TRIS is a fast and accurate approach for splitting and expanding source code identifiers. It treats identifier splitting as an optimization problem and uses a two-phase strategy: 1) building a dictionary of terms and transformation rules, and 2) applying these to split identifiers. A case study found TRIS outperformed other approaches like Camel Case splitting, Samurai, and TIDIER in accuracy on two software projects. TRIS also had higher accuracy than Samurai and GenTest on a large dataset of identifier split correctness.

1. TRIS: a Fast and Accurate IdentifiersTRIS: a Fast and Accurate Identifiers
Splitting and ExpansionSplitting and ExpansionSplitting and ExpansionSplitting and Expansion
AlgorithmAlgorithm
WCRE 2012, Kingston, CanadaWCRE 2012, Kingston, Canada
Latifa Guerrouj, Philippe Galinier, Yann-Gaël Guéhéneuc,
Giuliano Antoniol, and Massimiliano Di Penta

2. Content
Research Context
Current Practices
TRIS in Essence
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TRIS in Essence
Case Study
Threats to Validity
Conclusion

3. Research Context
Researchers agree that the identifier semantics areResearchers agree that the identifier semantics are
important:important:
Help program comprehension: [CT99, DP05, LFB06]…;
Improve software quality: [MPF08, LMFB06, JH06]…;
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Suggest clues: [TGM96, LMFB07]...
Composed identifiers:Composed identifiers:
Camel Case: MyLocalAccount, User_Address
Contraction based: pntr ctr, usrAdrss , imagEdge
Good and possibly known to the developers:
hmmm, ixoth , pqrstuvwxyz

4. Current Practices
Camel CaseCamel Case--based approaches [based approaches [LFB06LFB06]]
Samurai bySamurai by EnslenEnslen [ELK09]:[ELK09]:
Mines term frequencies in large source code bases and relies on two
assumptions:
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1) A substring composing an identifier is also likely to be used in
other parts of the program or in other programs.
2) Given two possible splits of a given identifier, the split that
most likely represents the developer’s intent partitions the
identifier into terms occurring more often in the program.
Abbreviations not treated, no quantification of howAbbreviations not treated, no quantification of how
close the match is to the unknown string.close the match is to the unknown string.

5. Current Practices
TIDIER [TIDIER [GMAGMA11]:11]:
Inspired by speech recognition techniques and uses Dynamic
Time Warping to match terms to domain concepts.
Exploits context in the form of specialized dictionaries and
mimics the process of transforming words via contraction
rules.
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GenTestGenTest/Normalize [LBM10, LB11]:/Normalize [LBM10, LB11]:
GenTest generates all possible splittings and evaluates a
scoring function against each proposed splitting.
Normalize uses a machine translation technique, the
maximum coherence model.
TIDIER,TIDIER, GenTestGenTest and Normalize are demandingand Normalize are demanding
in terms of computation time.in terms of computation time.

6. TRIS in Essence
TRIS assumes that developers compose identifiers:TRIS assumes that developers compose identifiers:
Using terms and words reflecting domain concepts developer’s
experience, knowledge.
TRIS uses set of transformation rules:TRIS uses set of transformation rules:
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Drop vowels, drop prefix, drop suffix, etc.
TRISTRIS treats the problem as an optimization problemtreats the problem as an optimization problem
where the cost function is:where the cost function is:
C(wOrig w) = α*Freq(wOrig) + C(type(wOrig w))
Freq(wOrig): frequency of wOrig in the source code
C(type(wOrig w)): cost of the transformation type

7. TRIS in Essence
TRIS applies a twoTRIS applies a two--phase strategy:phase strategy:
1) Building dictionary transformation1) Building dictionary transformation
Computation of the frequency of dictionary words
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Construction of the set of transformations
Construction of the arborescence
2) Identifier processing algorithm2) Identifier processing algorithm
Construction of the auxiliary graph of the identifier
Search for a shortest path in the auxiliary graph

8. TRIS in Essence
TRIS phasesTRIS phases –– Example:Example:
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Dictionary Transformations Building Information for the Identifier
callableint

9. TRIS in Essence
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Arborescence of Transformations for the Dictionary D
Auxiliary Graph for the Identifier callableint

10. Case Study - Research Question
Quality focus:
Accuracy of TRIS with respect to oracles, and compared with state-
of-the-art approaches: 1) Camel Case Splitter, 2) Samurai, 3)
TIDIER, and 4) GenTest.
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TIDIER, and 4) GenTest.
Research Question:
What is the accuracy of the TRIS identifier splitting and expansion
approach compared with alternative state-of-the art approaches?

11. Case Study – Analyzed Systems
JHotDraw – Java
16 KLOC
155 files
2,348 identifiers (longer than 2 chars)
957 manually segmented identifiers
Lynx – C
174 KLOC
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174 KLOC
247 files
12,194 identifiers (longer than 2 chars)
3,085 manually segmented identifiers
Lawrie et al. Data Set
186 programs
26 MLOC of C
15 MLOC of C++
7 MLOC of Java
489 C/C++ sampled from 430 GNU projects

12. Case Study - Results
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Precision, recall and F-measure of TRIS, Camel Case, Samurai,
and TIDIER on JHotDraw

13. Case Study - Results
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Precision, recall and F-measure of TRIS, Camel Case, Samurai,
and TIDIER on Lynx

