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This paper presents an ensemble part-of-speech tagging approach for source code identifiers. Ensemble tagging is a technique that uses machine-learning and the output from multiple part-of-speech taggers to annotate natural language text at a higher quality than the part-of-speech taggers are able to obtain independently. Our ensemble uses three state-of-the-art part-of-speech taggers: SWUM, POSSE, and Stanford. We study the quality of the ensemble's annotations on five different types of identifier names: function, class, attribute, parameter, and declaration statement at the level of both individual words and full identifier names. We also study and discuss the weaknesses of our tagger to promote the future amelioration of these problems through further research. Our results show that the ensemble achieves 75\% accuracy at the identifier level and 84-86\% accuracy at the word level. This is an increase of +17\% points at the identifier level from the closest independent part-of-speech tagger., Comment: 18 pages. arXiv admin note: text overlap with arXiv:2007.08033

This paper describes the results of a large (+1100 responses) survey of professional software developers concerning standards for naming source code methods. The various standards for source code method names are derived from and supported in the software engineering literature. The goal of the survey is to determine if there is a general consensus among developers that the standards are accepted and used in practice. Additionally, the paper examines factors such as years of experience and programming language knowledge in the context of survey responses. The survey results show that participants very much agree about the importance of various standards and how they apply to names. Additionally, the survey shows that years of experience and the programming language the participants use has almost no effect on their responses.

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., Comment: 69 pages, 3 figures, 16 tables