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Icpc11b.ppt

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Yann-Gaël Guéhéneuc
Yann-Gaël Guéhéneuc

The document discusses comparing different feature location techniques (FLTs) that use different identifier splitting algorithms. It describes building an "oracle" identifier splitting to use as a baseline for comparison by manually splitting identifiers. Two FLTs (Information Retrieval and dynamic Information Retrieval) are compared using two software systems and four datasets when using the oracle versus CamelCase splitting. The results show the FLTs using the oracle splitting generally perform better, especially on datasets with features rather than bugs. Percentage improvements of the oracle over CamelCase are also presented.

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Can Better Identifier Splitting T h i H l F L i ? B d Di L if G j D P h k Gi li A i l Techniques Help Feature Location? Bogdan Dit, Latifa Guerrouj, Denys Poshyvanyk, Giuliano Antoniol SEMERU 19th IEEE International Conference on Program Comprehension (ICPC’11) – Kingston, Ontario, Canada
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Textual information embeds d i k l ddomain knowledge 3
Textual information embeds d i k l ddomain knowledge About 70% of source codeAbout 70% of source code consists of identifiers* * Deissenboeck, F. and Pizka , M., "Concise and Consistent Naming", Software Quality Journal, vol. 14, no. 3, 2006, pp. 261-282 4
Textual information embeds d i k l ddomain knowledge About 70% of source codeAbout 70% of source code consists of identifiers* Identifiers are important source ofIdentifiers are important source of information for maintenance tasks:information for maintenance tasks: • traceability link recovery • feature location * Deissenboeck, F. and Pizka , M., "Concise and Consistent Naming", Software Quality Journal, vol. 14, no. 3, 2006, pp. 261-282 feature location 5
# of Cumulative Feature Location Papers based on Textual InformationPapers based on Textual Information 6
Related Work on IdentifiersRelated Work on Identifiers • Takang et al (JPL’96)• Takang et al. (JPL 96) – programs with full-word identifiers are more understandable than those with abbreviatedunderstandable than those with abbreviated ones • Lawrie et al (ICPC’06)• Lawrie et al. (ICPC 06) – full words and recognizable abbreviations lead to better comprehensionlead to better comprehension • Binkley et al. (ICPC’09) CamelCase style is easier to recognize than– CamelCase style is easier to recognize than underscore 7
Related Work on IdentifiersRelated Work on Identifiers • Enslen et al (MSR’09)• Enslen et al. (MSR 09) – Samurai: algorithm for splitting identifiers (using tables of identifier frequencies)(using tables of identifier frequencies) • Guerrouj et al. (JSME’11) TIDIER: algorithm for splitting identifiers– TIDIER: algorithm for splitting identifiers (using contextual information) Other related work• Other related work – Deissenboeck and Pizka (SQJ’06), Antoniol et al (ICSM’07) Haiduc and Marcus (ICPC’08)al. (ICSM’07), Haiduc and Marcus (ICPC’08), etc. 8
Splitting Identifiers Correctly is h llChallenging 9
Identifier Splitting AlgorithmsIdentifier Splitting Algorithms Original Identifier userId setGID print file2deviceprint_file2device SSLCertificate MINstring USERID currentsize readadapterobjectp j tolocale imitating DEFMASKBitDEFMASKBit 10
Identifier Splitting AlgorithmsIdentifier Splitting Algorithms Original Identifier Camel Case userId user Id setGID set GID print file2device print file 2 deviceprint_file2device print file 2 device SSLCertificate SSL Certificate MINstring MI Nstring USERID USERID currentsize currentsize readadapterobject readadapterobjectp j p j tolocale tolocale imitating imitating DEFMASKBit DEFMASK BitDEFMASKBit DEFMASK Bit 11
Identifier Splitting AlgorithmsIdentifier Splitting Algorithms Original Identifier Camel Case Handles underscore and userId user Id setGID set GID print file2device print file 2 device underscore and digits print_file2device print file 2 device SSLCertificate SSL Certificate MINstring MI Nstring USERID USERID currentsize currentsize readadapterobject readadapterobject Fails at mixed casesp j p j tolocale tolocale imitating imitating DEFMASKBit DEFMASK BitDEFMASKBit DEFMASK Bit 12 Fails at same case identifiers
