DeepAPI#: CLR/C# call sequence synthesis from text query

Alexander Chebykin, Mikhail Kita, Iakov Kirilenko

Research output: Contribution to journalConference articlepeer-review


Developers often search for an implementation of typical features via libraries (for example, how to create a UI button control, extract data from a JSON-formatted file, etc.). The Internet is the usual source of the information on the topic. However, various statistical tools provide an alternative: after processing large amounts of source code and learning common patterns, they can convert a user request to a set of relevant function calls. We examine one of those tools - DeepAPI. This fresh deep learning based algorithm outperforms all others (according to its authors). We attempt to reproduce this result using different target programming language - C# - instead of Java used in the original DeepAPI. In this paper we report arising problems in the data gathering for training, difficulties in the model construction and training, and finally discuss possible modifications of the algorithm.

Original languageEnglish
JournalCEUR Workshop Proceedings
StatePublished - 1 Jan 2017
Event2nd Conference on Software Engineering and Information Management, SEIM 2017 - Saint Petersburg, Russian Federation
Duration: 21 Apr 2017 → …

Scopus subject areas

  • Computer Science(all)


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