Software language identification techniques are applicable to many situations from universal IDE support to legacy code analysis. Most widely used heuristics are based on software artefact metadata such as file extensions or on grammar-based text analysis such as keyword search. In this paper we propose to use statistical language models from the natural language processing field such as n-grams, skip-grams, multinominal naive Bayes and normalised compression distance. Our preliminary experiments show that some of these models used as classifiers can achieve high precision and recall and can be used to properly identify language families, languages and even deal with embedded code fragments.
['Juriaan Kennedy van Dam', 'Vadim Zaytsev']
Software Language Identification with Natural Language Classifiers