Identifier Splitting AlgorithmsIdentifier Splitting Algorithms Original Identifier Camel Case userId user Id setGID set GID print file2device print file 2 deviceprint_file2device print file 2 device SSLCertificate SSL Certificate MINstring MI Nstring USERID USERID currentsize currentsize readadapterobject readadapterobjectp j p j tolocale tolocale imitating imitating DEFMASKBit DEFMASK BitDEFMASKBit DEFMASK Bit 13
Identifier Splitting AlgorithmsIdentifier Splitting Algorithms Original Identifier Camel Case Samurai userId user Id user Id setGID set GID set GID print file2device print file 2 device print file 2 deviceprint_file2device print file 2 device print file 2 device SSLCertificate SSL Certificate SSL Certificate MINstring MI Nstring MIN string USERID USERID USER ID currentsize currentsize current size readadapterobject readadapterobject read adapter objectp j p j p j tolocale tolocale tol ocal e imitating imitating imi ta ting DEFMASKBit DEFMASK Bit DEF MASK BitDEFMASKBit DEFMASK Bit DEF MASK Bit 14
Identifier Splitting AlgorithmsIdentifier Splitting Algorithms Original Identifier Camel Case Samurai userId user Id user Id setGID set GID set GID print file2device print file 2 device print file 2 device Splits some cases where CamelCase print_file2device print file 2 device print file 2 device SSLCertificate SSL Certificate SSL Certificate MINstring MI Nstring MIN string cannot USERID USERID USER ID currentsize currentsize current size readadapterobject readadapterobject read adapter objectp j p j p j tolocale tolocale tol ocal e imitating imitating imi ta ting DEFMASKBit DEFMASK Bit DEF MASK BitDEFMASKBit DEFMASK Bit DEF MASK Bit 15 Oversplits
# of Cumulative Feature Location Papers based on Textual InformationPapers based on Textual Information 16
# of Cumulative Feature Location Papers based on Textual InformationPapers based on Textual Information Existing feature location techniques use Camel Case splittinguse Camel Case splitting 17
Information Retrieval FLTInformation Retrieval FLT • Generate corpus synchronized void print(TestResult result, l Ti ) h IOE i { • Preprocessing – Remove non-literals – Remove stop words long runTime) throws IOException{ printHeader(runTime); printErrors(result); printFailures(result);Remove stop words – Split identifiers – Stemming I d i p ( ); printFooter(result); } synchronized void print TestResult result • Indexing – Term-by-document matrix – Singular Value synchronized void print TestResult result long runTime throws IOException printHeader runTime printErrors result printFailures result printFooter result g Decomposition • User formulate query G t lt print TestResult result runTime IOException printHeader runTime • Generate results • Ranked list 18 O cept o p t eade u e printErrors result printFailures result printFooter result
Information Retrieval FLTInformation Retrieval FLT • Generate corpus print Test Result result run Time IO E ti i t H d Ti i t • Preprocessing – Remove non-literals – Remove stop words Exception print Header run Time print Errors result print Failures result print Footer result Remove stop words – Split identifiers – Stemming I d i print test result result run time io exception print head run time print error • Indexing – Term-by-document matrix – Singular Value result print fail result print foot result g Decomposition • User formulate query G t lt print test result ... m1 5 1 3 ... • Generate results • Ranked list 19 m2 ... ... ... ...
Information Retrieval FLTInformation Retrieval FLT • Generate corpus print test result ... • Preprocessing – Remove non-literals – Remove stop words m1 5 1 3 ... m2 ... ... ... ... Remove stop words – Split identifiers – Stemming I d i• Indexing – Term-by-document matrix – Singular Valueg Decomposition • User formulate query G t lt• Generate results • Ranked list 20
IR and Dynamic Information FLTIR and Dynamic Information FLT • Generate corpus • Preprocessing – Remove non-literals – Remove stop wordsRemove stop words – Split identifiers – Stemming I d i• Indexing – Term-by-document matrix – Singular Value Decompositiong p • User formulate query Collect execution trace • Generate results • Ranked list of executed methods 21
R h G lResearch Goal Evaluate how advanced splitting techniques impact th f f f t l ti t h ithe performance of feature location techniques 22
