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Recommending Variable Names for Extract Local Variable Refactorings
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PRE
AI
DOI：10.1145/3712191.png)
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Abstract
En 中文
Extract local variable
is one of the most popular refactorings. It is frequently employed to replace occurrences of a complex expression with simple accesses to a newly introduced variable that is initialized by the original complex expression. Consequently, most IDEs and refactoring tools provide automated support for this refactoring, e.g., to suggest names for the newly extracted variables. However, we find approximately 70% of the names recommended by these IDEs are different from what developers manually constructed, adding additional renaming burdens to developers and providing limited assistance. In this paper, we introduce
VarNamer
, an automated approach designed to recommend variable names for
extract local variable
refactorings. Through a large-scale empirical study, we identify key contexts, such as variable initializations and homogeneous variables (variables whose initializations are identical to that of the newly extracted variable), that are useful for composing variable names. Leveraging these insights, we developed a set of heuristic rules through program static analysis techniques, e.g., lexical analysis, syntax analysis, control flow analysis, and data flow analysis, and employ data mining techniques, i.e., FP-growth algorithm, to recommend variable names effectively. Notably, some of our heuristic rules have been successfully integrated into
Eclipse
, where they are now distributed with the latest releases of the IDE. Evaluation of
VarNamer
on a dataset of 27,158 real-world
extract local variable
refactorings in Java applications demonstrates its superiority over state-of-the-art IDEs. Specifically,
VarNamer
significantly increases the chance of exact match by 52.6% compared to
Eclipse
and 40.7% compared to
IntelliJ IDEA
. We also evaluated the proposed approach with real-world extract local variable refactorings conducted in C++ projects, and the results suggest that the approach can achieve comparable performance on programming languages besides Java. It may suggest the generalizability of
VarNamer
. Finally, we designed and conducted a user study to investigate the impact of
VarNamer
on developers’ productivity. The results of the user study suggest that our approach can speed up the refactoring by 27.8% and reduce 49.3% edits on the recommended variable names.
Keywords:
extract local variable
variable name recommendation
static analysis
data mining
refactoring
Journal
A
IF:
6.2
Papers:
1.2K
Citations:
3.4K
Organization
No organization information available
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