The quality of method names is critical for the readability and maintainability of source code. However, it is often challenging to construct concise method names. To alleviate this problem, a number of approaches have been proposed to automatically recommend high-quality names for methods.
Despite being effective, existing approaches meet their bottlenecks mainly in two aspects: (1) the leveraged information is restricted to the target method itself; and (2) lack of distinctions towards the contributions of tokens extracted from different program contexts.
Through a large-scale empirical analysis on +12M methods from +14K real-world projects, we found that (1) the tokens composing a method's name can be frequently observed in its callers/callees; and (2) tokens extracted from different specific contexts have diverse probabilities to compose the target method's name.
Motivated by our findings, we propose, in this paper, a context-guided method name recommender, which mainly embodies two key ideas:
(1) apart from the {\em local context}, which is extracted from the target method itself, we also consider the {\em global context}, which is extracted from other methods in the project that have call relations with the target method, to include more useful information; and (2) we utilize our empirical results as the {\em prior knowledge} to guide the generation of method names and also to restrict the number of tokens extracted from the global contexts.
We implemented the idea as {\bf Cognac} and performed extensive experiments to assess its effectiveness. Results reveal that \toolname can (1) perform better than existing approaches on the \textit{method name recommendation} task (e.g., it achieves an F-score of 63.2%, 60.8%, 66.3%, and 68.5%, respectively, on four widely-used datasets, which all outperform existing techniques);
and (2) achieve higher performance than existing techniques on the \textit{method name consistency checking} task (e.g., its overall $accuracy$ reaches 76.6%, outperforming the state-of-the-art MNire by 11.2%).
Further results reveal that the caller/callee information and the prior knowledge all contribute significantly to the overall performance of {\bf Cognac}.