We propose a model to automatically describe changes introduced in the source code of a program using natural language. Our method receives as input a set of code commits, which contains both the modifications and message introduced by an user. These two modalities are used to train an encoder-decoder architecture. We evaluated our approach on twelve real world open source projects from four different programming languages. Quantitative and qualitative results showed that the proposed approach can generate feasible and semantically sound descriptions not only in standard in-project settings, but also in a cross-project setting.
Source code modifications are often documented with log messages. Such messages are a key component of software maintenance: they can help developers validate changes, locate and triage defects, and understand modifications. However, this documentation can be burdensome to create and can be incomplete or inaccurate. We present an automatic technique for synthesizing succinct human-readable documentation for arbitrary program differences. Our algorithm is based on a combination of symbolic execution and a novel approach to code summarization. The documentation it produces describes the effect of a change on the runtime behavior of a program, including the conditions under which program behavior changes and what the new behavior is. We compare our documentation to 250 human-written log messages from 5 popular open source projects. Employing a human study, we find that our generated documentation is suitable for supplementing or replacing 89 of existing log messages that directly describe a code change.