Information Retrieval FLTInformation Retrieval FLT • Generate corpus • Preprocessing – Remove non-literals – Remove stop words Replace Camel Case with : •SamuraiRemove stop words – Split identifiers – Stemming I d i g ( ) •“Perfect” Splitting algorithm (Oracle) • Indexing – Term-by-document matrix – Singular Value B g Decomposition • User formulate query G t lt etterW • Generate results • Ranked list 23 Worst
Extract Identifiers AllAll Identifiers B ildiBuilding the Oracle 24
Extract Identifiers All Same split?All Identifiers p (CamelCase Samurai TIDIER) B ildi Concordant YES Building the Oracle Concordant Split Identifiers 25
Extract Identifiers All Same split?All Identifiers p (CamelCase Samurai TIDIER) • Assume they are correct B ildi Concordant YES • Manually verified a sample Building the Oracle Concordant Split Identifiers 26 sample • Threat to validity
Manually Split IdentifiersIdentifiers Extract Identifiers Manual Split All Discordant Split Same split? NO All Identifiers Split Identifiers p (CamelCase Samurai TIDIER) B ildi Concordant YES Building the Oracle Concordant Split Identifiers 27
Manually Split IdentifiersIdentifiers Extract Identifiers Manual Split Consensus between authors All Discordant Split Same split? NO Checked source codeAll Identifiers Split Identifiers p (CamelCase Samurai TIDIER) Concordant YES B ildi Concordant Split Identifiers Building the Oracle 28
Manually Split Identifiers Identifiers that could not be split • Examples: DT, i3, Identifiersnot be split P754, zzz, etc. • Left unchanged Extract Identifiers Manual Split Left unchanged All Discordant Split Same split? NO All Identifiers Split Identifiers p (CamelCase Samurai TIDIER) Concordant YES B ildi Concordant Split Identifiers Building the Oracle 29
Design of the Case StudyDesign of the Case Study 30
Design of the Case StudyDesign of the Case Study • RQ: Does a FLT with an advanced• RQ: Does a FLT with an advanced splitting algorithm produce better results than the same FLT using the CamelCasethan the same FLT using the CamelCase splitting algorithm? 31
How to Compare two FLTs?How to Compare two FLTs? 32
How to Compare two FLTs? • Effectiveness measure for each feature How to Compare two FLTs? • Effectiveness measure for each feature Method LSI IR G ld t th d Method LSI score M121 0.92 M64 0.89 Gold set methodM15 0.86 M39 0.80 M7 0.74 M 0 65M152 0.65 M234 0.56 M12 0.54 M 0 52 Effectiveness = 5 M78 0.52 33
How to Compare two FLTs? • Effectiveness measure for each feature How to Compare two FLTs? • Effectiveness measure for each feature Method LSI IR IRDynMethod LSI score M121 0.92 M64 0.89 Method LSI score M15 0.86Gold set method y M15 0.86 M39 0.80 M7 0.74 M 0 65 M7 0.74 M234 0.56 M12 0.54 Effectiveness = 2M152 0.65 M234 0.56 M12 0.54 M 0 52 ect e ess M78 0.52 34Method Executed method (from trace)
Which FLTs are we Comparing?Which FLTs are we Comparing? 35
Software SystemsSoftware Systems • Rhino 1 6R5• Rhino 1.6R5 • 138 classes, 1,870 methods, 32K LOC E dd l ’ d *• Eaddy et al.’s data* • 2 datasets Dataset Size Queries Gold Sets Execution Information RhinoFeatures 241 Sections of ECMAScript documentation Eaddy et al.* Full Execution Traces (from unit tests) Rhi 143 B titl d E dd t l * N/A 36 * http://www.cs.columbia.edu/~eaddy/concerntagger/ RhinoBugs 143 Bug title and description Eaddy et al.* (CVS) N/A
Software SystemsSoftware Systems • jEdit 4 3• jEdit 4.3 • 483 classes, 6.4K methods, 109K LOC 2 d t t• 2 datasets Dataset Size Queries Gold Sets Execution Information jEditFeatures 64 Feature (or Patch) title and description SVN Marked Execution Traces jEditBugs 86 Bug title and description SVN Marked Execution Traces 37 Datasets available at: http://www.cs.wm.edu/semeru/data/icpc11-identifier-splitting/
Generating the jEdit Datasets SVN Commits between v4.2-v4.3 38
Generating the jEdit Datasets SVN commit message Title Description + Query = Query
Generating the jEdit Datasets Changed files Previous V i Current V i Compare using Eclipse AST Version Version c pse S Modified methods (gold set) 40
Presenting the Results 41
Presenting the Results Box plot of all effectiveness measure in datasets (e.g., 241 datapoints for RhinoFeatures) Average Median 42
IR FLTs RhinoFeaturesRhinoFeatures
IR FLTs S RhinoFeatures Similar median and average RhinoFeatures
