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David Shepherd, Lori Pollock, and K. Vijay-Shanker

Case Study: Supplementing Program Analysis with Natural Language Analysis to Improve a Reverse Engineering Task. David Shepherd, Lori Pollock, and K. Vijay-Shanker. University of Delaware. Consider the Reverse Eng Task…. Aspect-Oriented Programming , language support for:. Aspect Mining.

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David Shepherd, Lori Pollock, and K. Vijay-Shanker

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  1. Case Study: Supplementing Program Analysis with Natural Language Analysis to Improve a Reverse Engineering Task David Shepherd, Lori Pollock, and K. Vijay-Shanker • University of Delaware

  2. Consider the Reverse Eng Task… Aspect-Oriented Programming, language support for: Aspect Mining Locates refactoring candidates

  3. Classic Aspect Candidates • Before/After Aspects [Mastering AspectJ, Aspectj.com] • Certain Design Patterns [Hanneman 02] Circle.setRadius(x){ r = x; } Square.setSize(x){ size = x; } Aspect Updater After Shape.set*() Call Display.update() Display.update() Display.update() public MultiFigure(){ numFigs =n;…. } public Square(y){ side = y; } Aspect Listener After Figure.construct() Add parent as listener addListener(Drawing) addListener(MultiFigure)

  4. Current Aspect Mining Analyses Traditional Program Analyses (TPA) Code Clone Analysis Fan-In Analysis [Shepherd SERP 04, Bruntink ICSM 04] [Marin et al. WCRE 2005] Method Display.update() Call Graph Fragment Traditional Program Analyses (TPA) can be effective

  5. Consider Natural Language Analyses (NLA) Scenario • Class: DrawApplication • Methods • addViewChangeListener • removeViewChangeListener Opposite Verbs Observer Word Combo Natural Language Analyses can be effective

  6. Quiz: Is TPA Enough? Which method(s) are good candidates? Method TextAreaTool.beginEdit() Method Geom.length() A B Call Graph Fragments Traditional Program Analyses (TPA) can be misleading

  7. Quiz: Is NLA Enough? Scenario A • Method uses verb “add” • Another method in the same class uses verb “remove” Scenario B • Method uses verb “add” • Another method in the same class uses verb “remove” Natural Language Analyses can be misleading Figure.addListener() List.add()

  8. Summary: TPA & NLA Complementary Using NLA and TPA together tobuild a casefor refactoring Method Auctions.startBlocking() Fan-in is high Called in a clone Opposite verb is used Method Auctions.endBlocking() Supplementing TPA with NLA makes a stronger case for refactoring

  9. Talk Progression • Aspect Mining Framework: Timna Timna Fan-in Method signature Code clone iTimna … NLA • Integration of Natural Language in iTimna • Evaluation

  10. Timna: An Aspect Mining Framework Method name| Fan-in | Code Clone | Is Void openWindow 3 T T addListener 7 F F Timna uses machine learning to combine analyses Phase 1: Learning (occurs once) If( Fan-in >3 and Is-Void) then the method is a refactoring candidate Mining Analyses Analyses Results Mining Rules Tagged Program Learner Phase 2: Classifying (occurs many times) Mining Analyses Analyses Results Marked Program Program Classifer

  11. iTimna: Adding NLA to Timna Analyses Driver Analyses Results … Program Add the NL Analyses here NLAnalyses • Opposite verbs • Observer words • Constraint verbs TPAnalyses • Code clone • Signature analysis • Callsite pairings

  12. An Example NLA • NLA: Past-Tense Verb • Intuition: Past-Tense indicates reactionary • Process: 1. Extract verb [AOSD 06] 2. Use morphological analysis to detect verb tense • Examples: • figureSelectionChanged() • setSelection • drawingInvalidated()  past-tense  changed  set  not past-tense  past-tense  invalidated

  13. Tagger bias? Evaluation Overfitting? Enough examples? • Question: What effect does integrating NLA with TPA have on AM? • Methodology: Manually tag a program, Use AM tools, Check results • Measures: Precision (quality) and Recall (completeness) • Subject Program: JHotDraw • AM Tools: Fan-in, Timna, iTimna (2,739 methods)

  14. Quantitative Results • Fan-in Precision 37 Recall 2 • Timna Precision 62 Recall 60 • iTimna Precision81Recall73

  15. Qualitative Results NLA and Simple Program Information If (Void Return Type & Opposite Verb & No Parameter) Then the method is a candidate

  16. Qualitative Results NLA and Simple Program Information If (Not Void Return Type && Constraint Verb) Then the method is a candidate

  17. Related Work • Mining Dynamic Information [Breu ASE 04, Ceccato WCRE 04] • Mining Static Information [Marin WCRE 04, Shepherd SERP 04, Bruntink ICSM 04] • Combining/Comparing Approaches • Sorts [Marin WCRE 2006] • Qualitative Comparison [Tourwe IWPC 05] Single Analyses Simple Combinations Recommend Combining

  18. Conclusions • Created NLAs for Aspect Mining • Showed that TPA and NLA can work together • Evaluated prototype, shows strong promise Supplementing TPA with NLA improves the effectiveness of our aspect mining framework and warrants further evaluation

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