RefDiff: Detecting Refactorings in Version Histories (MSR 2017)

Refactoring is a well-known technique that is widely adopted by software engineers to improve the design and enable the evolution of a system. Knowing which refactoring operations were applied in a code change is a valuable information to understand software evolution, adapt software components, merge code changes, and other applications. In this paper, we present RefDiff, an automated approach that identifies refactorings performed between two code revisions in a git repository. RefDiff employs a combination of heuristics based on static analysis and code similarity to detect 13 well-known refactoring types. In an evaluation using an oracle of 448 known refactoring operations, distributed across seven Java projects, our approach achieved precision of 100% and recall of 88%. Moreover, our evaluation suggests that RefDiff has superior precision and recall than existing state-of-the-art approaches.

Transcript

1. RefDiff: Detecting Refactorings in
   Version Histories
   Danilo Silva, Marco Tulio Valente
   Universidade Federal de Minas Gerais
   Belo Horizonte, Brazil
   

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2. Introduction
   • Software components are in constant change
   • One important kind of change is refactoring
   2
   

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3. Introduction
   • Knowledge of the refactoring operations
   applied is a valuable information
   – Analyze software evolution
   – Study refactoring practice
   – Review and merge code
   3
   

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4. Problem: Finding refactoring activity is a non-
   trivial task
   4
   

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5. Problem: Finding refactoring activity is a non-
   trivial task
   Documentation?
   • Refactorings are rarely documented
   5
   

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6. Problem: Finding refactoring activity is a non-
   trivial task
   Instrumenting refactoring engines?
   • Refactorings are not always performed using
   automated support
   6
   

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7. Problem: Finding refactoring activity is a non-
   trivial task
   Source code analysis?
   • Viable, but current approaches have precision
   and recall issues
   – Refactoring Miner: 63% precision
   – Ref-Finder: 35% precision and 24% recall
   7
   

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8. PROPOSED SOLUTION
   8
   

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9. RefDiff
   9
   • A refactoring detection approach
   – Employs a combination of heuristics based on
   static analysis and code similarity
   – 13 well-known refactoring types
   – TF-IDF based similarity index
   

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10. RefDiff: Overview
    10
    Version
    before
    Version
    after
    Input
    

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11. RefDiff: Overview
    11
    Version
    before
    Version
    after
    Source Code
    Analysis
    Types,
    methods,
    and fields
    }
    

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12. RefDiff: Overview
    12
    Version
    before
    Version
    after
    Relationship
    Analysis
    Rename
    Extract
    Move
    

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13. Relationships
    13
    

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14. Relationship Example: Rename Method
    14
    

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15. Relationship Example: Rename Method
    15
    names of mb
    and ma
    should be different
    

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16. Relationship Example: Rename Method
    16
    mb
    and ma
    are in “the same” class
    

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17. Relationship Example: Rename Method
    17
    the similarity index between mb
    and ma
    should be greater than a threshold
    

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18. Computing Similarity
    18
    • Source code represented as a multiset (or bag)
    of tokens
    • Similarity index based on Information
    Retrieval techniques (TF-IDF)
    

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19. Calibration of Thresholds
    19
    • Oracle of known refactorings in 10 commits of
    a public dataset (Silva et al., 2016)
    • Thresholds from 0.1 to 0.9 by 0.1 increments
    • We choose the value that optimize the F1
    score
    

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20. Calibration Results
    20
    

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21. EVALUATION
    21
    

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22. Evaluation: Precision and Recall
    22
    • Oracle of known refactorings applied by
    students
    – 7 open-source systems
    – 448 refactoring relationships
    • Compare RefDiff’s precison and recall with
    – Refactoring Miner
    – Refactoring Crawler
    – Ref-Finder
    

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23. Evaluation: Precision and Recall
    23
    

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24. Conclusion
    24
    • RefDiff has better precision and recall than
    other approaches
    • Execution time is acceptable (1.96s per commit)
    

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25. Future Work
    25
    • Extended evaluation of RefDiff using
    actual refactorings applied in open-source
    systems
    

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26. THANK YOU!
    https://github.com/aserg-ufmg/RefDiff
    

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27. Evaluation: Precision and Recall
    27
    

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28. Evaluation: Execution Time
    28
    

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29. Evaluation: Execution Time
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    • We analyzed each commit between January 1,
    2017 and March 27, of 10 Java repositories
    – 1990 commits
    • We compared execution time with Refactoring
    Miner
    

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30. Evaluation: Execution Time
    30
    Approach Avg. time (s) Total time(s)
    RefDiff 1.96 3,893
    Ref. Miner 0.89 1,779
    

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31. Computing Similarity: Example
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32. Computing Similarity: Example
    32
    

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33. Computing Similarity: Example
    33
    

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34. Computing Similarity
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    token frequency in entity e inverse document frequency of
    the token in the collection
    weighted Jaccard coefficient
    

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