Improving Software Maintenance using Unsupervised Machine Learning techniques - Ph.D. Defence Dissertation

Ph.D. Dissertation for the Thesis entitled "Improving Software Maintenance using Unsupervised Machine Learning techniques"

Unsupervised Machine Learning techniques have been used to face different software maintenance issues such as Software Modularisation and Clone detection.

1. Dottorato in Scienze Computazionali e Informatiche, XXV Ciclo Ph.D. Candidate: Valerio Maggio Thesis Advisors: Dr. Sergio Di Martino Dr. Anna Corazza June 5th, 2013 UNSUPERVISED MACHINE LEARNING FOR SOFTWARE MAINTENANCE
2. THESIS G O A L UNSUPERVISED MACHINE LEARNING FOR SOFTWARE MAINTENANCE These solutions exploit (unsupervised) machine learning techniques to mine information from the source code Define and experimentally evaluate solutions (techniques and prototype tools) for automatic software analysis to support software maintenance activities.
3. There exist different types of Software Maintenance. SOFTWARE MAINTENANCE “A software system must be continually adapted during its overall life cycle or it progressively becomes less satisfactory.” (cit. Lehman’s First Law of Software Evolution)
4. SOFTWARE MAINTENANCE “A software system must be continually adapted during its overall life cycle or it progressively becomes less satisfactory.” (cit. Lehman’s First Law of Software Evolution) Software Maintenance represents the most expensive, time consuming and challenging phase of the whole development process.
5. SOFTWARE MAINTENANCE “A software system must be continually adapted during its overall life cycle or it progressively becomes less satisfactory.” (cit. Lehman’s First Law of Software Evolution) Software Maintenance represents the most expensive, time consuming and challenging phase of the whole development process. Software Maintenance could account up to the 85-90% of the total software costs.
6. ISSUES SOFTWARE MAINTENANCE “Software Maintenance is about change!” (cit. S. Jarzabek) Change Analysis
7. ISSUES SOFTWARE MAINTENANCE “Software Maintenance is about change!” (cit. S. Jarzabek) Change Analysis Program Comprehension The documentation is usually scarce or not up to date!
8. ISSUES SOFTWARE MAINTENANCE The source code (usually) represents the most reliable source of information about the system “Software Maintenance is about change!” (cit. S. Jarzabek) Change Analysis Program Comprehension The documentation is usually scarce or not up to date!
9. ISSUES SOFTWARE MAINTENANCE The source code (usually) represents the most reliable source of information about the system “Software Maintenance is about change!” (cit. S. Jarzabek) Change Analysis Program Comprehension Reverse Engineering The documentation is usually scarce or not up to date!
10. REVERSE E N G I N E E R I N G • Definition of tools and techniques to support maintenance activities • Goal: Build higher-level software models in an automatic fashion gathering information from the source code or any other document • Goal: To aid the comprehension of the system
11. REVERSE E N G I N E E R I N G • Definition of tools and techniques to support maintenance activities • Goal: Build higher-level software models in an automatic fashion gathering information from the source code or any other document • Goal: To aid the comprehension of the system STATIC ANALYSIS DYNAMIC ANALYSIS
12. REVERSE E N G I N E E R I N G • Definition of tools and techniques to support maintenance activities • Goal: Build higher-level software models in an automatic fashion gathering information from the source code or any other document • Goal: To aid the comprehension of the system STATIC ANALYSIS DYNAMIC ANALYSIS
13. MACHINE L E A R N I N G • Provides computational effective solutions to analyze large data sets
14. MACHINE L E A R N I N G • Provides computational effective solutions to analyze large data sets • Provides solutions that can be tailored to different tasks/domains
