Understanding Code Change with Micro-Changes

TL;DR

This paper introduces a micro-change-based approach to interpret code diffs, translating them into natural language descriptions to improve understanding of software changes, especially in conditional logic, through an automated detector.

Contribution

The paper presents a novel catalog of micro-changes and an automated detector to better interpret code diffs in natural language, aiding software comprehension.

Findings

The micro-change detector explains over 67% of code changes in studied repositories.

The approach simplifies understanding code diffs by translating them into natural language.

Focus on changes affecting conditional logic demonstrates effectiveness.

Abstract

A crucial activity in software maintenance and evolution is the comprehension of the changes performed by developers, when they submit a pull request and/or perform a commit on the repository. Typically, code changes are represented in the form of code diffs, textual representations highlighting the differences between two file versions, depicting the added, removed, and changed lines. This simplistic representation must be interpreted by developers, and mentally lifted to a higher abstraction level, that more closely resembles natural language descriptions, and eases the creation of a mental model of the changes. However, the textual diff-based representation is cumbersome, and the lifting requires considerable domain knowledge and programming skills. We present an approach, based on the concept of micro-change, to overcome these difficulties, translating code diffs into a series of pre-defined change operations, which can be described in natural language. We present a catalog of micro-changes, together with an automated micro-change detector. To evaluate our approach, we performed an empirical study on a large set of open-source repositories, focusing on a subset of our micro-change catalog, namely those related to changes affecting the conditional logic. We found that our detector is capable of explaining more than 67% of the changes taking place in the systems under study.