LINSEN an efficient approach to split identifiers and expand abbreviations

Slide of the talk presented at IEEE ICSM 2012 (International Conference on Software Maintenance), held in Riva del Garda (TN), Italy on Sept. 2012

**Abstract**:
Information Retrieval (IR) techniques are being exploited by an increasing number of tools supporting Software Maintenance activities. Indeed the lexical information embedded in the source code can be valuable for tasks such as concept location, clustering or recovery of traceability links. The application of such IR-based techniques relies on the consistency of the lexicon available in the different artifacts, and their effectiveness can worsen if programmers introduce abbreviations (e.g: rect) and/or do not strictly follow naming conventions such as Camel Case (e.g: UTFtoASCII). In this paper we propose an approach to automatically split identifiers in their composing words, and expand abbreviations. The solution is based on a graph model and performs in linear time with respect to the size of the dictionary, taking advantage of an approximate string matching technique. The proposed technique exploits a number of different dictionaries, referring to increasingly broader contexts, in order to achieve a disam- biguation strategy based on the knowledge gathered from the most appropriate domain. The approach has been compared to other splitting and expansion techniques, using freely available oracles for the identifiers extracted from 24 C/C++ and Java open source systems. Results show an improvement in both splitting and expanding performance, in addition to a strong enhancement in the computational efficiency.

Transcript

1. LINSEN AN EFFICIENT APPROACH TO SPLIT IDENTIFIERS AND EXPAND ABBREVIATIONS Anna Corazza, Sergio Di Martino, Valerio Maggio Università di Napoli “Federico II” 26th Sept. 2012, ICSM2012@Riva del Garda(Trento), Italy
2. MOTIVA TIONS IR FOR SE
3. MOTIVA TIONS IR FOR SE
4. IR FOR NATURAL LANGUAGE
5. 1. Tokenization IR FOR NATURAL LANGUAGE
6. 1. Tokenization IR FOR NATURAL LANGUAGE Draws, the, are, NullHandle, box, r, Rectangle, g, Graphics, box, displayBox, ...
7. 1. Tokenization 2. Normalization IR FOR NATURAL LANGUAGE Draws, the, are, NullHandle, box, r, Rectangle, g, Graphics, box, displayBox, ...
8. 1. Tokenization 2. Normalization IR FOR NATURAL LANGUAGE Draws, the, are, NullHandle, box, r, Rectangle, g, Graphics, box, displayBox, ... 1. Change to Lower case draws, the, are, nullhandle, box, r, rectangle, g, graphics, box, displaybox, ...
9. 1. Tokenization 2. Normalization IR FOR NATURAL LANGUAGE Draws, the, are, NullHandle, box, r, Rectangle, g, Graphics, box, displayBox, ... 1. Change to Lower case draws, the, are, nullhandle, box, r, rectangle, g, graphics, box, displaybox, ... 2.Remove StopWords
10. 1. Tokenization 2. Normalization IR FOR NATURAL LANGUAGE Draws, the, are, NullHandle, box, r, Rectangle, g, Graphics, box, displayBox, ... 1. Change to Lower case draw, the, are, nullhandl, box, r, rectangl, g, graphic, box, displaybox, ... 2.Remove StopWords 3.Apply Stemming 4. ...
11. Implicit assumption: The “same” words are used whenever a particular concept is described 1. Tokenization 2. Normalization IR FOR NATURAL LANGUAGE Draws, the, are, NullHandle, box, r, Rectangle, g, Graphics, box, displayBox, ... 1. Change to Lower case draw, the, are, nullhandl, box, r, rectangl, g, graphic, box, displaybox, ... 2.Remove StopWords 3.Apply Stemming 4. ...
12. Implicit assumption: The “same” words are used whenever a particular concept is described 1. Tokenization 2. Normalization IR FOR NATURAL LANGUAGE Draws, the, are, NullHandle, box, r, Rectangle, g, Graphics, box, displayBox, ... 1. Change to Lower case draw, the, are, nullhandl, box, r, rectangl, g, graphic, box, displaybox, ... 2.Remove StopWords 3.Apply Stemming 4. ...
13. 1. Tokenization IR FOR SOURCE CODE 2. Normalization 1.5 Identifier Splitting
14. 1. Tokenization IR FOR SOURCE CODE 2. Normalization 1.5 Identifier Splitting • snake_case Splitter: r’(?<=\w)_’ • display_box ==> display | box • camelCase/PascalCase Splitter: r’(?<!^)([A-Z][a-z]+)’ • displayBox ==> display | Box draw, the, are, null, handl, box, r, rectangl, g, graphic, box, display, box, ...
