[Paper Review] On the Detection of High-Impact Refactoring Opportunities in Programs

Jens Dietrich, Catherine McCartin+2 more|arXiv (Cornell University)|Jun 9, 2010

Software Engineering Research31 references3 citations

TL;DR

This paper proposes a method to detect high-impact refactoring opportunities by analyzing reference participation in design flaw patterns within Java programs. By identifying a small set of critical references whose removal eliminates most instances of modularization flaws, the approach efficiently targets high-leverage code improvements in 95 open-source projects.

ABSTRACT

We present a novel approach to detect refactoring opportunities by measuring the participation of references between types in instances of patterns representing design flaws. This technique is validated using an experiment where we analyse a set of 95 open-source Java programs for instances of four patterns representing modularisation problems. It turns out that our algorithm can detect high impact refactorings opportunities - a small number of references such that the removal of those references removes the majority of patterns from the program.

Motivation & Objective

• To identify refactoring opportunities that resolve a large portion of modularization-related design flaws with minimal changes.
• To address the challenge of prioritizing refactoring efforts in large codebases by focusing on high-impact changes.
• To develop a technique that quantitatively measures the contribution of individual references to the presence of design pattern instances.
• To validate the approach on real-world open-source Java systems to assess its practical effectiveness.

Proposed method

• The method measures the participation of references between types in instances of four specific design flaw patterns related to modularization.
• It identifies references whose removal would eliminate the largest number of pattern instances across the codebase.
• The approach uses static analysis to detect pattern occurrences and compute reference participation scores.
• A reference is considered high-impact if its removal results in the removal of multiple pattern instances.
• The algorithm ranks references based on their contribution to pattern prevalence, prioritizing those with the highest impact.
• The method is evaluated on 95 open-source Java programs to assess scalability and precision.

Experimental results

Research questions

• RQ1 Which references, when removed, lead to the highest reduction in the number of detected design flaw instances?
• RQ2 How many references are required to eliminate the majority of modularization pattern instances in real-world Java programs?
• RQ3 Can a small set of references account for a significant portion of design flaw occurrences across diverse codebases?
• RQ4 How effective is the proposed method in identifying high-impact refactoring opportunities compared to heuristic or random selection?

Key findings

• A small number of references, when removed, eliminate the majority of detected modularization flaw instances across the 95 analyzed programs.
• The approach successfully identifies high-impact refactoring opportunities by focusing on references with high participation in pattern instances.
• The method demonstrates practical relevance by pinpointing a minimal set of changes that resolve widespread design issues.
• The validation on real-world systems confirms the method's scalability and precision in identifying leverage points for refactoring.