Paper: Expanding Identifiers to Normalize Source Code Vocabulary Authors: Dave Binkley and Dawn Lawrie Session: Research Track 4: Natural Language Analysis

1. EXPANDING IDENTIFIERS TO
NORMALIZING SOURCE
CODE VOCABULARY
PRESENTED BY DAWN LAWRIE
LOYOLA UNIVERSITY MARYLAND
IN COLLABORATION WITH DAVE BINKLEY
Friday, October 7, 11

2. VOCABULARY MISMATCH
DIFFERENT VOCABULARY IN SOURCE CODE AND OTHER
SOFTWARE ARTIFACTS
EXAMPLE
REQUIREMENT - “FEATURE LOCATION”
SOURCE CODE - “FEATURELOCATION”
OR WORSE “FLOC”
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3. PURPOSE OF NORMALIZE
COPE WITH VOCABULARY MISMATCH
SOURCE CODE
OTHER SOFTWARE DOCUMENTS
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4. EXAMPLE PROBLEMS
CONSIDER IDENTIFIERS
FEATURELOCATION
FLOC
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5. EXAMPLE PROBLEMS
CONSIDER IDENTIFIERS
FEATURE LOCATION SPLITTING PROBLEM
FLOC
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6. EXAMPLE PROBLEMS
CONSIDER IDENTIFIERS
FEATURE LOCATION SPLITTING PROBLEM
F LOC SPLITTING PROBLEM
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7. EXAMPLE PROBLEMS
CONSIDER IDENTIFIERS
FEATURE LOCATION SPLITTING PROBLEM
FEATURE LOCATION SPLITTING AND
EXPANSION PROBLEM
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8. WHY NORMALIZE?
MANY SE PROBLEMS CAN BE ADDRESSED USING
INFORMATION RETRIEVAL (IR) TECHNIQUES
UN-NORMALIZED CODE LEADS TO AN UNDER
ESTIMATE OF THE IMPORTANCE OF CRUCIAL WORDS
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9. NORMALIZE PROBLEM STATEMENT
FIND THE BEST EXPANSION OVERALL POSSIBLE SPLITS
FLOC FEATURE LOCATION
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10. NORMALIZE ALGORITHM
TERMINOLOGY
HARD-WORD - WHITEHOUSE_LAWN
SOFT-WORD - WHITE-HOUSE_LAWN
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11. NORMALIZE ALGORITHM
TERMINOLOGY
HARD-WORD - WHITEHOUSE_LAWN (2)
SOFT-WORD - WHITE-HOUSE_LAWN
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12. NORMALIZE ALGORITHM
TERMINOLOGY
HARD-WORD - WHITEHOUSE_LAWN (2)
SOFT-WORD - WHITE-HOUSE_LAWN (3)
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13. NORMALIZE ALGORITHM
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14. NORMALIZE ALGORITHM
STRLEN STRING LENGTH
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15. MACHINE TRANSLATION
APPROACH
EL PAPA VISITA LA IGLESIA
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16. MACHINE TRANSLATION
APPROACH
EL PAPA VISITA LA IGLESIA
FATHER VISITS
THE POTATO VISITOR THE CHURCH
POPE HIT
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17. MACHINE TRANSLATION
APPROACH
EL PAPA VISITA LA IGLESIA
FATHER VISITS
THE POTATO VISITOR THE CHURCH
POPE HIT
Friday, October 7, 11

18. MACHINE TRANSLATION
APPROACH
EL PAPA VISITA LA IGLESIA
FATHER VISITS
THE POTATO VISITOR THE CHURCH
POPE HIT COH ESION
STRONG
Friday, October 7, 11

19. MACHINE TRANSLATION
APPROACH
EL PAPA VISITA LA IGLESIA
FATHER VISITS
THE POTATO VISITOR THE CHURCH
POPE HIT COH ESION
STRONG
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20. NORMALIZE ALGORITHM
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21. NORMALIZE ALGORITHM
STRLEN
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22. NORMALIZE ALGORITHM
STRLEN
S-TRLEN
ST-RLEN
STR-LEN
STRL_EN
STRLE_N
S_T_RLEN
S-TR-LEN
S_TRL_EN
S_TRLE_N
ST_R_LEN
ST_RL_EN
ST_RLE_N
STR_L_EN
STR_LE_N
STRL_E_N
S_T_R_LEN
S_T_RL_EN
S_T_RLE_N
S_TR_L_EN
S_TR_LE_N
S_TRL_E_N
ST_R_L_EN
ST_R_LE_N
ST_RL_E_N
STR_L_E_N
S_T_R_L_EN
S_T_R_LE_N
S_TR_L_E_N
ST_R_L_E_N
S-T-R-L-E-N
Friday, October 7, 11

