The document proposes using semantic similarity between API usage patterns and code under editing to improve automatic code completion. It discusses extracting API usage patterns and representing them and code in a semantic model. An experimental design is presented that would use semantic queries to find similar patterns for code completion suggestions, evaluated based on precision, recall and F-score. The expected results are that using semantic features rather than just syntax would provide more accurate and context-aware suggestions.

1. Research Methods & Topics
Automatic Code
Completion Using
Semantic Similarity
A Proposal for CMPT 880 Research Project
Presenter : Mohammad Masudur Rahman
(NSID: MOR543)

2. Contents
Automatic Code Completion
 Motivating Example
 Thesis Statement
 Experimental Design
 Expected Results
 Summary

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3. Automatic Code Completion
Fig: Developer types a character
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4. Automatic Code Completion
Fig: List of suggestions
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5. Automatic Code Completion
Fig: IDE completes the word
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6. Automatic Code Completion

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Automatic coding support from IDE
First in IDE by Microsoft in 1996
Intellisense
Very common in modern IDEs
Also in search engines, email, database query
tools, Unix terminals
Features: control structure, loop structure,
event handling support
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7. API Usage Pattern
Fig: SWT API usage pattern, Nguyen et al. ICSE’12
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8. API Usage Pattern
Fig: Code under editing
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9. Motivating Example
Fig: Graph representation of API usage pattern, Nguyen et al, ICSE’12
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10. Motivating Example (Semantic Model)
Fig: Semantic representation of API usage pattern,
Wursch et al., ICSE’10
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11. Thesis Statement

An approach that considers semantic
similarity such as conceptual or functional
similarity and code context between API usage
patterns and current code under editing, can
improve the automatic code completion in
terms of accuracy.
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12. Experimental Design
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13. Experimental Design

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API Usage Pattern Extraction, Nguyen et al, ICSE’12
FAMIX Model, Wursch et al., ICSE’10
Semantic Model, Wursch et al., ICSE’10
Semantic Query
Binary Feature Matrix, Bruch et al, FSE’09
K Nearest Neighbors, Bruch et al, FSE’09
Ranked Patterns
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14. Experimental Design
Existing Code base
Semantic Query
Methods
API Usage Patterns
Query Engine
FAMIX
Semantic Model
Binary Feature Matrix
Code Query
KNN Patterns
Fig: Overall System architecture
Ranked Suggestions
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15. Evaluation Plan
Performance Evaluation
 Precision, Recall, F-Score, Bruch et al, FSE’09
 Comparing with existing methods
 Usability Testing
 Integration into IDE

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16. Evaluation Metrics
recommenda tion made  recommenda tion relevant
precision
recommenda tion made
recall
recommenda tion relevant  recommenda tion made
recommenda tion relevant
f
score
2
precision
precision
recall
recall
Precision, Recall, F-Score, Bruch et al. FSE’09
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17. Expected Results &
Interpretation
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18. Semantic Features in Pattern Matching
Fig: Semantic representation vs. Graph representation, Nguyen et
al, ICSE’12
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19. Context Capture from Semantic Model
Fig: Detailed context in Semantic model, SPARQL [12]
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20. Summary
API usage pattern based code
completion
 Semantic Model better for OOP
relationship representation
 Semantic Similarity between API usage
pattern and editing code

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21. Thank You !!!
Please ask your questions
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22. Bibliography
[1].
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23. Bibliography
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Nguyen, A.T., Nguyen, T.T., Nguyen, H.A., Tamrawi, A., Nguyen, H.V., AlKofahi J. and Nguyen, T.N. Graph-based pattern-oriented, context-sensitive
source code completion. Software Engineering (ICSE), 2012 34th International
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Omar, C, Yoon, Y.S., LaToza, T.D., Myers, B.A.. Active code completion.
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Semantic web development tools, URL: http://www.w3.org/2001/sw/wiki/Tools
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24. Bibliography
[11]
[12]
[13]
Intellisense, URL: http://en.wikipedia.org/wiki/IntelliSense
SPARQL Query Tools, URL: http://www.w3.org/2001/sw/wiki/SPARQL
Semantic Model vs. Conceptual Model, URL:
http://en.wikipedia.org/wiki/Semantic_model#Data_models
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