IR FLTs S RhinoFeatures RhinoBugs Similar median and average RhinoFeatures RhinoBugs 45jEditFeatures jEditBugs
IR FLTs S RhinoFeatures RhinoBugs Similar median and average RhinoFeatures RhinoBugs Datasets with features have better results than datasets with bugs 46jEditFeatures jEditBugs
IRDyn FLTs S N/A RhinoFeatures RhinoBugs Similar median and average RhinoFeatures RhinoBugs 47jEditFeatures jEditBugs
IRDyn FLTs S N/A RhinoFeatures RhinoBugs Similar median and average RhinoFeatures RhinoBugs Datasets withatasets t features have better results than datasetsthan datasets with bugs 48jEditFeatures jEditBugs
Compare FLTs by PercentagesCompare FLTs by Percentages IROracle IRCamelCase (Baseline)(Baseline) 10 17 20 1520 15 18 18 5 9 4 16 19 7 12 28 49 14 15
Compare FLTs by PercentagesCompare FLTs by Percentages IROracle IRCamelCase (Baseline) 5/8 (Baseline) 10 17 20 1520 15 18 18 2/8 5 9 4 16 2/8 19 7 12 28 50 14 15
IRIR RhinoFeatures RhinoBugs 51 jEditFeatures jEditBugs
IRIR Datasets with features RhinoFeatures RhinoBugs Datasets with features vs. Datasets with bugs 52 jEditFeatures jEditBugs
IRDyn N/A Similar trend RhinoFeatures RhinoBugs 53 jEditFeatures jEditBugs
Statistical ResultsStatistical Results • Wilcoxon signed-rank test• Wilcoxon signed rank test • Null hypothesis Th i t ti ti l i ifi diff– There is no statistical significance difference in terms of effectiveness between IR /IR l and IR lIRSamurai/IROracle and IRCamelCase • Alternative hypothesis IR /IR h t ti ti ll i ifi tl– IRSamurai/IROracle has statistically significantly higher effectiveness than IRCamelCase l h 0 05• alpha = 0.05 54
IRIR RhinoFeatures RhinoBugsThe only statistical significant resultsignificant result (p=0.05) 55 jEditFeatures jEditB
Qualitative ResultsQualitative Results • Vocabulary mismatch between queries and code:and code: – Name of developers (e.g., Slava, Carlos) Id ifi ifi i i (– Identifiers specific to communication (e.g., thanks, greetings, annoying) 56
Qualitative ResultsQualitative Results • Features are more “descriptive” than bugsbugs 57
Qualitative ResultsQualitative Results • Features are more “descriptive” than bugsbugs Words “join” and “line” are not mentioned 58
Threats to ValidityThreats to Validity • External – 2 Java applications (different domains) – More systems needed • Construct – Errors may be present in Oracle and gold setsy p g – We used data produced by other researchers • Internal• Internal – Subjectivity and bias in building the Oracle Conclusion• Conclusion – Non-parametric test: Wilcoxon signed-rank 59
Research Questions • RQ1 Does IRSamurai outperform IRCamelCase in terms of effectiveness? NO • RQ2 Does IRS iDyn outperform IRC lC Dyn• RQ2 Does IRSamuraiDyn outperform IRCamelCaseDyn in terms of effectiveness? NO • RQ3 Does IROracle outperform IRCamelCase in terms f ff ti ? I (Rhi )of effectiveness? In some cases (Rhino) • RQ4 Does IROracleDyn outperform IRCamelCaseDyn in terms of effectiveness? NO 60
Future WorkFuture Work • More systems and datasets• More systems and datasets • Different maintenance tasks T bilit li k– Traceability link recovery • Consider other splitting algorithms 61
ConclusionsConclusions • Advanced splitting technique could• Advanced splitting technique could improve FLTs We found some empirical evidence– We found some empirical evidence • Splitting has more impact on IR FLT f f l bl• If execution information is available, it is not necessary to use an advance splitting h itechnique 62
Thank you! Questions?Thank you! Questions? SEMERU @ William and MarySEMERU @ William and Mary http://www.cs.wm.edu/semeru/ bdi dbdit@cs.wm.edu SEMERU 63
ReferencesReferences • Takang et al. (1996) Takang, A., Grubb, P., and Macredie, R., "The Effects of Comments and Identifier Names on Program Comprehensibility: An Experimental Investigation", Journal of Programming Languages, vol. 4, no. 3, 1996, pp. 143-167 • Lawrie et al (2006) Lawrie D Morrell C Feild H and Binkley D• Lawrie et al. (2006) Lawrie, D., Morrell, C., Feild, H., and Binkley, D., "What's in a Name? A Study of Identifiers", in Proc. of IEEE ICPC'06, June 14-16 2006, pp. 3-12 • Binkley et al. (2009) Binkley, D., Davis, M., Lawrie, D., and Morrell, C.,y ( ) y, , , , , , , , "To CamelCase or Under_score", in Proc. of IEEE ICPC'09, May 17-19 2009, pp. 158-167 • Enslen et al. (2009) Enslen, E., Hill, E., Pollock, L., and Vijay-Shanker, K., "Mi i S C d A i ll S li Id ifi f S f"Mining Source Code to Automatically Split Identifiers for Software Analysis", in Proc. of IEEE MSR'09, May 16-17 2009, pp. 71-80 • Guerrouj et al. (2011) Guerrouj, L., Di Penta, M., Antoniol, G., and Guéhéneuc Y -G "TIDIER: An Identifier Splitting Approach using SpeechGuéhéneuc, Y. G., TIDIER: An Identifier Splitting Approach using Speech Recognition Techniques", JSME, vol. to appear, 2011 64