15. MACHINE L E A R N I N G • 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
16. MACHINE L E A R N I N G • 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
17. UNSUPERVISED LEARNING • Supervised Learning: • Learn from labelled samples • Unsupervised Learning: • Learn (directly) from the data Learn by examples
18. UNSUPERVISED LEARNING • 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
19. THESIS C O N T R I B U T I O N S &
20. THESIS C O N T R I B U T I O N S Contributions to three relevant and related open issues in Software Maintenance &
21. THESIS C O N T R I B U T I O N S Contributions to three relevant and related open issues in Software Maintenance 1. SOFTWARE RE-MODULARIZATION 3. CLONE DETECTION 2. SOURCE CODE NORMALIZATION &
22. ISSUE #1 SOFTWARE RE-MODULARIZATION
23. Software Classes UI Process Components UI Components Data Access Components Data Helpers / Utilities Security Operational Management Communications Business Components Application Facade Buisiness Workflows Messages Interfaces Service Interfaces Re-modularization provides a way to support software maintainers by automatically grouping together (clustering) “related” software classes SOFTWARE RE-MODULARIZATION PROBL EM S T A T E M E N T
24. Re-modularization provides a way to support software maintainers by automatically grouping together (clustering) “related” software classes SOFTWARE RE-MODULARIZATION External Systems Service Consumers Services Service Interfaces Messages Interfaces Cross Cutting Security Operational Management Communications Data Data Access Components Data Helpers / Utilities Presentation UI Components UI Process Components Business Application Facade Buisiness Workflows Business Components Clusters of Software Classes PROBL EM S T A T E M E N T
25. SOURCE CODE LEXICAL INFORMATION
26. SOURCE CODE LEXICAL INFORMATION
27. SOURCE CODE LEXICAL INFORMATION (IDEA): Exploit the lexical information gathered from the source code to produce clusters of classes that are lexically related.
28. SOURCE CODE LEXICAL INFORMATION (IDEA): Exploit the lexical information gathered from the source code to produce clusters of classes that are lexically related. State of the Art: Information Retrieval (IR) based approaches.
29. IR INDEXING
30. Implicit assumption: The “same” words are used whenever a particular concept occurs IR INDEXING
31. 1. Tokenization Draws, the, are, NullHandle, box, r, Rectangle, g, Graphics, box, displayBox, ... Implicit assumption: The “same” words are used whenever a particular concept occurs IR INDEXING
32. 1. Tokenization 2. Normalization draw, the, are, null, handl, box, r, rectangl, g, graphic, box, display, box, ... Draws, the, are, NullHandle, box, r, Rectangle, g, Graphics, box, displayBox, ... Implicit assumption: The “same” words are used whenever a particular concept occurs IR INDEXING
33. SOURCE CODE ZONES LEXICAL INFORMATION
34. SOURCE CODE ZONES LEXICAL INFORMATION Class Names
35. SOURCE CODE ZONES LEXICAL INFORMATION Class Names Attribute Names
36. SOURCE CODE ZONES LEXICAL INFORMATION Class Names Attribute Names Method Names Method Names
37. SOURCE CODE ZONES LEXICAL INFORMATION Class Names Attribute Names Method Names Parameter Names Method Names Parameter Names
38. SOURCE CODE ZONES LEXICAL INFORMATION Class Names Attribute Names Method Names Parameter Names Comments Comments Method Names Parameter Names Comments
39. SOURCE CODE ZONES LEXICAL INFORMATION Source Code Class Names Attribute Names Method Names Parameter Names Comments Comments Method Names Parameter Names Source Code Comments
40. ZONE INDEXING
41. ZONE INDEXING RQ1: Do terms in different Zones provide different contributions?
42. 1. Tokenization 2. Normalization ZONE INDEXING Draws, the, are, NullHandle, box, r, draw, g, Graphics, color, displayBox, ... draw, the, are, null, handl, box, r, draw, g, graphic, color, display, box, ... RQ1: Do terms in different Zones provide different contributions?