14. Case Study - Results
Comparison among approaches: Results of Wilcoxon paired test
and Cliff’s Delta effect size on JHotDraw
Approach 1 Approach 2 Adj p-value Cliff's delta
TRIS Camel Case 0.431 0.041
TRIS Samurai 0.894 0.001
TRIS TIDIER 0.024 0.043
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Comparison among approaches: Results of Wilcoxon paired test
and Cliff’s Delta effect size on Lynx
Approach 1 Approach 2 Adj p-value Cliff's delta
TRIS Camel Case <0.001 0.743
TRIS Samurai <0.001 0.684
TRIS TIDIER <0.001 0.204
Cliff’s delta Interpretation:
- small for 0.148 <= d <0.33
- medium for 0.33 <= d < 0.474
- large for d >= 0.474

15. Case Study - Results
Metric Approach 1Q Median Mean 3Q σ
Precision TIDIER 0.4000 0.6667 0.6368 1.0000 0.3681
TRIS 1.0000 1.0000 0.8933 1.0000 0.2471
Recall TIDIER 0.5000 0.6667 0.6496 1.0000 0.3654
TRIS 1.0000 1.0000 0.8720 1.0000 0.2606
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Precision, recall and F-measure of TRIS and TIDIER on the
489 C Sampled Identifiers
TRIS 1.0000 1.0000 0.8720 1.0000 0.2606
F-measure TIDIER 0.4000 0.6667 0.6409 1.0000 0.3650
TRIS 1.0000 1.0000 0.8790 1.0000 0.2524

16. Case Study - Results
Metric Approach 1Q Median Mean 3Q σ
Precision TRIS 1.0000 1.0000 0.9763 1.0000 0.1184
Recall TRIS 1.0000 1.0000 0.9439 1.0000 0.1565
F-measure TRIS 1.0000 1.0000 0.9559 1.0000 0.1358
Precision, recall and F-measure of TRIS and TIDIER on the
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Approach Identifier Splitting Correctness
Samurai 70%
GenTest 82%
TRIS 86%
Correctness of the splitting provided using the data set from
LAWRIE et al
data set from LAWRIE et al

17. Conclusion
TRIS maps the identifier splitting/expansion problem in a graph
optimization problem to find the optimal path in an identifier
acyclic weighted graph.
TRIS was applied on Java, C, and C++ samples and compared to
Camel Case, Samurai, TIDIER, and GenTest.
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Camel Case, Samurai, TIDIER, and GenTest.
Results show that TRIS outperforms other approaches with
medium to large effect size.
TRIS is also efficient in terms of computation time: quadratic
complexity in the length of the identifier.

18. Finally… Questions
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Thank you for your attention

19. References
[LB11 ] D. Lawrie and D. Binkley, “Expanding identifiers to normalize source
code vocabulary,” International Conference on Software Maintenance , 2011, pp.
113–122.
[LBM10] D. Lawrie, D. Binkley, and C. Morrell, “Normalizing source code
vocabulary,” Working Conference on Reverse Engineering, 2010, pp. 112–122.
[GMA11] L. Guerrouj, D. P. Massimiliano, A. Giuliano, and Y.-G. Guéhéneuc,
“Tidier: An identifier splitting approach using speech recognition techniques,”
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“Tidier: An identifier splitting approach using speech recognition techniques,”
Journal of Software Maintenance and Evolution: Research and Practice, 2011.
[MGD10] N. Madani, L. Guerrouj, M. Di Penta, Y. Guéhéneuc, and G. Antoniol.
“Recognizing words from source code identifiers using speech recognition
techniques”. 14th European Conference on Software Maintenance and
Reengineering, March 2010.
[ELK09] E. Enslen, E. Hill, L. Pollock, and K. Vijay-Shanker, “Mining source
code to automatically split identifiers for software analysis”. Mining Software
Repositories, International Workshop on, vol. 0, pp. 71-80, 2009.

20. References
[MPF08] A. Marcus, D. Poshyvanyk, and R. Ferenc. “Using the conceptual
cohesion of classes for fault prediction in object-oriented systems”. IEEE
Transactions on Software Engineering, 34(2):287-300, 2008.
LMFB07] Dawn Lawrie, Christopher Morrell, Henry Feild, and David Binkley.
“Effective identifier names for comprehension and memory”. Innovations in
Systems and Software Engineering, 3(4):303-318, 2007.
[LFB06] D. Lawrie, H. Feild, and D. Binkley. “Syntactic identifier conciseness
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[LFB06] D. Lawrie, H. Feild, and D. Binkley. “Syntactic identifier conciseness
and consistency”. 6th International Workshop on Source Code Analysis and
Manipulation, pages139-148, Sept 27-29, 2006.
[LMFB06] D. Lawrie, C. Morrell, H. Feild, and D. Binkley. “What’s in a name? a
study of identifiers”. 14th International Conference on Program
Comprehension, pages 3-12, Athens, Greece, 2006.
[JH06] Z. Ming Jiang and Ahmed E. Hassan. “Examining the evolution of code
comments in postgresql”. 2006 International Workshop on Mining Software
Repositories, pages 179-180, 2006.

21. References
[DP05] Florian Deissenbock and Markus Pizka. “Concise and consistent
naming”. Proceedings of the International Workshop on Program
Comprehension, May 2005.
[Ian00] Ian Sommerville. Software Engineering. Addison-Wesley, sixth edition,
2000.
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[TGM96] Armstrong Takang, Penny A. Grubb, and Robert D. Macredie. “The
effects of comments and identifier names on program comprehensibility: an
experiential study”. Journal of Program Languages, 4(3):143–167, 1996.
[HNey84] H. Ney, “The use of a one-stage dynamic programming algorithm for
connected word recognition”. Acoustics, Speech and Signal Processing, IEEE
Transactions on, vol. 32, no. 2, pp. 263-271, Apr 1984.