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Icpc11b.ppt

  • 1. Can Better Identifier Splitting T h i H l F L i ? B d Di L if G j D P h k Gi li A i l Techniques Help Feature Location? Bogdan Dit, Latifa Guerrouj, Denys Poshyvanyk, Giuliano Antoniol SEMERU 19th IEEE International Conference on Program Comprehension (ICPC’11) – Kingston, Ontario, Canada
  • 2. 2
  • 3. Textual information embeds d i k l ddomain knowledge 3
  • 4. Textual information embeds d i k l ddomain knowledge About 70% of source codeAbout 70% of source code consists of identifiers* * Deissenboeck, F. and Pizka , M., "Concise and Consistent Naming", Software Quality Journal, vol. 14, no. 3, 2006, pp. 261-282 4
  • 5. Textual information embeds d i k l ddomain knowledge About 70% of source codeAbout 70% of source code consists of identifiers* Identifiers are important source ofIdentifiers are important source of information for maintenance tasks:information for maintenance tasks: • traceability link recovery • feature location * Deissenboeck, F. and Pizka , M., "Concise and Consistent Naming", Software Quality Journal, vol. 14, no. 3, 2006, pp. 261-282 feature location 5
  • 6. # of Cumulative Feature Location Papers based on Textual InformationPapers based on Textual Information 6
  • 7. Related Work on IdentifiersRelated Work on Identifiers • Takang et al (JPL’96)• Takang et al. (JPL 96) – programs with full-word identifiers are more understandable than those with abbreviatedunderstandable than those with abbreviated ones • Lawrie et al (ICPC’06)• Lawrie et al. (ICPC 06) – full words and recognizable abbreviations lead to better comprehensionlead to better comprehension • Binkley et al. (ICPC’09) CamelCase style is easier to recognize than– CamelCase style is easier to recognize than underscore 7
  • 8. Related Work on IdentifiersRelated Work on Identifiers • Enslen et al (MSR’09)• Enslen et al. (MSR 09) – Samurai: algorithm for splitting identifiers (using tables of identifier frequencies)(using tables of identifier frequencies) • Guerrouj et al. (JSME’11) TIDIER: algorithm for splitting identifiers– TIDIER: algorithm for splitting identifiers (using contextual information) Other related work• Other related work – Deissenboeck and Pizka (SQJ’06), Antoniol et al (ICSM’07) Haiduc and Marcus (ICPC’08)al. (ICSM’07), Haiduc and Marcus (ICPC’08), etc. 8
  • 9. Splitting Identifiers Correctly is h llChallenging 9
  • 10. Identifier Splitting AlgorithmsIdentifier Splitting Algorithms Original Identifier userId setGID print file2deviceprint_file2device SSLCertificate MINstring USERID currentsize readadapterobjectp j tolocale imitating DEFMASKBitDEFMASKBit 10
  • 11. Identifier Splitting AlgorithmsIdentifier Splitting Algorithms Original Identifier Camel Case userId user Id setGID set GID print file2device print file 2 deviceprint_file2device print file 2 device SSLCertificate SSL Certificate MINstring MI Nstring USERID USERID currentsize currentsize readadapterobject readadapterobjectp j p j tolocale tolocale imitating imitating DEFMASKBit DEFMASK BitDEFMASKBit DEFMASK Bit 11
  • 12. Identifier Splitting AlgorithmsIdentifier Splitting Algorithms Original Identifier Camel Case Handles underscore and userId user Id setGID set GID print file2device print file 2 device underscore and digits print_file2device print file 2 device SSLCertificate SSL Certificate MINstring MI Nstring USERID USERID currentsize currentsize readadapterobject readadapterobject Fails at mixed casesp j p j tolocale tolocale imitating imitating DEFMASKBit DEFMASK BitDEFMASKBit DEFMASK Bit 12 Fails at same case identifiers
  • 13. Identifier Splitting AlgorithmsIdentifier Splitting Algorithms Original Identifier Camel Case userId user Id setGID set GID print file2device print file 2 deviceprint_file2device print file 2 device SSLCertificate SSL Certificate MINstring MI Nstring USERID USERID currentsize currentsize readadapterobject readadapterobjectp j p j tolocale tolocale imitating imitating DEFMASKBit DEFMASK BitDEFMASKBit DEFMASK Bit 13