43. SOURCE CODE LEXICON JFreeChart JEdit JUnit Xerces
44. SOURCE CODE LEXICON JFreeChart • Good lexicon in every Zone!
45. SOURCE CODE LEXICON JEdit • Very good in Comments • Very poor in Method and Parameter names
46. SOURCE CODE LEXICON JUnit • Good in Class and Attribute names • No Comments at all
47. SOURCE CODE LEXICON Xerces • Poor in Method, Parameter names and Comments • Good in Method Code and Variable names
48. SOURCE CODE LEXICON JFreeChart JEdit JUnit Xerces RQ2: How to automatically weight the different contributions ?
49. CONTRI BUTION SOFTWARE RE- MODULARIZATION • We propose a source code Zone Indexing Corazza A., Di Martino, S., Maggio, V., Scanniello, G. Investigating the use of lexical information for software system clustering (2011) Proceedings of the European Conference on Software Maintenance and Reengineering, CSMR, art. no. 5741257, pp. 35-44. ISSN: 15345351 ISBN: 978-076954343-7 Corazza, A., Di Martino, S., Maggio, V., Scanniello, G. Combining machine learning and information retrieval techniques for software clustering (2012) Communications in Computer and Information Science, 255 CCIS, pp. 42-60. ISSN: 18650929 ISBN: 978-364228032-0
50. CONTRI BUTION SOFTWARE RE- MODULARIZATION • We propose a source code Zone Indexing • An approach to automatically weight the contribution of different terms in Zones • Maximum-Likelihood Estimation (MLE) Approach Corazza A., Di Martino, S., Maggio, V., Scanniello, G. Investigating the use of lexical information for software system clustering (2011) Proceedings of the European Conference on Software Maintenance and Reengineering, CSMR, art. no. 5741257, pp. 35-44. ISSN: 15345351 ISBN: 978-076954343-7 Corazza, A., Di Martino, S., Maggio, V., Scanniello, G. Combining machine learning and information retrieval techniques for software clustering (2012) Communications in Computer and Information Science, 255 CCIS, pp. 42-60. ISSN: 18650929 ISBN: 978-364228032-0
51. CONTRI BUTION SOFTWARE RE- MODULARIZATION • We propose a source code Zone Indexing • An approach to automatically weight the contribution of different terms in Zones • Maximum-Likelihood Estimation (MLE) Approach • A variant of the K-medoid clustering algorithm • Tailored to the software clustering task Corazza A., Di Martino, S., Maggio, V., Scanniello, G. Investigating the use of lexical information for software system clustering (2011) Proceedings of the European Conference on Software Maintenance and Reengineering, CSMR, art. no. 5741257, pp. 35-44. ISSN: 15345351 ISBN: 978-076954343-7 Corazza, A., Di Martino, S., Maggio, V., Scanniello, G. Combining machine learning and information retrieval techniques for software clustering (2012) Communications in Computer and Information Science, 255 CCIS, pp. 42-60. ISSN: 18650929 ISBN: 978-364228032-0
52. StackedVerticalBarRenderer LineAndShapeRenderer K-MEDOID VARIANT ArrayHolder Plot LinearPlotFitAlgorithm LinearPlotFit HorizontalNumberAxis NumberAxisPropertyEditPanel PlotFitAlgorithm ChartFrame GraphicsHolder PropertyEditPanel DateAxisPropertyEditPanel StandardLegend LegendItem FigureLegend VerticalNumberAxis VerticalXYDataPlot DataPlot SampleXYDataset SampleXYDatasetThread Non-extreme Distribution property VerticalPlot FitAlgorithm
