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Recent works have concluded that software code is more repetitive and predictable, i.e. more natural, than English texts. On re-examination, we find that much of the apparent "naturalness" of source code is due to the presence of language specific syntax, especially separators, such as semi-colons and brackets. For example, separators account for 44% of all tokens in our Java corpus. When we follow the NLP practices of eliminating punctuation (e.g., separators) and stopwords (e.g., keywords), we find that code is still repetitive and predictable, but to a lesser degree than previously thought. We suggest that SyntaxTokens be filtered to reduce noise in code recommenders. Unlike the code written for a particular project, API code usage is similar across projects: a file is opened and closed in the same manner regardless of domain. When we restrict our n-grams to those contained in the Java API, we find that API usages are highly repetitive. Since API calls are common across programs, researchers have made reliable statistical models to recommend sophisticated API call sequences. Sequential n-gram models were developed for natural languages. Code is usually represented by an AST which contains control and data flow, making n-grams models a poor representation of code. Comparing n-grams to statistical graph representations of the same codebase, we find that graphs are more repetitive and contain higherlevel patterns than n-grams. We suggest that future work focus on statistical code graphs models that accurately capture complex coding patterns. Our replication package makes our scripts and data available to future researchers [1]. [ABSTRACT FROM AUTHOR]

Natural languages like English are rich, complex, and powerful. The highly creative and graceful use of languages like English and Tamil, by masters like Shakespeare and Avvaiyar, can certainly delight and inspire. But in practice, given cognitive constraints and the exigencies of daily life, most human utterances are far simpler and much more repetitive and predictable. In fact, these utterances can be very usefully modeled using modern statistical methods. This fact has led to the phenomenal success of statistical approaches to speech recognition, natural language translation, question-answering, and text mining and comprehension. We begin with the conjecture that most software is also natural, in the sense that it is created by humans at work, with all the attendant constraints and limitations-and thus, like natural language, it is also likely to be repetitive and predictable. We then proceed to ask whether a) code can be usefully modeled by statistical language models and b) such models can be leveraged to support software engineers. Using the widely adopted n-gram model, we provide empirical evidence supportive of a positive answer to both these questions. We show that code is also very repetitive, and in fact even more so than natural languages. As an example use of the model, we have developed a simple code completion engine for Java that, despite its simplicity, already improves Eclipse's built-in completion capability. We conclude the paper by laying out a vision for future research in this area. [ABSTRACT FROM AUTHOR]