  • 14. Identifier Splitting AlgorithmsIdentifier Splitting Algorithms Original Identifier Camel Case Samurai userId user Id user Id setGID set GID set GID print file2device print file 2 device print file 2 deviceprint_file2device print file 2 device print file 2 device SSLCertificate SSL Certificate SSL Certificate MINstring MI Nstring MIN string USERID USERID USER ID currentsize currentsize current size readadapterobject readadapterobject read adapter objectp j p j p j tolocale tolocale tol ocal e imitating imitating imi ta ting DEFMASKBit DEFMASK Bit DEF MASK BitDEFMASKBit DEFMASK Bit DEF MASK Bit 14
  • 15. Identifier Splitting AlgorithmsIdentifier Splitting Algorithms Original Identifier Camel Case Samurai userId user Id user Id setGID set GID set GID print file2device print file 2 device print file 2 device Splits some cases where CamelCase print_file2device print file 2 device print file 2 device SSLCertificate SSL Certificate SSL Certificate MINstring MI Nstring MIN string cannot USERID USERID USER ID currentsize currentsize current size readadapterobject readadapterobject read adapter objectp j p j p j tolocale tolocale tol ocal e imitating imitating imi ta ting DEFMASKBit DEFMASK Bit DEF MASK BitDEFMASKBit DEFMASK Bit DEF MASK Bit 15 Oversplits
  • 16. # of Cumulative Feature Location Papers based on Textual InformationPapers based on Textual Information 16
  • 17. # of Cumulative Feature Location Papers based on Textual InformationPapers based on Textual Information Existing feature location techniques use Camel Case splittinguse Camel Case splitting 17
  • 18. Information Retrieval FLTInformation Retrieval FLT • Generate corpus synchronized void print(TestResult result, l Ti ) h IOE i { • Preprocessing – Remove non-literals – Remove stop words long runTime) throws IOException{ printHeader(runTime); printErrors(result); printFailures(result);Remove stop words – Split identifiers – Stemming I d i p ( ); printFooter(result); } synchronized void print TestResult result • Indexing – Term-by-document matrix – Singular Value synchronized void print TestResult result long runTime throws IOException printHeader runTime printErrors result printFailures result printFooter result g Decomposition • User formulate query G t lt print TestResult result runTime IOException printHeader runTime • Generate results • Ranked list 18 O cept o p t eade u e printErrors result printFailures result printFooter result
  • 19. Information Retrieval FLTInformation Retrieval FLT • Generate corpus print Test Result result run Time IO E ti i t H d Ti i t • Preprocessing – Remove non-literals – Remove stop words Exception print Header run Time print Errors result print Failures result print Footer result Remove stop words – Split identifiers – Stemming I d i print test result result run time io exception print head run time print error • Indexing – Term-by-document matrix – Singular Value result print fail result print foot result g Decomposition • User formulate query G t lt print test result ... m1 5 1 3 ... • Generate results • Ranked list 19 m2 ... ... ... ...
  • 20. Information Retrieval FLTInformation Retrieval FLT • Generate corpus print test result ... • Preprocessing – Remove non-literals – Remove stop words m1 5 1 3 ... m2 ... ... ... ... Remove stop words – Split identifiers – Stemming I d i• Indexing – Term-by-document matrix – Singular Valueg Decomposition • User formulate query G t lt• Generate results • Ranked list 20
  • 21. IR and Dynamic Information FLTIR and Dynamic Information FLT • Generate corpus • Preprocessing – Remove non-literals – Remove stop wordsRemove stop words – Split identifiers – Stemming I d i• Indexing – Term-by-document matrix – Singular Value Decompositiong p • User formulate query Collect execution trace • Generate results • Ranked list of executed methods 21
  • 22. R h G lResearch Goal Evaluate how advanced splitting techniques impact th f f f t l ti t h ithe performance of feature location techniques 22
  • 23. Information Retrieval FLTInformation Retrieval FLT • Generate corpus • Preprocessing – Remove non-literals – Remove stop words Replace Camel Case with : •SamuraiRemove stop words – Split identifiers – Stemming I d i g ( ) •“Perfect” Splitting algorithm (Oracle) • Indexing – Term-by-document matrix – Singular Value B g Decomposition • User formulate query G t lt etterW • Generate results • Ranked list 23 Worst