15. IR FOR SOURCE CODE
16. IR FOR SOURCE CODE
17. • camelCase Splitter: r’(?<=!^)([A-Z][a-z]+)’ • drawXORRect ==> drawXOR | Rect • drawxorrect ==> NO SPLIT IR FOR SOURCE CODE
18. • camelCase Splitter: r’(?<=!^)([A-Z][a-z]+)’ • drawXORRect ==> drawXOR | Rect • drawxorrect ==> NO SPLIT IR FOR SOURCE CODE Splitting algorithms based on naming conventions are not robust enough
19. Splitting algorithms based on naming conventions are not robust enough • Heavy use of Abbreviations in the source code IR FOR SOURCE CODE
20. Splitting algorithms based on naming conventions are not robust enough • Heavy use of Abbreviations in the source code • rect as for Rectangle • r as for Rectangle IR FOR SOURCE CODE
21. Splitting algorithms based on naming conventions are not robust enough • Heavy use of Abbreviations in the source code • rect as for Rectangle • r as for Rectangle IR FOR SOURCE CODE
22. There could be multiple and equally correct splitting or expansion solutions THE AMBIGUITY PROBLEM
23. There could be multiple and equally correct splitting or expansion solutions • r as for Rectangle OR red THE AMBIGUITY PROBLEM
24. There could be multiple and equally correct splitting or expansion solutions • r as for Rectangle OR red THE AMBIGUITY PROBLEM •nsISupport ==> ns|IS|up|ports OR ==> ns|I|Supports
25. 1. Tokenization IDENTIFIER MAPPING 2. Normalization 1.5 Identifier Mapping • SAMURAI (Enslen, et.al , 2011) • TIDIER (Guerrouj, et.al , 2011) • GenTest+Normalize (Lawrie and Binkley, 2011) • AMAP (Hill and Pollock, 2008) • ... • LINSEN draw, the, are, null, handl, box, r, rectangl, g, graphic, box, display, box, ...
26. LINSEN A L G O R I T H M CONTRIBUTION • Novel technique for the Identifier Mapping
27. LINSEN A L G O R I T H M CONTRIBUTION • Novel technique for the Identifier Mapping • Based on an efficient String Matching technique: Baeza-Yates&Perlberg Algorithm (BYP)
28. LINSEN A L G O R I T H M CONTRIBUTION • Novel technique for the Identifier Mapping • Based on an efficient String Matching technique: Baeza-Yates&Perlberg Algorithm (BYP) • Applied on a Graph-based model
29. LINSEN A L G O R I T H M CONTRIBUTION • Novel technique for the Identifier Mapping • Based on an efficient String Matching technique: Baeza-Yates&Perlberg Algorithm (BYP) • Applied on a Graph-based model • Able to both Split Identifiers and Expand possible occurring abbreviations
30. DICTIONARIES
31. DICTIONARIES
32. DICTIONARIES Application-aware Dictionaries
33. DICTIONARIES Application-aware Dictionaries (108,315 Entries)
34. DICTIONARIES Application-aware Dictionaries (22,940 Entries) (108,315 Entries)
35. DICTIONARIES Application-aware Dictionaries (22,940 Entries) (108,315 Entries) (588 Entries)
36. GRAPH MODEL Model: Weighted Directed Graph Example: drawXORRect identifier
37. GRAPH MODEL • 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
38. GRAPH MODEL • 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 “raw”,c(“raw”) “draw”,c(“draw”) “or”,c(“or”) “xor”,c(“xor”) “rectangle”,c(“rectangle”)
39. GRAPH MODEL • ARCS corresponds to matchings between identifier substrings and dictionary words • Application of the String Matching Algorithm (BYP) • 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 “raw”,c(“raw”) “draw”,c(“draw”) “or”,c(“or”) “xor”,c(“xor”) “rectangle”,c(“rectangle”)
40. GRAPH MODEL • ARCS corresponds to matchings between identifier substrings and dictionary words • Application of the String Matching Algorithm (BYP) • 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
41. GRAPH MODEL • Every Arc is Labelled with the corresponding dictionary word • Weights represent the “cost” of each matching • Cost function [c(“word”)] favors longest words and words coming from the application-aware dictionaries 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
42. GRAPH MODEL • 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
43. STRING MATCHING • Application of the Baeza-Yates and Perlberg (BYP) Algorithm • Signature: BYP(identifier, word, φ(word)) • identifier: target string • word: string to match • φ(·): Tolerance (Error) function • Bounds the length of acceptable matchings Advantage: Use the same algorithm for both the splitting and the expansion step with different input Tolerance function
44. • BYP(identifier, word, φ Split (word)) φ Split : Exact Matching (i.e., No Errors allowed) BYP FOR SPLITTING .. draw, the, are, null, handle, box, red, rectangle, ... ... echo, testing, threading, xpm, xor, .... ... abort absolute abstract ... or ... raw ... DFile D COMPUTER-SCIENCE DEnglish Identifier: drawXORRect