53. StackedVerticalBarRenderer LineAndShapeRenderer K-MEDOID VARIANT ArrayHolder Plot LinearPlotFitAlgorithm LinearPlotFit HorizontalNumberAxis NumberAxisPropertyEditPanel PlotFitAlgorithm ChartFrame GraphicsHolder PropertyEditPanel DateAxisPropertyEditPanel StandardLegend LegendItem FigureLegend VerticalNumberAxis VerticalXYDataPlot DataPlot SampleXYDataset SampleXYDatasetThread Non-extreme Distribution property VerticalPlot FitAlgorithm
54. StackedVerticalBarRenderer LineAndShapeRenderer K-MEDOID VARIANT ArrayHolder Plot LinearPlotFitAlgorithm LinearPlotFit HorizontalNumberAxis NumberAxisPropertyEditPanel PlotFitAlgorithm ChartFrame GraphicsHolder PropertyEditPanel DateAxisPropertyEditPanel StandardLegend LegendItem FigureLegend VerticalNumberAxis VerticalXYDataPlot DataPlot SampleXYDataset SampleXYDatasetThread Non-extreme Distribution property VerticalPlot FitAlgorithm Extreme Extreme
55. StackedVerticalBarRenderer LineAndShapeRenderer K-MEDOID VARIANT ArrayHolder Plot LinearPlotFitAlgorithm LinearPlotFit HorizontalNumberAxis NumberAxisPropertyEditPanel PlotFitAlgorithm ChartFrame GraphicsHolder PropertyEditPanel DateAxisPropertyEditPanel StandardLegend LegendItem FigureLegend VerticalNumberAxis VerticalXYDataPlot DataPlot SampleXYDataset SampleXYDatasetThread Non-extreme Distribution property VerticalPlot FitAlgorithm
56. AUTHORITATIVENESS RE SULTS Comparison of Clustering Results with an Authoritative Target Partition Ant Lucene Tomcat Azureus Hibernate ITextdotNet jEdit JFreeChart JFTP JHotDraw JRefactory JUnit LiferayPortal Pmd Synapse TigerEnvelopes Velocity Xalan Xerces 0 0,2 0,4 0,6 0,8 1,0 19 target Open Source Java Systems FLAT INDEXING (NO ZONES) = STATE OF THE ART FLAT INDEXING (NO ZONES) ZONE INDEXING ZONE INDEXING + MLE WEIGHTS
57. AUTHORITATIVENESS RE SULTS Comparison of Clustering Results with an Authoritative Target Partition Ant Lucene Tomcat Azureus Hibernate ITextdotNet jEdit JFreeChart JFTP JHotDraw JRefactory JUnit LiferayPortal Pmd Synapse TigerEnvelopes Velocity Xalan Xerces 0 0,2 0,4 0,6 0,8 1,0 19 target Open Source Java Systems FLAT INDEXING (NO ZONES) = STATE OF THE ART FLAT INDEXING (NO ZONES) ZONE INDEXING ZONE INDEXING + MLE WEIGHTS
58. AUTHORITATIVENESS RE SULTS Comparison of Clustering Results with an Authoritative Target Partition Ant Lucene Tomcat Azureus Hibernate ITextdotNet jEdit JFreeChart JFTP JHotDraw JRefactory JUnit LiferayPortal Pmd Synapse TigerEnvelopes Velocity Xalan Xerces 0 0,2 0,4 0,6 0,8 1,0 19 target Open Source Java Systems FLAT INDEXING (NO ZONES) = STATE OF THE ART FLAT INDEXING (NO ZONES) ZONE INDEXING ZONE INDEXING + MLE WEIGHTS
59. AUTHORITATIVENESS RE SULTS Comparison of Clustering Results with an Authoritative Target Partition 19 target Open Source Java Systems FLAT INDEXING (NO ZONES) = STATE OF THE ART 0 0,2 0,4 0,6 0,8 1,0 Ant Lucene Tomcat Azureus Hibernate ITextdotNet jEdit JFreeChart JFTP JHotDraw JRefactory JUnit LiferayPortal Pmd Synapse TigerEnvelopes Velocity Xalan Xerces FLAT INDEXING (NO ZONES) ZONE INDEXING ZONE INDEXING + MLE WEIGHTS
60. ISSUE #2 SOURCE CODE NORMALIZATION
61. SOURCE CODE LEXICAL INFORMATION Implicit assumption: The “same” words are used whenever a particular concept occurs