  • 26. Extract Identifiers All Same split?All Identifiers p (CamelCase Samurai TIDIER) • Assume they are correct B ildi Concordant YES • Manually verified a sample Building the Oracle Concordant Split Identifiers 26 sample • Threat to validity
  • 27. Manually Split IdentifiersIdentifiers Extract Identifiers Manual Split All Discordant Split Same split? NO All Identifiers Split Identifiers p (CamelCase Samurai TIDIER) B ildi Concordant YES Building the Oracle Concordant Split Identifiers 27
  • 28. Manually Split IdentifiersIdentifiers Extract Identifiers Manual Split Consensus between authors All Discordant Split Same split? NO Checked source codeAll Identifiers Split Identifiers p (CamelCase Samurai TIDIER) Concordant YES B ildi Concordant Split Identifiers Building the Oracle 28
  • 29. Manually Split Identifiers Identifiers that could not be split • Examples: DT, i3, Identifiersnot be split P754, zzz, etc. • Left unchanged Extract Identifiers Manual Split Left unchanged All Discordant Split Same split? NO All Identifiers Split Identifiers p (CamelCase Samurai TIDIER) Concordant YES B ildi Concordant Split Identifiers Building the Oracle 29
  • 30. Design of the Case StudyDesign of the Case Study 30
  • 31. Design of the Case StudyDesign of the Case Study • RQ: Does a FLT with an advanced• RQ: Does a FLT with an advanced splitting algorithm produce better results than the same FLT using the CamelCasethan the same FLT using the CamelCase splitting algorithm? 31
  • 32. How to Compare two FLTs?How to Compare two FLTs? 32
  • 33. How to Compare two FLTs? • Effectiveness measure for each feature How to Compare two FLTs? • Effectiveness measure for each feature Method LSI IR G ld t th d Method LSI score M121 0.92 M64 0.89 Gold set methodM15 0.86 M39 0.80 M7 0.74 M 0 65M152 0.65 M234 0.56 M12 0.54 M 0 52 Effectiveness = 5 M78 0.52 33
  • 34. How to Compare two FLTs? • Effectiveness measure for each feature How to Compare two FLTs? • Effectiveness measure for each feature Method LSI IR IRDynMethod LSI score M121 0.92 M64 0.89 Method LSI score M15 0.86Gold set method y M15 0.86 M39 0.80 M7 0.74 M 0 65 M7 0.74 M234 0.56 M12 0.54 Effectiveness = 2M152 0.65 M234 0.56 M12 0.54 M 0 52 ect e ess M78 0.52 34Method Executed method (from trace)
  • 35. Which FLTs are we Comparing?Which FLTs are we Comparing? 35
  • 36. Software SystemsSoftware Systems • Rhino 1 6R5• Rhino 1.6R5 • 138 classes, 1,870 methods, 32K LOC E dd l ’ d *• Eaddy et al.’s data* • 2 datasets Dataset Size Queries Gold Sets Execution Information RhinoFeatures 241 Sections of ECMAScript documentation Eaddy et al.* Full Execution Traces (from unit tests) Rhi 143 B titl d E dd t l * N/A 36 * http://www.cs.columbia.edu/~eaddy/concerntagger/ RhinoBugs 143 Bug title and description Eaddy et al.* (CVS) N/A
  • 37. Software SystemsSoftware Systems • jEdit 4 3• jEdit 4.3 • 483 classes, 6.4K methods, 109K LOC 2 d t t• 2 datasets Dataset Size Queries Gold Sets Execution Information jEditFeatures 64 Feature (or Patch) title and description SVN Marked Execution Traces jEditBugs 86 Bug title and description SVN Marked Execution Traces 37 Datasets available at: http://www.cs.wm.edu/semeru/data/icpc11-identifier-splitting/
  • 38. Generating the jEdit Datasets SVN Commits between v4.2-v4.3 38
  • 39. Generating the jEdit Datasets SVN commit message Title Description + Query = Query
  • 40. Generating the jEdit Datasets Changed files Previous V i Current V i Compare using Eclipse AST Version Version c pse S Modified methods (gold set) 40
  • 42. Presenting the Results Box plot of all effectiveness measure in datasets (e.g., 241 datapoints for RhinoFeatures) Average Median 42
  • 45. IR FLTs S RhinoFeatures RhinoBugs Similar median and average RhinoFeatures RhinoBugs 45jEditFeatures jEditBugs
  • 46. IR FLTs S RhinoFeatures RhinoBugs Similar median and average RhinoFeatures RhinoBugs Datasets with features have better results than datasets with bugs 46jEditFeatures jEditBugs
  • 47. IRDyn FLTs S N/A RhinoFeatures RhinoBugs Similar median and average RhinoFeatures RhinoBugs 47jEditFeatures jEditBugs
