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We propose a model to automatically describe changes introduced in the sourcecode of a program using natural language. Our method receives as input a set ofcode commits, which contains both the modifications and message introduced byan user. These two modalities are used to train an encoder-decoderarchitecture. We evaluated our approach on twelve real world open sourceprojects from four different programming languages. Quantitative andqualitative results showed that the proposed approach can generate feasible andsemantically sound descriptions not only in standard in-project settings, butalso in a cross-project setting.

A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes