More Accurate Recommendations for Method-Level Changes

TL;DR

ARES is a recommendation system for method-level code changes that significantly improves accuracy by effectively handling code movements, achieving 96% correctness in suggestions compared to manual developer changes.

Contribution

The paper introduces ARES, a novel tool that enhances recommendation accuracy for code changes by accounting for code movements, surpassing existing tools in correctness.

Findings

ARES achieves 96% recommendation accuracy.

ARES maintains comparable precision and recall to existing tools.

Handling code movements improves recommendation quality.

Abstract

During the life span of large software projects, developers often apply the same code changes to different code locations in slight variations. Since the application of these changes to all locations is time-consuming and error-prone, tools exist that learn change patterns from input examples, search for possible pattern applications, and generate corresponding recommendations. In many cases, the generated recommendations are syntactically or semantically wrong due to code movements in the input examples. Thus, they are of low accuracy and developers cannot directly copy them into their projects without adjustments. We present the Accurate REcommendation System (ARES) that achieves a higher accuracy than other tools because its algorithms take care of code movements when creating patterns and recommendations. On average, the recommendations by ARES have an accuracy of 96% with respect to code changes that developers have manually performed in commits of source code archives. At the same time ARES achieves precision and recall values that are on par with other tools.