Icse2010 adams presentation
- 1. Identifying Crosscutting Concerns Using Historical Code Changes Bram Adams Zhen Ming Jiang Ahmed E. Hassan SAIL, Queen's University http://sailhome.cs.queensu.ca/~bram/
- 2. What are crosscutting concerns? 2
- 15. 1. Which concerns are implemented? 2. Where? 3. How are concerns composed together? (Crosscutting) Concern Mining 4
- 16. 1. What is a Crosscutting Concern? 2. The Concern Mining Process and its Shortcomings 3. COMMIT 4. Case Study 5. Conclusion 5
- 19. Concern Mining Process data source concern seeds Concern Mining Techniques :-) 1 6
- 20. Concern Mining Process data source concern seeds Concern Mining Techniques :-) concerns 1 2 6
- 21. Concern Mining Process data source concern seeds expanded concerns Concern Mining Techniques :-) concerns 1 2 3 6
- 22. Concern Mining Process data source concern seeds expanded concerns concern composition Concern Mining Techniques :-) concerns 1 2 3 4 6
- 23. Concern Mining Process data source concern seeds expanded concerns concern composition Concern Mining Techniques :-) MANUAL :-( concerns 1 2 3 4 6
- 24. Concern Mining Process data source concern seeds expanded concerns concern composition Concern Mining Techniques :-) MANUAL :-( concerns 1 2 3 4 6
- 25. S1: Limited Context 1 2 3 4 7
- 29. S2: Noise 1 2 3 4 8
- 30. S2: Noise 1 2 3 4 8
- 31. S2: Noise 1 2 3 4 8
- 34. 1. What is a Crosscutting Concern? 2. The Concern Mining Process and its Shortcomings 3. COMMIT 4. Case Study 5. Conclusion 10
- 35. COncern Mining using Mutual Information over Time CVS 11 limited context noise no composition
- 36. COncern Mining using Mutual Information over Time analyze historical changes to all code entities CVS 11 limited context noise no composition
- 37. COncern Mining using Mutual Information over Time analyze historical changes to all code entities statistical clustering based on mutual information CVS 11 limited context noise no composition
- 38. S1. Historical Data Sources CVS CVS 12
- 39. S1. Historical Data Sources CVS transactions CVS 12
- 40. S1. Historical Data Sources CVS transactions CVS 12
- 41. S1. Historical Data Sources CVS transactions CVS function call or variable access added 12
- 42. S1. Historical Data Sources CVS transactions CVS function call or variable access added intentional co- addition of calls and accesses 12
- 43. S1. Historical Data Sources CVS transactions CVS function call or variable access added intentional co- addition of calls and accesses concern seed 12
- 58. How much does occurrence of reveal about occurrence of ? S2. Mutual Information 14
- 59. How much does occurrence of reveal about occurrence of ? S2. Mutual Information 14
- 60. How much does occurrence of reveal about occurrence of ? S2. Mutual Information 14
- 61. How much does occurrence of reveal about occurrence of ? S2. Mutual Information 14
- 62. How much does occurrence of reveal about occurrence of ? S2. Mutual Information 14
- 63. S3. Concern Relations seed graph 15
- 66. 1. What is a Crosscutting Concern? 2. The Concern Mining Process and its Shortcomings 3. COMMIT 4. Case Study 5. Conclusion 16
- 67. Case Study 1996-2002 1993-2003 (800 kLOC) (2 MLOC) 17
- 68. Comparative Study 18 CBFA HAM COMMIT similar entity names ✖ identical set of callers ✖ mutual information ✔ limited context noise no composition
- 69. Comparative Study 18 CBFA HAM COMMIT similar entity names ✖ identical set of callers ✖ mutual information ✔ limited context noise no composition
- 70. Comparative Study 18 CBFA HAM COMMIT similar entity names ✖ identical set of callers ✖ mutual information ✔ limited context noise no composition CVS CVS snapshot
- 71. Comparative Study 18 CBFA HAM COMMIT similar entity names ✖ identical set of callers ✖ mutual information ✔ limited context noise no composition CVS CVS snapshot
- 72. Comparative Study 18 CBFA HAM COMMIT similar entity names ✖ identical set of callers ✖ mutual information ✔ limited context noise no composition CVS CVS snapshot
- 73. Study Design 19
- 74. Study Design 19
- 76. Study Design CBFA HAM COMMIT top 20 top 20 top 20 19
- 77. Study Design CBFA HAM COMMIT top 20 top 20 top 20 19 concern?
