2: Parameter Substituted • Structurally identical segments except for differences in identifiers, literals, layout, whitespace, and comments
- 18. TYPE 3: Structure Substituted • Similar segments with further modifications such as changed, added (or deleted) statements, in additions to variations in identifiers, literals, layout and comments
- 19. import os def do_something_with(path, marker='---end---'): ! # Check if the input path corresponds to a file ! if not os.path.isfile(path): ! ! return None ! bad_ones = list() ! good_ones = list() ! with open(path) as report: ! ! for line in report: ! ! ! line = line.strip() ! ! ! if line.endswith(marker): ! ! ! ! good_ones.append(line) ! ! ! else: ! ! ! ! bad_ones.append(line) ! #Return the lists of different lines ! return good_ones, bad_ones TYPE 3: Structure Substituted • Similar segments with further modifications such as changed, added (or deleted) statements, in additions to variations in identifiers, literals, layout and comments
- 20. import os def do_something_with(path, marker='---end---'): ! # Check if the input path corresponds to a file ! if not os.path.isfile(path): ! ! return None ! bad_ones = list() ! good_ones = list() ! with open(path) as report: ! ! for line in report: ! ! ! line = line.strip() ! ! ! if line.endswith(marker): ! ! ! ! good_ones.append(line) ! ! ! else: ! ! ! ! bad_ones.append(line) ! #Return the lists of different lines ! return good_ones, bad_ones TYPE 3: Structure Substituted • Similar segments with further modifications such as changed, added (or deleted) statements, in additions to variations in identifiers, literals, layout and comments
- 21. import os def do_something_with(path, marker='---end---'): ! # Check if the input path corresponds to a file ! if not os.path.isfile(path): ! ! return None ! bad_ones = list() ! good_ones = list() ! with open(path) as report: ! ! for line in report: ! ! ! line = line.strip() ! ! ! if line.endswith(marker): ! ! ! ! good_ones.append(line) ! ! ! else: ! ! ! ! bad_ones.append(line) ! #Return the lists of different lines ! return good_ones, bad_ones TYPE 3: Structure Substituted • Similar segments with further modifications such as changed, added (or deleted) statements, in additions to variations in identifiers, literals, layout and comments
- 22. import os def do_something_with(path, marker='---end---'): ! # Check if the input path corresponds to a file ! if not os.path.isfile(path): ! ! return None ! bad_ones = list() ! good_ones = list() ! with open(path) as report: ! ! for line in report: ! ! ! line = line.strip() ! ! ! if line.endswith(marker): ! ! ! ! good_ones.append(line) ! ! ! else: ! ! ! ! bad_ones.append(line) ! #Return the lists of different lines ! return good_ones, bad_ones TYPE 3: Structure Substituted • Similar segments with further modifications such as changed, added (or deleted) statements, in additions to variations in identifiers, literals, layout and comments
- 23. TYPE 4: “Functional” Copies • Semantically equivalent segments that perform the same computation but are implemented by different syntactic variants
- 24. # Original Fragment def do_something_cool_in_Python(filepath, marker='---end---'): ! lines = list() ! with open(filepath) as report: ! ! for l in report: ! ! ! if l.endswith(marker): ! ! ! ! lines.append(l) # Stores only lines that ends with "marker" ! return lines #Return the list of different lines def do_always_the_same_stuff(filepath, marker='---end---'): ! report = open(filepath) ! file_lines = report.readlines() ! report.close() ! #Filters only the lines ending with marker ! return filter(lambda l: len(l) and l.endswith(marker), file_lines) TYPE 4: “Functional” Copies • Semantically equivalent segments that perform the same computation but are implemented by different syntactic variants
