Toward Interactive Optimization of Source Code Differences: An Empirical Study of Its Performance

TL;DR

This paper introduces an interactive method for optimizing source code diffs, allowing user feedback to improve diff quality, demonstrated to significantly reduce nonoptimal diffs with minimal feedback in empirical tests.

Contribution

It presents a novel interactive approach for diff optimization that incorporates user feedback to enhance diff accuracy over existing automatic methods.

Findings

92% of nonoptimal diffs corrected with fewer than four feedback actions

Empirical validation on 23 GitHub projects shows high effectiveness

Interactive feedback significantly improves diff quality

Abstract

A source code difference (diff) indicates changes made by comparing new and old source codes, and it can be utilized in code reviews to help developers understand the changes made to the code. Although many diff generation methods have been proposed, existing automatic methods may generate nonoptimal diffs, hindering reviewers from understanding the changes. In this paper, we propose an interactive approach to optimize diffs. Users can provide feedback for the points of a diff that should not be matched but are or parts that should be matched but are not. The edit graph is updated based on this feedback, enabling users to obtain a more optimal diff. We simulated our proposed method by applying a search algorithm to empirically assess the number of feedback instances required and the amount of diff optimization resulting from the feedback to investigate the potential of this approach. The results of 23 GitHub projects confirm that 92% of nonoptimal diffs can be addressed with less than four feedback actions in the ideal case.