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Area of Science:

  • Software Engineering
  • Computer Science
  • Programming Language Analysis

Background:

  • Software evolution is a complex process involving diverse strategies and recurring coding patterns.
  • Understanding these patterns offers insights into the software development lifecycle.
  • Existing patterns are often project or task-specific, limiting broader applicability.

Purpose of the Study:

  • To identify high-level code change patterns within software evolution.
  • To analyze these patterns to draw conclusions about the software development process.
  • To develop novel methods for calculating file-overarching diffs and parallelizing pattern mining.

Main Methods:

  • Developed a novel approach for calculating high-level file-overarching differences.
  • Implemented a novel parallelization technique for mining code change patterns.
  • Analyzed 45,000 patterns mined from a corpus of 1000 Java projects.

Main Results:

  • Identified 7 categories of code change patterns, presenting 13 representative patterns.
  • Found a large number of high-level change patterns that occur frequently.
  • Observed that most patterns are specific to particular projects and contributors.

Conclusions:

  • A wide variety of code change patterns exist and are frequently utilized in software development.
  • Few patterns are universally common; the majority are context-dependent.
  • Contextual factors significantly influence the prevalence and application of code change patterns.