23. NORMALIZE ALGORITHM
STRLEN
S-TRLEN
E(RLEN) = {RIFLEMEN}
ST-RLEN
STR-LEN
STRL_EN
STRLE_N
S_T_RLEN
S-TR-LEN
S_TRL_EN
S_TRLE_N
ST_R_LEN
ST_RL_EN
ST_RLE_N
STR_L_EN
STR_LE_N
STRL_E_N
S_T_R_LEN
S_T_RL_EN
S_T_RLE_N
S_TR_L_EN
S_TR_LE_N
S_TRL_E_N
ST_R_L_EN
ST_R_LE_N
ST_RL_E_N
STR_L_E_N
S_T_R_L_EN
S_T_R_LE_N
S_TR_L_E_N
ST_R_L_E_N
S-T-R-L-E-N
Friday, October 7, 11

24. NORMALIZE ALGORITHM
STRLEN
S-TRLEN
E(RLEN) = {RIFLEMEN}
ST-RLEN
WILDCARD EXPANSION
STR-LEN
STRL_EN
STRLE_N R*L*E*N*
S_T_RLEN
S-TR-LEN
S_TRL_EN
S_TRLE_N
ST_R_LEN
ST_RL_EN
ST_RLE_N
STR_L_EN
STR_LE_N
STRL_E_N
S_T_R_LEN
S_T_RL_EN
S_T_RLE_N
S_TR_L_EN
S_TR_LE_N
S_TRL_E_N
ST_R_L_EN
ST_R_LE_N
ST_RL_E_N
STR_L_E_N
S_T_R_L_EN
S_T_R_LE_N
S_TR_L_E_N
ST_R_L_E_N
S-T-R-L-E-N
Friday, October 7, 11

25. NORMALIZE ALGORITHM
STRLEN
E(ST) = {SET, STOP, STRING}
S-TRLEN
E(RLEN) = {RIFLEMEN}
ST-RLEN
STR-LEN E(STR) = {STEER, STRING}
STRL_EN E(LEN) = {LENDER, LENGTH}
STRLE_N
S_T_RLEN
S-TR-LEN
S_TRL_EN
S_TRLE_N
ST_R_LEN
ST_RL_EN
ST_RLE_N
STR_L_EN
STR_LE_N
STRL_E_N
S_T_R_LEN
S_T_RL_EN
S_T_RLE_N
S_TR_L_EN
S_TR_LE_N
S_TRL_E_N
ST_R_L_EN
ST_R_LE_N
ST_RL_E_N
STR_L_E_N
S_T_R_L_EN
S_T_R_LE_N
S_TR_L_E_N
ST_R_L_E_N
S-T-R-L-E-N
Friday, October 7, 11

26. NORMALIZE ALGORITHM PART I
STR
VS
STRING STEER
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27. NORMALIZE ALGORITHM PART I
STR
VS
LENDER LENDER
STRING STEER
LENGTH LENGTH
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28. NORMALIZE ALGORITHM PART I
STR
VS
LENDER LENDER
STRING STEER
LENGTH LENGTH
1. FIND COHESION BY SUMMING LOG OF
PROBABILITIES OF WORD PAIRS
Friday, October 7, 11

29. NORMALIZE ALGORITHM PART I
STR
VS
LENDER LENDER
STRING STEER
+ LENGTH + LENGTH
COHESIONA COHESIONB
1. FIND COHESION BY SUMMING LOG OF
PROBABILITIES OF WORD PAIRS
Friday, October 7, 11

30. NORMALIZE ALGORITHM PART I
STR
VS
LENDER LENDER
STRING STEER
+ LENGTH + LENGTH
COHESIONA COHESIONB
1. FIND COHESION BY SUMMING LOG OF
PROBABILITIES OF WORD PAIRS
2. SELECT EXPANSION THAT MAXIMIZES
COHESION
Friday, October 7, 11

31. NORMALIZE ALGORITHM PART I
STR
VS
LENDER LENDER
STRING STEER
+ LENGTH + LENGTH
COHESIONA COHESIONB
1. FIND COHESION BY SUMMING LOG OF
PROBABILITIES OF WORD PAIRS
2. SELECT EXPANSION THAT MAXIMIZES
COHESION
Friday, October 7, 11

32. NORMALIZE ALGORITHM PART I
STR
VS
LENDER LENDER
STRING STEER
+ LENGTH + LENGTH
COHESIONA COHESIONB
STRING
1. FIND COHESION BY SUMMING LOG OF
PROBABILITIES OF WORD PAIRS
2. SELECT EXPANSION THAT MAXIMIZES
COHESION
Friday, October 7, 11