62. SOURCE CODE LEXICAL INFORMATION Implicit assumption: The “same” words are used whenever a particular concept occurs
63. 1. Tokenization 2. Normalization 1.5 Identifier Splitting • Splitting algorithms based on naming conventions • camelCase Splitter: r’(?<=!^)([A-Z][a-z]+)’ • NullHandle ==> Null | Handle • displayBox ==> display | Box draw, the, are, box, r, rectangl, g, graphic, color, .... STATE OF THE ART TOOLS display, box null, handl
64. 1. Tokenization 2. Normalization 1.5 Identifier Splitting • Splitting algorithms based on naming conventions • camelCase Splitter: r’(?<=!^)([A-Z][a-z]+)’ • NullHandle ==> Null | Handle • displayBox ==> display | Box draw, the, are, box, r, rectangl, g, graphic, color, .... STATE OF THE ART TOOLS display, box null, handl
65. 1. Tokenization 2. Normalization 1.5 Identifier Splitting • Splitting algorithms based on naming conventions • camelCase Splitter: r’(?<=!^)([A-Z][a-z]+)’ • NullHandle ==> Null | Handle draw, the, are, box, r, rectangl, g, graphic, color, .... STATE OF THE ART TOOLS display, box null, handl
66. 1. Tokenization 2. Normalization 1.5 Identifier Splitting • Splitting algorithms based on naming conventions • camelCase Splitter: r’(?<=!^)([A-Z][a-z]+)’ • NullHandle ==> Null | Handle • displayBox ==> display | Box draw, the, are, box, r, rectangl, g, graphic, color, .... STATE OF THE ART TOOLS null, handl display, box
67. • camelCase Splitter: r’(?<=!^)([A-Z][a-z]+)’ IDENTIFIERS SPLITTING
68. • camelCase Splitter: r’(?<=!^)([A-Z][a-z]+)’ • drawXORRect ==> drawXOR | Rect IDENTIFIERS SPLITTING
69. • camelCase Splitter: r’(?<=!^)([A-Z][a-z]+)’ • drawXORRect ==> drawXOR | Rect • drawxorrect ==> NO SPLIT Splitting algorithms based on naming conventions are not robust enough IDENTIFIERS SPLITTING
70. Splitting algorithms based on naming conventions are not robust enough ABBREVIATIONS EXPANSION
71. Splitting algorithms based on naming conventions are not robust enough • Heavy use of Abbreviations in the source code • r as for Rectangle • rect as for Rectangle ABBREVIATIONS EXPANSION
72. Splitting algorithms based on naming conventions are not robust enough • Heavy use of Abbreviations in the source code • r as for Rectangle • rect as for Rectangle ABBREVIATIONS EXPANSION
73. 1. Tokenization CODE NORMALIZATION 2. Normalization 1.5 Code Normalization • AMAP (Hill and Pollock, 2008) • SAMURAI (Enslen, et.al , 2011) • TIDIER (Guerrouj, et.al , 2011) • GenTest+Normalize (Lawrie and Binkley, 2011) • LINSEN (Corazza, Di Martino and Maggio, 2012) draw, the, are, null, handl, box, rectangl, graphic, color, display, box, rectangl, draw, xor, rectangl
74. 1. Tokenization CODE NORMALIZATION 2. Normalization 1.5 Code Normalization • AMAP (Hill and Pollock, 2008) • SAMURAI (Enslen, et.al , 2011) • TIDIER (Guerrouj, et.al , 2011) • GenTest+Normalize (Lawrie and Binkley, 2011) • LINSEN (Corazza, Di Martino and Maggio, 2012) draw, the, are, null, handl, box, rectangl, graphic, color, display, box, rectangl, draw,xor, rectangl
75. CONTRI BUTION SOURCE CODE NORMALIZATION • LINSEN: novel technique for code normalization • Able to both Split Identifiers and Expand possible occurring abbreviations Corazza, A., Di Martino, S., Maggio, V. LINSEN: An efficient approach to split identifiers and expand abbreviations (2012) IEEE International Conference on Software Maintenance, ICSM, art. no. 6405277, pp. 233-242. ISBN: 978-146732312-3
