Abstract
Recently, source code mining has received increasing attention due to the rapid increase of open-sourced code repositories and the tremendous values implied in this large dataset, which can help us understand the organization of functions or classes in different software and analyze the impact of these organized patterns on the software behaviors. Hence, learning an effective representation model for the functions of source code, from a modern view, is a crucial problem. Considering the inherent hierarchy of functions, we propose a novel hyperbolic function embedding (HFE) method, which can learn a distributed and hierarchical representation for each function via the Poincaré ball model. To achieve this, a function call graph (FCG) is first constructed to model the call relationship among functions. To verify the underlying geometry of FCG, the Ricci curvature model is used. Finally, an HFE model is built to learn the representations that can capture the latent hierarchy of functions in the hyperbolic space, instead of the Euclidean space, which are usually used in those state-of-the-art methods. Moreover, HFE is more compact in terms of lower dimensionality than the existing graph embedding methods. Thus, HFE is more effective in terms of computation and storage. To experimentally evaluate the performance of HFE, two application scenarios, namely, function classification and link prediction, have been applied. HFE achieves up to 7.6% performance improvement compared to the chosen state-of-the-art methods, namely, Node2vec and Struc2vec.
Highlights
There are billions of lines of source code (e.g., GitHub) open to the software community on the Internet
We propose a novel hyperbolic function embedding method, which can learn a distributed and hierarchical representation for each function via the Poincaré ball model
We use the Ricci curvature to describe the intrinsic geometry of the function call graph (FCG)
Summary
There are billions of lines of source code (e.g., GitHub) open to the software community on the Internet. Among them, inspired by word2vec [5], various embedding techniques that learn function representations have received a great deal of attention because the learned features of functions by embedding into vector space can compactly encode the latent semantic structure Those embedding vectors can achieve better performance as pre-trained inputs to machine learning models. We use Ricci curvature [15] to estimate the geometric structure of the FCG and identify that the curvature for most of the edges in the FCG are negative This phenomenon suggests hyperbolic space instead of Euclidean space as a natural embedding space for FCG since hyperbolic space is usually associated with constant negative curvature [16].
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