Neuro-symbolic Zero-Shot Code Cloning with Cross-Language Intermediate Representation

• URL: http://arxiv.org/abs/2304.13350v1
• Date: Wed, 26 Apr 2023 07:41:26 GMT
• Title: Neuro-symbolic Zero-Shot Code Cloning with Cross-Language Intermediate Representation
• Authors: Krishnam Hasija, Shrishti Pradhan, Manasi Patwardhan, Raveendra Kumar Medicherla, Lovekesh Vig, Ravindra Naik
• Abstract summary: We define a neuro-symbolic approach to address the task of finding semantically similar clones for the codes of the legacy programming language, without training data. We fine-tune UnixCoder, the best-performing model for cross-programming language search, for the Code Cloning task with the SBT IRs of C code-pairs, available in the CodeNet dataset. 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 test split synthesized from the CodeNet
• Score: 13.881954273779403
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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