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Misleading method names in software projects can confuse developers, whichmay lead to software defects and affect code understandability. In this paper,we present DeepName, a context-based, deep learning approach to detect methodname inconsistencies and suggest a proper name for a method. The key departurepoint is the philosophy of "Show Me Your Friends, I'll Tell You Who You Are".Unlike the state-of-the-art approaches, in addition to the method's body, wealso consider the interactions of the current method under study with the otherones including the caller and callee methods, and the sibling methods in thesame enclosing class. The sequences of sub-tokens in the program entities'names in the contexts are extracted and used as the input for an RNN-basedencoder-decoder to produce the representations for the current method. Wemodify that RNN model to integrate the copy mechanism and our newly developedcomponent, called the non-copy mechanism, to emphasize on the possibility of acertain sub-token not to be copied to follow the current sub-token in thecurrently generated method name.  We conducted several experiments to evaluate DeepName on large datasets with+14M methods. For consistency checking, DeepName improves the state-of-the-artapproach by 2.1%, 19.6%, and 11.9% relatively in recall, precision, andF-score, respectively. For name suggestion, DeepName improves relatively overthe state-of-the-art approaches in precision (1.8%--30.5%), recall(8.8%--46.1%), and F-score (5.2%--38.2%). To assess DeepName's usefulness, wedetected inconsistent methods and suggested new method names in activeprojects. Among 50 pull requests, 12 were merged into the main branch. Intotal, in 30/50 cases, the team members agree that our suggested method namesare more meaningful than the current names.

A Context-based Automated Approach for Method Name Consistency Checking and Suggestion