45. • BYP(identifier, word, φ Split (word)) φ Split : Exact Matching (i.e., No Errors allowed) BYP FOR SPLITTING .. draw, the, are, null, handle, box, red, rectangle, ... ... echo, testing, threading, xpm, xor, .... ... abort absolute abstract ... or ... raw ... 0 11 1 9 4 5 7 8 2 6 3 10 DFile D COMPUTER-SCIENCE DEnglish Identifier: drawXORRect
46. • BYP(identifier, word, φ Split (word)) φ Split : Exact Matching (i.e., No Errors allowed) BYP FOR SPLITTING .. draw, the, are, null, handle, box, red, rectangle, ... ... echo, testing, threading, xpm, xor, .... ... abort absolute abstract ... or ... raw ... 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 “R”,C-MAX DFile D COMPUTER-SCIENCE DEnglish Identifier: drawXORRect
47. • BYP(identifier, word, φ Split (word)) φ Split : Exact Matching (i.e., No Errors allowed) BYP FOR SPLITTING ... echo, testing, threading, xpm, xor, .... ... abort absolute abstract ... or ... raw ... 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 “draw”,c(“draw”) “R”,C-MAX .. draw, the, are, null, handle, box, red, rectangle, ... DFile D COMPUTER-SCIENCE DEnglish Identifier: drawXORRect
48. • BYP(identifier, word, φ Split (word)) φ Split : Exact Matching (i.e., No Errors allowed) BYP FOR SPLITTING ... abort absolute abstract ... or ... raw ... 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 “draw”,c(“draw”) “xor”,c(“xor”) “R”,C-MAX .. draw, the, are, null, handle, box, red, rectangle, ... DFile ... echo, testing, threading, xpm, xor, .... D COMPUTER-SCIENCE DEnglish Identifier: drawXORRect
49. • BYP(identifier, word, φ Split (word)) φ Split : Exact Matching (i.e., No Errors allowed) BYP FOR SPLITTING 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”) “xor”,c(“xor”) “R”,C-MAX .. draw, the, are, null, handle, box, red, rectangle, ... DFile ... echo, testing, threading, xpm, xor, .... D COMPUTER-SCIENCE ... abort absolute abstract ... or ... raw ... DEnglish Identifier: drawXORRect
50. • BYP(identifier, word, φ Split (word)) φ Split : Exact Matching (i.e., No Errors allowed) BYP FOR SPLITTING 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”) “R”,C-MAX .. draw, the, are, null, handle, box, red, rectangle, ... DFile ... echo, testing, threading, xpm, xor, .... D COMPUTER-SCIENCE ... abort absolute abstract ... or ... raw ... DEnglish Identifier: drawXORRect
51. • BYP(identifier, word, φ Split (word)) φ Split : Exact Matching (i.e., No Errors allowed) BYP FOR SPLITTING 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”) “R”,C-MAX .. draw, the, are, null, handle, box, red, rectangle, ... DFile ... echo, testing, threading, xpm, xor, .... D COMPUTER-SCIENCE ... abort absolute abstract ... or ... raw ... DEnglish Identifier: drawXORRect
52. • BYP(identifier, word, φ Exp (word)) • φ Exp : Approximate Matching BYP FOR EXPANSION 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”) “R”,C-MAX .. draw, the, are, null, handle, box, red, rectangle, ... ... echo, testing, threading, xpm, xor, .... D COMPUTER-SCIENCE ... abort absolute abstract ... or ... raw ... DEnglish DFile Identifier: drawXORRect
53. • BYP(identifier, word, φ Exp (word)) • φ Exp : Approximate Matching BYP FOR EXPANSION 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 “draw”,c(“draw”) “xor”,c(“xor”) “R”,C-MAX .. draw, the, are, null, handle, box, red, rectangle, ... ... echo, testing, threading, xpm, xor, .... D COMPUTER-SCIENCE ... abort absolute abstract ... or ... raw ... DEnglish DFile Identifier: drawXORRect
54. • BYP(identifier, word, φ Exp (word)) • φ Exp : Approximate Matching BYP FOR EXPANSION 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 “draw”,c(“draw”) “xor”,c(“xor”) “R”,C-MAX ... echo, testing, threading, xpm, xor, .... D COMPUTER-SCIENCE ... abort absolute abstract ... or ... raw ... DEnglish “red”,c(“red”) .. draw, the, are, null, handle, box, red, rectangle, ... DFile Identifier: drawXORRect
55. • BYP(identifier, word, φ Exp (word)) • φ Exp : Approximate Matching BYP FOR EXPANSION 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 “draw”,c(“draw”) “xor”,c(“xor”) “R”,C-MAX ... echo, testing, threading, xpm, xor, .... D COMPUTER-SCIENCE ... abort absolute abstract ... or ... raw ... DEnglish “red”,c(“red”) “rectangle”,c(“rectangle”) .. draw, the, are, null, handle, box, red, rectangle, ... DFile Identifier: drawXORRect
56. • BYP(identifier, word, φ Exp (word)) • φ Exp : Approximate Matching BYP FOR EXPANSION 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 “draw”,c(“draw”) “xor”,c(“xor”) “R”,C-MAX ... echo, testing, threading, xpm, xor, .... D COMPUTER-SCIENCE ... abort absolute abstract ... or ... raw ... DEnglish “red”,c(“red”) “rectangle”,c(“rectangle”) .. draw, the, are, null, handle, box, red, rectangle, ... DFile Identifier: drawXORRect
57. EMPIRI CAL E V A L U A T I O N RESEARCH QUESTIONS • RQ1: How does LINSEN compare with state-of-the-art approaches as for the splitting of identifiers?