- 31. SOURCECODEINFORMATION FUNCTION parser_compare PARAMS PARAMPARAM node *left node *right IF-STMT IF-STMT RETURN-STMT BODY CALL-STMT parser_compare_node PARAMS STRUCT-OP right st_nodeleft st_node BODY BODYCOND COND OR ==== left right0 0 == rightleft RETURN- STMTRETURN-STMT 00
- 32. SOURCECODEINFORMATION ENTRY EXIT FORMAL-IN ACTUAL-IN ACTUAL-IN FORMAL-IN BODY CONTROL-POINT EXPR CONTROL-POINT CONTROL-POINT CALL-SITE RETURN ACTUAL-OUT RETURN EXPR EXPR FORMAL-OUT
- 34. Duplix Scorpio PMD CCFinder Dup CPD Duplix Shinobi Clone Detective Gemini iClones KClone ConQAT Deckard Clone Digger JCCD CloneDr SimScan CLICS NiCAD Simian Duploc Dude SDD Text Based Tools: Text is compared line by line STATEOFTHEARTTOOLS
- 35. Duplix Scorpio PMD CCFinder Dup CPD Duplix Shinobi Clone Detective Gemini iClones KClone ConQAT Deckard Clone Digger JCCD CloneDr SimScan CLICS NiCAD Simian Duploc Dude SDD Token Based Tools: Token sequences are compared to sequences STATEOFTHEARTTOOLS
- 36. Duplix Scorpio PMD CCFinder Dup CPD Duplix Shinobi Clone Detective Gemini iClones KClone ConQAT Deckard Clone Digger JCCD CloneDr SimScan CLICS NiCAD Simian Duploc Dude SDD Syntax Based Tools: Syntax subtrees are compared to each other STATEOFTHEARTTOOLS
- 37. Duplix Scorpio PMD CCFinder Dup CPD Duplix Shinobi Clone Detective Gemini iClones KClone ConQAT Deckard Clone Digger JCCD CloneDr SimScan CLICS NiCAD Simian Duploc Dude SDD Graph Based Tools: (sub) graphs are compared to each other STATEOFTHEARTTOOLS
- 38. • String/Token based Techniques: • Pros: Run very fast • Cons: Too many false clones STATEOFTHEART TECHNIQUES
- 39. • String/Token based Techniques: • Pros: Run very fast • Cons: Too many false clones • Syntax based (AST) Techniques: • Pros: Well suited to detect structural similarities • Cons: Not Properly suited to detect Type 3 Clones STATEOFTHEART TECHNIQUES
- 40. • String/Token based Techniques: • Pros: Run very fast • Cons: Too many false clones • Syntax based (AST) Techniques: • Pros: Well suited to detect structural similarities • Cons: Not Properly suited to detect Type 3 Clones • Graph based Techniques: • Pros: The only one able to deal with Type 4 Clones • Cons: Performance Issues STATEOFTHEART TECHNIQUES
- 42. USE MACHINE LEARNING L U K E • Provides computational effective solutions to analyze large data sets
- 43. USE MACHINE LEARNING L U K E • Provides computational effective solutions to analyze large data sets • Provides solutions that can be tailored to different tasks/domains
- 44. USE MACHINE LEARNING L U K E • Provides computational effective solutions to analyze large data sets • Provides solutions that can be tailored to different tasks/domains • Requires many efforts in:
- 45. USE MACHINE LEARNING L U K E • Provides computational effective solutions to analyze large data sets • Provides solutions that can be tailored to different tasks/domains • Requires many efforts in: • the definition of the relevant information best suited for the specific task/domain