76. CONTRI BUTION SOURCE CODE NORMALIZATION • LINSEN: novel technique for code normalization • Able to both Split Identifiers and Expand possible occurring abbreviations • Based on an efficient String Matching technique: Baeza-Yates&Perlberg Algorithm (BYP) • Exploit different Sources of Information to find the matching words Corazza, A., Di Martino, S., Maggio, V. LINSEN: An efficient approach to split identifiers and expand abbreviations (2012) IEEE International Conference on Software Maintenance, ICSM, art. no. 6405277, pp. 233-242. ISBN: 978-146732312-3
77. SKETCH OF THE ALGORITHM Model: Weighted Directed Graph Example: drawXORRect identifier
78. SKETCH OF THE ALGORITHM • NODES correspond to characters of the current identifier Model: Weighted Directed Graph Example: drawXORRect identifier 0 11 1 9 4 5 7 8 2 6 3 10
79. SKETCH OF THE ALGORITHM • ARCS corresponds to matchings between identifier substrings and dictionary words • NODES correspond to characters of the current identifier Model: Weighted Directed Graph Example: drawXORRect identifier 0 11 1 9 4 5 7 8 2 6 3 10
80. SKETCH OF THE ALGORITHM • ARCS corresponds to matchings between identifier substrings and dictionary words • Padding Arcs to ensure the Graph always connected • NODES correspond to characters of the current identifier Model: Weighted Directed Graph Example: drawXORRect identifier 0 11 1 9 4 “r”,C-MAX “d”,C-MAX 5 7 8 2 “R”,C-MAX 6 “a”,C-MAX 3 “w”,C-MAX “X”,C-MAX “O”,C-MAX “e”,C-MAX 10 “c”,C-MAX “t”,C-MAX “raw”,c(“raw”) “draw”,c(“draw”) “or”,c(“or”) “xor”,c(“xor”) “rectangle”,c(“rectangle”) “R”,C-MAX
81. SKETCH OF THE ALGORITHM • Every Arc is Labelled with the corresponding dictionary word • Weights represent the “cost” of each matching • Cost function [c(“word”)] favors longest words and domain- related information Model: Weighted Directed Graph Example: drawXORRect identifier 0 11 1 9 4 “r”,C-MAX “d”,C-MAX 5 7 8 2 “R”,C-MAX 6 “a”,C-MAX 3 “w”,C-MAX “X”,C-MAX “O”,C-MAX “e”,C-MAX 10 “c”,C-MAX “t”,C-MAX “raw”,c(“raw”) “draw”,c(“draw”) “or”,c(“or”) “xor”,c(“xor”) “rectangle”,c(“rectangle”) “R”,C-MAX
82. SKETCH OF THE ALGORITHM • The final Mapping Solution corresponds to the sequence of labels in the path with the minimum cost (Djikstra Algorithm) Model: Weighted Directed Graph Example: drawXORRect identifier 0 11 1 9 4 “r”,C-MAX “d”,C-MAX 5 7 8 2 “R”,C-MAX 6 “a”,C-MAX 3 “w”,C-MAX “X”,C-MAX “O”,C-MAX “e”,C-MAX 10 “c”,C-MAX “t”,C-MAX “raw”,c(“raw”) “draw”,c(“draw”) “or”,c(“or”) “xor”,c(“xor”) “rectangle”,c(“rectangle”) “R”,C-MAX
83. SPLITTING Accuracy Rates for the comparison with DTW (Madani et. al 2010) 0 0,25 0,5 0,75 1 JhotDraw 5.1 Lynx 2.8.5 DTW LINSEN DTW LINSEN DTW LINSEN RE SULTS # 1 Accuracy Rates for the comparison with GenTest (Lawrie and Binkley, 2011) 0 0,25 0,5 0,75 1 which 2.20 a2ps 4.14 GenTest LINSEN DTW O(n3)
84. Accuracy Rates for the comparison with AMAP (Hill and Pollock, 2008) 0 0,25 0,5 0,75 1 CW DL OO AC PR SL TOTAL (Avg) AMAP LINSEN EXPANSION RE SULTS # 2 CW: Combination Words DL: Dropped Letters OO: Others AC: Acronyms PR: Prefix SL: Single Letters
