In this paper, we define a neuro-symbolic approach to address the task of
finding semantically similar clones for the codes of the legacy programming
language COBOL, without training data. We define a meta-model that is
instantiated to have an Intermediate Representation (IR) in the form of
Abstract Syntax Trees (ASTs) common across codes in C and COBOL. We linearize
the IRs using Structure Based Traversal (SBT) to create sequential inputs. We
further fine-tune UnixCoder, the best-performing model for zero-shot
cross-programming language code search, for the Code Cloning task with the SBT
IRs of C code-pairs, available in the CodeNet dataset. This allows us to learn
latent representations for the IRs of the C codes, which are transferable to
the IRs of the COBOL codes. With this fine-tuned UnixCoder, we get a
performance improvement of 12.85 MAP@2 over the pre-trained UniXCoder model, in
a zero-shot setting, on the COBOL test split synthesized from the CodeNet
dataset. This demonstrates the efficacy of our meta-model based approach to
facilitate cross-programming language transfer.

本文提出了神经符号方法，通过中间表示（IR）和 UnixCoder 优化，实现了 COBOL 代码克隆任务，取得了 12.85 MAP@2 的表现提升。