- 46. USE MACHINE LEARNING L U K E • Provides computational effective solutions to analyze large data sets • Provides solutions that can be tailored to different tasks/domains • Requires many efforts in: • the definition of the relevant information best suited for the specific task/domain • the application of the learning algorithms to the considered data
- 47. UNSUPERVISEDLEARNING • Supervised Learning: • Learn from labelled samples • Unsupervised Learning: • Learn (directly) from the data Learn by examples
- 48. UNSUPERVISEDLEARNING • Supervised Learning: • Learn from labelled samples • Unsupervised Learning: • Learn (directly) from the data Learn by examples (+) No cost of labeling samples (-) Trade-off imposed on the quality of the data
- 49. CODE STRUCTURES KERNELSFORSTRUCTURES Computation of the dot product between (Graph) Structures K( ),
- 50. CODE STRUCTURES KERNELSFORSTRUCTURES Abstract Syntax Tree (AST) Tree structure representing the syntactic structure of the different instructions of a program (function) Program Dependencies Graph (PDG) (Directed) Graph structure representing the relationship among the different statement of a program Computation of the dot product between (Graph) Structures K( ),
- 52. < x y = = x + x 1 y - y 1 while block while block block if > b a = = a + a 1 b - b 1 > b 0 = c 3 CODE AST KERNELFORCLONES
- 53. < x y = = x + x 1 y - y 1 while block while block block if > b a = = a + a 1 b - b 1 > b 0 = c 3 CODE AST AST KERNEL KERNELFORCLONES < block while = = block = y - = x + + x 1 - y 1 < x y > b 0 = c 3 if block > b a - b 1 < block while + a 1 = b - = a +
- 55. while block< x y KERNELS FOR CODE STRUCTURES: AST KERNELFEATURES Instruction Class (IC) i.e., LOOP, CALL, CONDITIONAL_STATEMENT
- 56. while block< x y KERNELS FOR CODE STRUCTURES: AST KERNELFEATURES Instruction Class (IC) i.e., LOOP, CALL, CONDITIONAL_STATEMENT Instruction (I) i.e., FOR, IF, WHILE, RETURN
- 57. while block< x y KERNELS FOR CODE STRUCTURES: AST KERNELFEATURES Instruction Class (IC) i.e., LOOP, CALL, CONDITIONAL_STATEMENT Instruction (I) i.e., FOR, IF, WHILE, RETURN Context (C) i.e., Instruction Class of the closer statement node
- 58. while block< x y KERNELS FOR CODE STRUCTURES: AST KERNELFEATURES Instruction Class (IC) i.e., LOOP, CALL, CONDITIONAL_STATEMENT Instruction (I) i.e., FOR, IF, WHILE, RETURN Context (C) i.e., Instruction Class of the closer statement node Lexemes (Ls) Lexical information gathered (recursively) from leaves
- 59. while block< x y KERNELS FOR CODE STRUCTURES: AST KERNELFEATURES IC = Conditional-Expr I = Less-operator C = Loop Ls= [x,y] IC = Loop I = while-loop C = Function-Body Ls= [x, y] Instruction Class (IC) i.e., LOOP, CALL, CONDITIONAL_STATEMENT Instruction (I) i.e., FOR, IF, WHILE, RETURN Context (C) i.e., Instruction Class of the closer statement node Lexemes (Ls) Lexical information gathered (recursively) from leaves IC = Block I = while-body C = Loop Ls= [ x ]
- 60. CLONE DETECTION • Comparison with another (pure) AST-based clone detector • Comparison on a system with randomly seeded clones 0 0.25 0.5 0.75 1 Precision Recall F-measure CloneDigger Tree Kernel Tool RE SULTS Results refer to clones where code fragments have been modified by adding/ removing or changing code statements
- 61. 0 0.25 0.50 0.75 1.00 0.6 0.62 0.64 0.66 0.68 0.7 0.72 0.74 0.76 0.78 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 Precision, Recall and F-Measure Precision Recall F1 Precision: How accurate are the obtained results? (Altern.) How many errors do they contain? Recall: How complete are the obtained results? (Altern.) How many clones have been retrieved w.r.t. Total Clones?