85. ISSUE #3 CLONE DETECTION
86. PROBL EM S T A T E M E N T CLONE DETECTION
87. PROBL EM S T A T E M E N T CLONE DETECTION Clones Textual Similarity
88. PROBL EM S T A T E M E N T CLONE DETECTION Clones Functional Similarity
89. PROBL EM S T A T E M E N T CLONE DETECTION Clones affect the reliability of the system! Sneaky Bug!
90. 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 STATE OF THE ART TOOLS
91. 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 STATE OF THE ART TOOLS
92. 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 STATE OF THE ART TOOLS
93. 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 STATE OF THE ART TOOLS
94. 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 STATE OF THE ART TOOLS
95. CONTRI BUTION CLONE DETECTION • Combining different sources of information to improve the effectiveness of the detection process Corazza, A., Di Martino, S., Maggio, V., Scanniello, G. A Tree Kernel based approach for clone detection (2010) IEEE International Conference on Software Maintenance, ICSM, art. no. 5609715. ISBN: 978-142448629-8 Corazza, A., Di Martino, S., Maggio, V., Moschitti, A., Passerini, A., Scanniello, G., Silvestri F. Using Machine Learning and Information Retrieval Techniques to Improve Software Maintainability (2013) Communications in Computer and Information Science, In Press
96. CONTRI BUTION CLONE DETECTION • Combining different sources of information to improve the effectiveness of the detection process • Structural and Lexical Information Corazza, A., Di Martino, S., Maggio, V., Scanniello, G. A Tree Kernel based approach for clone detection (2010) IEEE International Conference on Software Maintenance, ICSM, art. no. 5609715. ISBN: 978-142448629-8 Corazza, A., Di Martino, S., Maggio, V., Moschitti, A., Passerini, A., Scanniello, G., Silvestri F. Using Machine Learning and Information Retrieval Techniques to Improve Software Maintainability (2013) Communications in Computer and Information Science, In Press
97. CONTRI BUTION CLONE DETECTION • Combining different sources of information to improve the effectiveness of the detection process • Structural and Lexical Information • Application of Kernel Methods to Source Code Structures Corazza, A., Di Martino, S., Maggio, V., Scanniello, G. A Tree Kernel based approach for clone detection (2010) IEEE International Conference on Software Maintenance, ICSM, art. no. 5609715. ISBN: 978-142448629-8 Corazza, A., Di Martino, S., Maggio, V., Moschitti, A., Passerini, A., Scanniello, G., Silvestri F. Using Machine Learning and Information Retrieval Techniques to Improve Software Maintainability (2013) Communications in Computer and Information Science, In Press
98. CONTRI BUTION CLONE DETECTION • Combining different sources of information to improve the effectiveness of the detection process • Structural and Lexical Information • Application of Kernel Methods to Source Code Structures • Typically applied in other field such as NLP or Bioinformatics Corazza, A., Di Martino, S., Maggio, V., Scanniello, G. A Tree Kernel based approach for clone detection (2010) IEEE International Conference on Software Maintenance, ICSM, art. no. 5609715. ISBN: 978-142448629-8 Corazza, A., Di Martino, S., Maggio, V., Moschitti, A., Passerini, A., Scanniello, G., Silvestri F. Using Machine Learning and Information Retrieval Techniques to Improve Software Maintainability (2013) Communications in Computer and Information Science, In Press
99. CODE STRUCTURES KERNELS FOR STRUCTURES Computation of the dot product between (Graph) Structures K( ) ,
100. CODE STRUCTURES KERNELS FOR STRUCTURES 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( ) ,
101. CODE STRUCTURES KERNELS FOR STRUCTURES 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( ) ,
102. CODE KERNEL FOR CLONES
103. < 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 KERNEL FOR CLONES
104. < 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 KERNEL FOR CLONES < 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 +
105. while block < x y KERNELS FOR CODE STRUCTURES: AST KERNEL FEATURES
106. while block < x y KERNELS FOR CODE STRUCTURES: AST KERNEL FEATURES Instruction Class (IC) i.e., LOOP, CALL, CONDITIONAL_STATEMENT
107. while block < x y KERNELS FOR CODE STRUCTURES: AST KERNEL FEATURES Instruction Class (IC) i.e., LOOP, CALL, CONDITIONAL_STATEMENT Instruction (I) i.e., FOR, IF, WHILE, RETURN
108. while block < x y KERNELS FOR CODE STRUCTURES: AST KERNEL FEATURES 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
109. while block < x y KERNELS FOR CODE STRUCTURES: AST KERNEL FEATURES 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
110. while block < x y KERNELS FOR CODE STRUCTURES: AST KERNEL FEATURES 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 ]
111. 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
112. CONCLUSIONS
113. CONCLUSIONS
114. CONCLUSIONS
115. CONCLUSIONS
116. CONCLUSIONS
117. CONCLUSIONS FUTURE RESEARCH DIRECTIONS • Re-modularization: • Integrate code normalization algorithm (LINSEN) to lexical analysis • Code Normalization: • Investigate the contributions of different sources of information used for disambiguations • Clone Detection: • Combine Static (and Kernel methods) with Dynamic Analysis
118. THANK YOU June 5th, 2013 - Ph.D. Defense Valerio Maggio Ph.D. Student, University of Naples “Federico II” valerio.maggio@unina.it