Automatically producing behavioral exception (BE) API documentation helps developers correctly use the libraries. The state-of-the-art approaches are either rule-based, which is too restrictive in its applicability, or deep learning (DL)-based, which requires large training dataset. To address those issues, we propose StatGen, a novel hybrid approach between statistical machine translation (SMT) and tree-structured translation to generate BE documentation for any code and vice versa. We consider an API method to possess two levels of abstraction: the source code for the API method, and its documentation. StatGen is specifically designed for this two-way inference, taking advantages of the structures of source code and documentation to achieve higher accuracy. For practical use, if the code does not have BE documentation, StatGen can help users in writing it, and if it exists, one can use StatGen to verify the consistency between BE documentation and implementations. Moreover, it can generate BE code from existing BE documentation.

We conducted empirical experiments to intrinsically evaluate StatGen. We show that it achieves high precision (82% and 79%), and recall (86% and 90%), in inferring BE documentation from source code and vice versa. Our results show that StatGen achieves high accuracy in precision, recall, and BLEU score, and outperforms the state-of-the-art baselines in SMT, Neural Machine Translation, tree-based transformer, and dual-task learner. We showed StatGen’s usefulness in two applications. First, we used StatGen to generate the BE documentation for Apache APIs that lack of documentation by learning from the documentation of the equivalent APIs in JDK. 46% of the generated documentation were rated as useful and 41% as somewhat useful. In the second application, we used StatGen to detect the inconsistency between BE documentation and corresponding implementations of several packages in JDK8.