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(Source) Code summarization aims to automatically generate summaries/commentsfor a given code snippet in the form of natural language. Such summaries play akey role in helping developers understand and maintain source code. Existingcode summarization techniques can be categorized into extractive methods andabstractive methods. The extractive methods extract a subset of importantstatements and keywords from the code snippet using retrieval techniques, andgenerate a summary that preserves factual details in important statements andkeywords. However, such a subset may miss identifier or entity naming, andconsequently, the naturalness of generated summary is usually poor. Theabstractive methods can generate human-written-like summaries leveragingencoder-decoder models from the neural machine translation domain. Thegenerated summaries however often miss important factual details.  To generate human-written-like summaries with preserved factual details, wepropose a novel extractive-and-abstractive framework. The extractive module inthe framework performs a task of extractive code summarization, which takes inthe code snippet and predicts important statements containing key factualdetails. The abstractive module in the framework performs a task of abstractivecode summarization, which takes in the entire code snippet and importantstatements in parallel and generates a succinct and human-written-like naturallanguage summary. We evaluate the effectiveness of our technique, called EACS,by conducting extensive experiments on three datasets involving six programminglanguages. Experimental results show that EACS significantly outperformsstate-of-the-art techniques in terms of all three widely used metrics,including BLEU, METEOR, and ROUGH-L.

An Extractive-and-Abstractive Framework for Source Code Summarization