58. EMPIRI CAL E V A L U A T I O N RESEARCH QUESTIONS • RQ1: How does LINSEN compare with state-of-the-art approaches as for the splitting of identifiers? • RQ2: How does LINSEN compare with state-of-the-art approaches as for the mapping of identifiers to dictionary words?
59. EMPIRI CAL E V A L U A T I O N RESEARCH QUESTIONS • RQ1: How does LINSEN compare with state-of-the-art approaches as for the splitting of identifiers? • RQ2: How does LINSEN compare with state-of-the-art approaches as for the mapping of identifiers to dictionary words? • RQ3: What is the ability of the LINSEN approach in dealing with different types of abbreviations?
60. CASE STUDIES DTW (Madani et. al 2010) GenTest+Normalize (Lawrie and Binkley, 2011) RQ1 and RQ2 } RQ1 only LUDISO Dataset (2012) AMAP (Hill and Pollock, 2008) RQ3 only 15 out of 750 software systems Covering the 58% of total identifiers EMPIRI CAL E V A L U A T I O N
61. EVALUATION METRICS Comparability of Results: Accuracy rate Qualitative Evaluation: Precision/Recall/F-1 [Guerrouj, et.al , 2011] EMPIRI CAL E V A L U A T I O N
62. EVALUATION METRICS Comparability of Results: Accuracy rate Qualitative Evaluation: Precision/Recall/F-1 [Guerrouj, et.al , 2011] EMPIRI CAL E V A L U A T I O N
63. EVALUATION METRICS Comparability of Results: Accuracy rate • Identifier Level evaluation: Each mapping result must be completely correct • Soft-word Level evaluation: “Partial credit” given to each word correctly mapped Qualitative Evaluation: Precision/Recall/F-1 [Guerrouj, et.al , 2011] As for the comparison with GenTest+Normalize (Lawrie and Binkley, 2011) EMPIRI CAL E V A L U A T I O N
64. RQ1: 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 R Q 1
65. Accuracy Rates for the comparison with GenTest (Lawrie and Binkley, 2011) 0 0,175 0,35 0,525 0,7 which 2.20 a2ps 4.14 Identifier Level 0 0,2 0,4 0,6 0,8 which 2.20 a2ps 4.14 Soft-word Level GenTest LINSEN GenTest LINSEN RE SULTS R Q 1 RQ1: SPLITTING
66. 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 RQ2: MAPPING RE SULTS R Q 2
67. Accuracy Rates for the comparison with Normalize (Lawrie and Binkley, 2011) 0 0,15 0,3 0,45 0,6 which 2.20 a2ps 4.14 Identifier Level 0 0,225 0,45 0,675 0,9 which 2.20 a2ps 4.14 Soft-word Level Normalize LINSEN Normalize LINSEN RE SULTS R Q 2 RQ2: MAPPING
68. Accuracy Rates for the comparison with AMAP (Hill and Pollock, 2008) 0 0,225 0,45 0,675 0,9 CW DL OO AC PR SL AMAP LINSEN RQ3: EXPANSION RE SULTS R Q 3 CW: Combination Words DL: Dropped Letters OO: Others AC: Acronyms PR: Prefix SL: Single Letters
69. CONCLUSIONS
70. CONCLUSIONS
71. CONCLUSIONS
72. CONCLUSIONS
73. CONCLUSIONS
74. FUTURE WORKS • Evaluation of the impact of each adopted dictionary on the performance • Improve or change or add dictionaries • Improve the implementation of the prototype to speed up the computation • Make use of parallel computation to process each identifier in isolation
75. THANK YOU 26th Sept. 2012, ICSM2012@Riva del Garda(Trento), Italy Valerio Maggio Ph.D. Student, University of Naples “Federico II” valerio.maggio@unina.it