Recommending Extract Method Refactoring Based on Confidence of Predicted Method Name
This addresses the need for more accurate refactoring recommendations for software developers, though it is incremental as it builds on prior metrics with an added semantic component.
The paper tackled the problem of accurately recommending Extract Method refactoring in software development by incorporating semantic coherency based on predicted method name confidence, and the result showed higher correctness compared to existing techniques.
Refactoring is an important activity that is frequently performed in software development, and among them, Extract Method is known to be one of the most frequently performed refactorings. The existing techniques for recommending Extract Method refactoring calculate metrics from the source method and the code fragments to be extracted to order the recommendation candidates. This paper proposes a new technique for accurately recommending Extract Method refactoring by considering whether code fragments are semantically coherent chunks that can be given clear method names, in addition to the metrics used in previous studies. As a criterion for the semantic coherency, the proposed technique employs the probability (i.e. confidence) of the predicted method names for the code fragments output by code2seq, which is a state-of-the-art method name prediction technique. The evaluation experiment confirmed that the proposed technique has higher correctness of recommendation than the existing techniques.