  • 48. IRDyn FLTs S N/A RhinoFeatures RhinoBugs Similar median and average RhinoFeatures RhinoBugs Datasets withatasets t features have better results than datasetsthan datasets with bugs 48jEditFeatures jEditBugs
  • 49. Compare FLTs by PercentagesCompare FLTs by Percentages IROracle IRCamelCase (Baseline)(Baseline) 10 17 20 1520 15 18 18 5 9 4 16 19 7 12 28 49 14 15
  • 50. Compare FLTs by PercentagesCompare FLTs by Percentages IROracle IRCamelCase (Baseline) 5/8 (Baseline) 10 17 20 1520 15 18 18 2/8 5 9 4 16 2/8 19 7 12 28 50 14 15
  • 52. IRIR Datasets with features RhinoFeatures RhinoBugs Datasets with features vs. Datasets with bugs 52 jEditFeatures jEditBugs
  • 54. Statistical ResultsStatistical Results • Wilcoxon signed-rank test• Wilcoxon signed rank test • Null hypothesis Th i t ti ti l i ifi diff– There is no statistical significance difference in terms of effectiveness between IR /IR l and IR lIRSamurai/IROracle and IRCamelCase • Alternative hypothesis IR /IR h t ti ti ll i ifi tl– IRSamurai/IROracle has statistically significantly higher effectiveness than IRCamelCase l h 0 05• alpha = 0.05 54
  • 55. IRIR RhinoFeatures RhinoBugsThe only statistical significant resultsignificant result (p=0.05) 55 jEditFeatures jEditB
  • 56. Qualitative ResultsQualitative Results • Vocabulary mismatch between queries and code:and code: – Name of developers (e.g., Slava, Carlos) Id ifi ifi i i (– Identifiers specific to communication (e.g., thanks, greetings, annoying) 56
  • 57. Qualitative ResultsQualitative Results • Features are more “descriptive” than bugsbugs 57
  • 58. Qualitative ResultsQualitative Results • Features are more “descriptive” than bugsbugs Words “join” and “line” are not mentioned 58
  • 59. Threats to ValidityThreats to Validity • External – 2 Java applications (different domains) – More systems needed • Construct – Errors may be present in Oracle and gold setsy p g – We used data produced by other researchers • Internal• Internal – Subjectivity and bias in building the Oracle Conclusion• Conclusion – Non-parametric test: Wilcoxon signed-rank 59
  • 60. Research Questions • RQ1 Does IRSamurai outperform IRCamelCase in terms of effectiveness? NO • RQ2 Does IRS iDyn outperform IRC lC Dyn• RQ2 Does IRSamuraiDyn outperform IRCamelCaseDyn in terms of effectiveness? NO • RQ3 Does IROracle outperform IRCamelCase in terms f ff ti ? I (Rhi )of effectiveness? In some cases (Rhino) • RQ4 Does IROracleDyn outperform IRCamelCaseDyn in terms of effectiveness? NO 60
  • 61. Future WorkFuture Work • More systems and datasets• More systems and datasets • Different maintenance tasks T bilit li k– Traceability link recovery • Consider other splitting algorithms 61
  • 62. ConclusionsConclusions • Advanced splitting technique could• Advanced splitting technique could improve FLTs We found some empirical evidence– We found some empirical evidence • Splitting has more impact on IR FLT f f l bl• If execution information is available, it is not necessary to use an advance splitting h itechnique 62
  • 63. Thank you! Questions?Thank you! Questions? SEMERU @ William and MarySEMERU @ William and Mary http://www.cs.wm.edu/semeru/ bdi dbdit@cs.wm.edu SEMERU 63
  • 64. ReferencesReferences • Takang et al. (1996) Takang, A., Grubb, P., and Macredie, R., "The Effects of Comments and Identifier Names on Program Comprehensibility: An Experimental Investigation", Journal of Programming Languages, vol. 4, no. 3, 1996, pp. 143-167 • Lawrie et al (2006) Lawrie D Morrell C Feild H and Binkley D• Lawrie et al. (2006) Lawrie, D., Morrell, C., Feild, H., and Binkley, D., "What's in a Name? A Study of Identifiers", in Proc. of IEEE ICPC'06, June 14-16 2006, pp. 3-12 • Binkley et al. (2009) Binkley, D., Davis, M., Lawrie, D., and Morrell, C.,y ( ) y, , , , , , , , "To CamelCase or Under_score", in Proc. of IEEE ICPC'09, May 17-19 2009, pp. 158-167 • Enslen et al. (2009) Enslen, E., Hill, E., Pollock, L., and Vijay-Shanker, K., "Mi i S C d A i ll S li Id ifi f S f"Mining Source Code to Automatically Split Identifiers for Software Analysis", in Proc. of IEEE MSR'09, May 16-17 2009, pp. 71-80 • Guerrouj et al. (2011) Guerrouj, L., Di Penta, M., Antoniol, G., and Guéhéneuc Y -G "TIDIER: An Identifier Splitting Approach using SpeechGuéhéneuc, Y. G., TIDIER: An Identifier Splitting Approach using Speech Recognition Techniques", JSME, vol. to appear, 2011 64