- 78. Study Design CBFA HAM COMMIT top 20 top 20 top 20 19
- 81. Study Design CBFA HAM COMMIT top 20 top 20 top 20 19
- 82. Study Design CBFA HAM COMMIT top 20 top 20 top 20 19 top 20 top 20 top 20
- 83. H1. Richer Data Sources Yield richer Seeds CVS 20
- 84. H1. Richer Data Sources Yield richer Seeds 0 8 16 24 32 40 CBFA HAM COMMIT 0 45 90 135 180 225 CBFA HAM COMMIT #non-function entities #functions CVS 20
- 85. H1. Richer Data Sources Yield richer Seeds 0 8 16 24 32 40 CBFA HAM COMMIT 0 45 90 135 180 225 CBFA HAM COMMIT #non-function entities #functions CVS 20 50% 79% 83% 29% 88% 75%
- 86. H2. COMMIT Identifies a Larger Percentage of unique Concerns 21
- 87. H2. COMMIT Identifies a Larger Percentage of unique Concerns 21 0 20 40 60 80 100 CBFA HAM COMMIT 0 20 40 60 80 100 CBFA HAM COMMIT
- 88. H2. COMMIT Identifies a Larger Percentage of unique Concerns 21 0 20 40 60 80 100 CBFA HAM COMMIT 56% 56% 0 20 40 60 80 100 CBFA HAM COMMIT 87.5% 50%
- 89. H3. COMMIT complements CBFA and HAM (1) 22
- 90. H3. COMMIT complements CBFA and HAM (1) 22 CBFA HAM COMMIT 0 0 1 08 14 9 CBFA HAM COMMIT 1 0 0 09 14 9
- 91. H3. COMMIT complements CBFA and HAM (2) 23
- 92. d1 d2 d3 d4 d5 d6 d7 d8 d9 H3. COMMIT complements CBFA and HAM (2) device drivers 23 kernel
- 93. d1 d2 d3 d4 d5 d6 d7 d8 d9 H3. COMMIT complements CBFA and HAM (2) CBFA concern (e.g., driver API) 23 kernel
- 94. d1 d2 d3 d4 d5 d6 d7 d8 d9 H3. COMMIT complements CBFA and HAM (2) CBFA concern (e.g., driver API) HAM concern (e.g., cloned driver code) 23 kernel
- 95. d1 d2 d3 d4 d5 d6 d7 d8 d9 H3. COMMIT complements CBFA and HAM (2) CBFA concern (e.g., driver API) HAM concern (e.g., cloned driver code) COMMIT concern (e.g., driver + infrastructure) 23 kernel
- 96. d1 d2 d3 d4 d5 d6 d7 d8 d9 H3. COMMIT complements CBFA and HAM (2) CBFA concern (e.g., driver API) HAM concern (e.g., cloned driver code) COMMIT concern (e.g., driver + infrastructure) 23 kernel
- 97. 24 ODBC Data Retrieval Composite Concern
- 98. 24 ODBC Data Retrieval Composite Concern 1. connection configuration
- 99. 1 24 ODBC Data Retrieval Composite Concern ODBC 1. connection configuration
- 100. 1 2 24 ODBC Data Retrieval Composite Concern ODBC 1. connection configuration 2. connection error handling
- 101. 1 3 2 24 ODBC Data Retrieval Composite Concern ODBC 1. connection configuration 2. connection error handling 3. data transfer
- 102. 1 3 2 4 24 ODBC Data Retrieval Composite Concern ODBC 1. connection configuration 2. connection error handling 3. data transfer 4. SQL-to-ODBC conversion
- 103. 1 3 2 45 24 ODBC Data Retrieval Composite Concern ODBC 1. connection configuration 2. connection error handling 3. data transfer 4. SQL-to-ODBC conversion 5. ODBC-to-ESQL conversion
- 104. 1 3 2 6 45 24 ODBC Data Retrieval Composite Concern ODBC 1. connection configuration 2. connection error handling 3. data transfer 4. SQL-to-ODBC conversion 5. ODBC-to-ESQL conversion 6. conversion error handling
- 105. 1 3 4 6 2 1 3 2 6 45 5 24 ODBC Data Retrieval Composite Concern ODBC 1. connection configuration 2. connection error handling 3. data transfer 4. SQL-to-ODBC conversion 5. ODBC-to-ESQL conversion 6. conversion error handling
- 106. 36 seeds 25
- 108. 36 seeds ODBC Data Retrieval Concern 25 5 other composite concerns
- 109. Threats toValidity • generalizability to other systems • subjectivity substantial agreement (Kappa) • seed quality not checked • threshold optimization is task-specific 26
- 110. 1. What is a Crosscutting Concern? 2. The Concern Mining Process and its Shortcomings 3. COMMIT 4. Case Study 5. Conclusion 27
- 111. 28
- 113. Crosscutting Concerns multi-threading tracingexception handling data persistence security memory cleanup 3D rendering performance sound support 28 Concern Mining Shortcomings S1. limited seed context S2. noise between seeds S3. no composition of concerns
- 114. Crosscutting Concerns multi-threading tracingexception handling data persistence security memory cleanup 3D rendering performance sound support 28 Concern Mining Shortcomings S1. limited seed context S2. noise between seeds S3. no composition of concerns COMMIT CVS transactions function call or variable access added intentional co- addition of calls and accesses concern seed
- 115. Crosscutting Concerns multi-threading tracingexception handling data persistence security memory cleanup 3D rendering performance sound support 28 COMMIT complements CBFA and HAM CBFA HAM COMMIT 0 0 1 08 14 9 CBFA HAM COMMIT 1 0 0 09 14 9 Concern Mining Shortcomings S1. limited seed context S2. noise between seeds S3. no composition of concerns COMMIT CVS transactions function call or variable access added intentional co- addition of calls and accesses concern seed
- 116. QUESTIONS? Crosscutting Concerns multi-threading tracingexception handling data persistence security memory cleanup 3D rendering performance sound support 28 COMMIT complements CBFA and HAM CBFA HAM COMMIT 0 0 1 08 14 9 CBFA HAM COMMIT 1 0 0 09 14 9 Concern Mining Shortcomings S1. limited seed context S2. noise between seeds S3. no composition of concerns COMMIT CVS transactions function call or variable access added intentional co- addition of calls and accesses concern seed