Paper Info
Title
Mining fine-grained code changes to detect unknown change patterns
Abstract
Identifying repetitive code changes benefits developers, tool builders, and researchers. Tool builders can automate the popular code changes, thus improving the productivity of developers. Researchers can better understand the practice of code evolution, advancing existing code assistance tools and benefiting developers even further. Unfortunately, existing research either predominantly uses coarse-grained Version Control System (VCS) snapshots as the primary source of code evolution data or considers only a small subset of program transformations of a single kind - refactorings. We present the first approach that identifies previously unknown frequent code change patterns from a fine-grained sequence of code changes. Our novel algorithm effectively handles challenges that distinguish continuous code change pattern mining from the existing data mining techniques. We evaluated our algorithm on 1,520 hours of code development collected from 23 developers, and showed that it is effective, useful, and scales to large amounts of data. We analyzed some of the mined code change patterns and discovered ten popular kinds of high-level program transformations. More than half of our 420 survey participants acknowledged that eight out of ten transformations are relevant to their programming activities.
Year
DOI
Venue
2014
10.1145/2568225.2568317
ICSE
Keywords
Field
DocType
code changes,data mining,distribution, maintenance, and enhancement,program transformation
Data science,Static program analysis,Program transformation,Software engineering,Source code,Computer science,Real-time computing,KPI-driven code analysis,Snapshot (computer storage),Code refactoring,Change patterns,Code review
Conference
Citations 
PageRank 
References 
35
0.85
32
Authors
4
Name
Order
Citations
PageRank
Stas Negara12189.23
Mihai Codoban21133.92
Danny Dig3157079.66
Ralph E. Johnson41790264.74