Method name recommendation based on source code metrics

Abstract

Method naming is a critical factor in program comprehension, affecting software quality. State-of-the-art naming techniques use deep learning to compute the methods’ similarity considering their textual contents. They highly depend on identifiers’ names and do not compute semantical interrelations among methods’ instructions. Source code metrics compute such semantical interrelations. This article proposes using source code metrics to measure semantical and structural cross-project similarities. The metrics constitute features of a KNN model, determining the k-most similar methods to a given method. Experiments with 4000000 Java methods on the proposed model demonstrate improvements in precision and recall of state-of-the-arts with 4.25 and 12.08%.