- 62. CODE STRUCTURES PDG NODES AND EDGES while call-site argexpr
- 63. CODE STRUCTURES PDG • Two Types of Nodes • Control Nodes (Dashed ones) • e.g., if - for - while - function calls... • Data Nodes • e.g., expressions - parameters... NODES AND EDGES while call-site argexpr
- 64. CODE STRUCTURES PDG • Two Types of Nodes • Control Nodes (Dashed ones) • e.g., if - for - while - function calls... • Data Nodes • e.g., expressions - parameters... • Two Types of Edges (i.e., dependencies) • Control edges (Dashed ones) • Data edges NODES AND EDGES while call-site argexpr
- 65. • Features of nodes: • Node Label • i.e., , WHILE, CALL-SITE, EXPR, ... • Node Type • i.e., Data Node or Control Node • Features of edges: • Edge Type • i.e., Data Edge or Control Edge KERNELS FOR CODE STRUCTURES: PDG GRAPH KERNELS FOR PDG while call-site arg expr expr
- 66. • Features of nodes: • Node Label • i.e., , WHILE, CALL-SITE, EXPR, ... • Node Type • i.e., Data Node or Control Node • Features of edges: • Edge Type • i.e., Data Edge or Control Edge KERNELS FOR CODE STRUCTURES: PDG Node Label = WHILE Node Type = Control Node GRAPH KERNELS FOR PDG while call-site arg expr expr Control Edge Data Edge
- 67. while call-site arg expr expr while call-site arg expr call-site GRAPH KERNELS FOR PDG • Goal: Identify common subgraphs • Selectors: Compare nodes to each others and explore the subgraphs of only “compatible” nodes (i.e., Nodes of the same type) • Context: The subgraph of a node (with paths whose lengths are at most L to avoid loops)
- 68. while call-site arg expr expr while call-site arg expr call-site GRAPH KERNELS FOR PDG • Goal: Identify common subgraphs • Selectors: Compare nodes to each others and explore the subgraphs of only “compatible” nodes (i.e., Nodes of the same type) • Context: The subgraph of a node (with paths whose lengths are at most L to avoid loops)
- 69. while call-site arg expr expr while call-site arg expr call-site GRAPH KERNELS FOR PDG • Goal: Identify common subgraphs • Selectors: Compare nodes to each others and explore the subgraphs of only “compatible” nodes (i.e., Nodes of the same type) • Context: The subgraph of a node (with paths whose lengths are at most L to avoid loops)
- 70. while call-site arg expr expr while call-site arg expr call-site GRAPH KERNELS FOR PDG • Goal: Identify common subgraphs • Selectors: Compare nodes to each others and explore the subgraphs of only “compatible” nodes (i.e., Nodes of the same type) • Context: The subgraph of a node (with paths whose lengths are at most L to avoid loops)
- 74. PROBL EM S T A T E M E N T (MODEL) CLONE DETECTION Models: models are typically represented visually, as box-and-arrow diagrams, and the clones we are searching for are similar subgraphs of these diagrams. Model Granularity: models could be represented at different levels of granularity (such as the source code) corresponding to different syntactic (and semantic) units. Models Clones are categorized in (three) different Types
- 75. REFERENCEEXAMPLE
- 76. TYPE 1C L O N E S (MODEL) CLONE DETECTION • Type 1 (exact) model clones: Identical model fragments except for variations in visual presentation, layout and formatting.
- 77. TYPE 2C L O N E S (MODEL) CLONE DETECTION Type 2 (renamed) model clones: Structurally identical model fragments except for variations in labels, values, types, visual presentation, layout and formatting. model@Friction Mode Logic/Break Apart Detection model@Friction Mode Logic/Lockup Detection/Required Friction for Lockup
- 78. TYPE 3C L O N E S (MODEL) CLONE DETECTION Type 3 (near-miss) model clones: Model fragments with further modifications, such as changes in position or connection with respect to other model fragments and small additions or removals of blocks or lines in addition to variations in labels, values, types, visual presentation, layout and formatting. model@Speed.speed_estimation model@Throttle.throttle_estimation
- 80. THANK YOU Valerio Maggio Ph.D., University of Naples “Federico II” valerio.maggio@unina.it