On the Generation, Structure, and Semantics of Grammar Patterns in Source Code Identifiers

TL;DR

This paper analyzes naming patterns in source code identifiers using part-of-speech sequences, examining their structure, semantics, and how well current models can automatically identify these patterns to aid code comprehension.

Contribution

It establishes common naming patterns across identifier types, analyzes their impact on understanding code, and evaluates the accuracy of state-of-the-art POS tagging techniques for modeling identifiers.

Findings

Identified common naming patterns in class and attribute identifiers

Analyzed how patterns influence code comprehension

Evaluated POS tagging accuracy and its limitations

Abstract

Identifiers make up a majority of the text in code. They are one of the most basic mediums through which developers describe the code they create and understand the code that others create. Therefore, understanding the patterns latent in identifier naming practices and how accurately we are able to automatically model these patterns is vital if researchers are to support developers and automated analysis approaches in comprehending and creating identifiers correctly and optimally. This paper investigates identifiers by studying sequences of part-of-speech annotations, referred to as grammar patterns. This work advances our understanding of these patterns and our ability to model them by 1) establishing common naming patterns in different types of identifiers, such as class and attribute names; 2) analyzing how different patterns influence comprehension; and 3) studying the accuracy of state-of-the-art techniques for part-of-speech annotations, which are vital in automatically modeling identifier naming patterns, in order to establish their limits and paths toward improvement. To do this, we manually annotate a dataset of 1,335 identifiers from 20 open-source systems and use this dataset to study naming patterns, semantics, and tagger accuracy.