RePOR: Mimicking humans on refactoring tasks. Are we there yet?

TL;DR

This study evaluates whether automated refactoring tools can produce code that is as understandable as human refactoring, through empirical tests involving developer surveys and comprehension tasks.

Contribution

It provides empirical evidence on the effectiveness of RePOR, an automated refactoring approach, in producing code comparable to human refactoring in terms of understandability.

Findings

Automated refactoring can produce code comparable to human refactoring in understandability.

Developers often cannot distinguish between human and machine-generated refactoring.

Current technology limitations still exist but show promising results.

Abstract

Refactoring is a maintenance activity that aims to improve design quality while preserving the behavior of a system. Several (semi)automated approaches have been proposed to support developers in this maintenance activity, based on the correction of anti-patterns, which are `poor' solutions to recurring design problems. However, little quantitative evidence exists about the impact of automatically refactored code on program comprehension, and in which context automated refactoring can be as effective as manual refactoring. Leveraging RePOR, an automated refactoring approach based on partial order reduction techniques, we performed an empirical study to investigate whether automated refactoring code structure affects the understandability of systems during comprehension tasks. (1) We surveyed 80 developers, asking them to identify from a set of 20 refactoring changes if they were generated by developers or by a tool, and to rate the refactoring changes according to their design quality; (2) we asked 30 developers to complete code comprehension tasks on 10 systems that were refactored by either a freelancer or an automated refactoring tool. To make comparison fair, for a subset of refactoring actions that introduce new code entities, only synthetic identifiers were presented to practitioners. We measured developers' performance using the NASA task load index for their effort, the time that they spent performing the tasks, and their percentages of correct answers. Our findings, despite current technology limitations, show that it is reasonable to expect a refactoring tools to match developer code.