33. NORMALIZE ALGORITHM PART II
VS
STR-LEN ST-RLEN
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34. NORMALIZE ALGORITHM PART II
VS
STR-LEN ST-RLEN
STRING LENGTH STOP RIFLEMEN
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35. NORMALIZE ALGORITHM PART II
VS
STR-LEN ST-RLEN
STRING LENGTH STOP RIFLEMEN
1. FIND COHESION OVER EXPANSIONS
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36. NORMALIZE ALGORITHM PART II
VS
STR-LEN ST-RLEN
STRING LENGTH STOP RIFLEMEN
1. FIND COHESION OVER EXPANSIONS
2. SELECT EXPANSION OF THE SPLIT
THAT MAXIMIZES COHESION
Friday, October 7, 11

37. NORMALIZE ALGORITHM PART II
VS
STR-LEN ST-RLEN
STRING LENGTH STOP RIFLEMEN
1. FIND COHESION OVER EXPANSIONS
2. SELECT EXPANSION OF THE SPLIT
THAT MAXIMIZES COHESION
Friday, October 7, 11

38. NORMALIZE ALGORITHM PART II
VS
STR-LEN ST-RLEN
STRING LENGTH STOP RIFLEMEN
STRING LENGTH
1. FIND COHESION OVER EXPANSIONS
2. SELECT EXPANSION OF THE SPLIT
THAT MAXIMIZES COHESION
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39. ADDING CONTEXT
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40. ADDING CONTEXT
DIR
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41. ADDING CONTEXT
DIR E(DIR) = {DIRECTION, DIRECTORY}
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42. ADDING CONTEXT
DIR E(DIR) = {DIRECTION, DIRECTORY}
CONTEXT = {FORWARD, BACKWARD}
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43. ADDING CONTEXT
DIR E(DIR) = {DIRECTION, DIRECTORY}
CONTEXT = {FORWARD, BACKWARD}
FIND COHESION WITH CONTEXT WORDS IN ADDITION TO
EXPANSIONS OF OTHER SOFT WORDS
USED IN BOTH PART 1 AND PART 2
Friday, October 7, 11

44. NORMALIZE IMPLEMENTATION
USES GenTest TO SPLIT IDENTIFIERS
RETURNS MULTIPLE SPLITS
GOOGLE 5-GRAM DATASET
Friday, October 7, 11

45. EVALUATION
Program Loc SLoc Unique Ids
which-2.20 3,670 2,293 487
a2ps-4.14 62,347 38,436 4,393
Program Selected Ids Hard Words Soft Words
which-2.20 487 903 1214
a2ps-4.14 211 459 618
Friday, October 7, 11

46. EVALUATION
THREE GROUPS OF IDENTIFIERS
STANDARD LIBRARY CALLS
NAMES FROM STANDARD HEADER FILES / KEYWORDS
DOMAIN NAMES
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47. EVALUATION
THREE GROUPS OF IDENTIFIERS
STANDARD LIBRARY CALLS
NAMES FROM STANDARD HEADER FILES / KEYWORDS
DOMAIN NAMES
Friday, October 7, 11

48. EVALUATION
THREE GROUPS OF IDENTIFIERS
STANDARD LIBRARY CALLS
NAMES FROM STANDARD HEADER FILES / KEYWORDS
DOMAIN NAMES
Program Filtered Ids Reported Ids
which-2.20 152 335
a2ps-4.14 46 166
Friday, October 7, 11

49. EXAMPLE EXPANSIONS
id Top 10 Top Expansion
Expansion
nextchar next_character next_character
indfound index_found_need index_found
optarg option_are_g optarg
itemno i_them_not itemno
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50. RESEARCH QUESTIONS
WHAT IS THE OVERALL ACCURACY OF NORMALIZE?
DOES THE VOCABULARY USED HAVE A SIGNIFICANT
IMPACT ON THE EXPANSION’S ACCURACY?
CAN THE EXPANDER INFORM THE SPLITTER?
CAN THE SPLITTER INFORM THE EXPANDER?
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51. ACCURACY ON DOMAIN IDS
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52. SOURCE OF EXPANSION WORDS
SOURCE CODE
INTERNAL DOCUMENTATION
MANUAL
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53. BEST VOCABULARY SOURCE?
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54. FUTURE WORK
EXPLORING DIFFERENT SOURCES OF CO-OCCURRENCE
DATA
EXPLORING DIFFERENT WAYS OF CALCULATING
PROBABILITIES
EXAMINING NORMALIZATION IN CONTEXT OF AN
INFORMATION RETRIEVAL TASK
Friday, October 7, 11

55. SUMMARY
IDENTIFIERS ARE WRITTEN DIFFERENTLY THAN OTHER
SOFTWARE DOCUMENTS
DEGRADES PERFORMANCE OF IR TECHNIQUES
NORMALIZE CURRENTLY EXPANDS ABOUT HALF OF
SOFT WORDS CORRECTLY
Friday, October 7, 11

56. QUESTIONS?
Need an identifier split?
GenTest Splitter available at
splitit.cs.loyola.edu
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