SUT: Active Defects Probing for Transcompiler Models

• URL: http://arxiv.org/abs/2310.14209v1
• Date: Sun, 22 Oct 2023 07:16:02 GMT
• Title: SUT: Active Defects Probing for Transcompiler Models
• Authors: Mengnan Qi, Yufan Huang, Maoquan Wang, Yongqiang Yao, Zihan Liu, Bin Gu, Colin Clement, Neel Sundaresan
• Abstract summary: We introduce a new metrics for programming language translation and these metrics address basic syntax errors. Experiments have shown that even powerful models like ChatGPT still make mistakes on these basic unit tests.
• Score: 24.01532199512389
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic Program translation has enormous application value and hence has been attracting significant interest from AI researchers. However, we observe that current program translation models still make elementary syntax errors, particularly, when the target language does not have syntax elements in the source language. Metrics like BLUE, CodeBLUE and computation accuracy may not expose these issues. In this paper we introduce a new metrics for programming language translation and these metrics address these basic syntax errors. We develop a novel active defects probing suite called Syntactic Unit Tests (SUT) which includes a highly interpretable evaluation harness for accuracy and test scoring. Experiments have shown that even powerful models like ChatGPT still make mistakes on these basic unit tests. Specifically, compared to previous program translation task evaluation dataset, its pass rate on our unit tests has decreased by 26.15%. Further our evaluation harness reveal syntactic element errors in which these models exhibit deficiencies.

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