The software development process produces vast amounts of textual data expressed in natural language. Outcomes from the natural language processing community have been adapted in software engineering research for leveraging this rich textual information; these include methods and readily available tools, often furnished with pre-trained models. State of the art pre-trained models however, capture general, common sense knowledge, with limited value when it comes to handling data specific to a specialized domain. There is currently a lack of domain-specific pre-trained models that would further enhance the processing of natural language artefacts related to software engineering. To this end, we release a word2vec model trained over 15GB of textual data from Stack Overflow posts. We illustrate how the model disambiguates polysemous words by interpreting them within their software engineering context. In addition, we present examples of fine-grained semantics captured by the model, that imply transferability of these results to diverse, targeted information retrieval tasks in software engineering and motivate for further reuse of the model.

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The emergence of online open source repositories in the recent years has led to an explosion in the volume of openly available source code, coupled with metadata that relate to a variety of software development activities. As an effect, in line with recent advances in machine learning research, software maintenance activities are switching from symbolic formal methods to data-driven methods. In this context, the rich semantics hidden in source code identifiers provide opportunities for building semantic representations of code which can assist tasks of code search and reuse. To this end, we deliver in the form of pretrained vector space models, distributed code representations for six popular programming languages, namely, Java, Python, PHP, C, C++, and C#. The models are produced using fastText, a state-of-the-art library for learning word representations. Each model is trained on data from a single programming language; the code mined for producing all models amounts to over 13.000 repositories. We indicate dissimilarities between natural language and source code, as well as variations in coding conventions in between the different programming languages we processed. We describe how these heterogeneities guided the data preprocessing decisions we took and the selection of the training parameters in the released models. Finally, we propose potential applications of the models and discuss limitations of the models.

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