On-the-fly Improving Performance of Deep Code Models via Input Denoising

• URL: http://arxiv.org/abs/2308.09969v1
• Date: Sat, 19 Aug 2023 09:55:19 GMT
• Title: On-the-fly Improving Performance of Deep Code Models via Input Denoising
• Abstract summary: We propose the first input denoising technique (i.e., CodeDenoise) for deep code models. CodeDenoise localizes noisy identifiers in (likely) mispredicted inputs. It does not need to retrain or reconstruct the model, but only needs to cleanse inputs on-the-fly to improve performance.
• Score: 15.166827643436346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has been widely adopted to tackle various code-based tasks by building deep code models based on a large amount of code snippets. While these deep code models have achieved great success, even state-of-the-art models suffer from noise present in inputs leading to erroneous predictions. While it is possible to enhance models through retraining/fine-tuning, this is not a once-and-for-all approach and incurs significant overhead. In particular, these techniques cannot on-the-fly improve performance of (deployed) models. There are currently some techniques for input denoising in other domains (such as image processing), but since code input is discrete and must strictly abide by complex syntactic and semantic constraints, input denoising techniques in other fields are almost not applicable. In this work, we propose the first input denoising technique (i.e., CodeDenoise) for deep code models. Its key idea is to localize noisy identifiers in (likely) mispredicted inputs, and denoise such inputs by cleansing the located identifiers. It does not need to retrain or reconstruct the model, but only needs to cleanse inputs on-the-fly to improve performance. Our experiments on 18 deep code models (i.e., three pre-trained models with six code-based datasets) demonstrate the effectiveness and efficiency of CodeDenoise. For example, on average, CodeDenoise successfully denoises 21.91% of mispredicted inputs and improves the original models by 2.04% in terms of the model accuracy across all the subjects in an average of 0.48 second spent on each input, substantially outperforming the widely-used fine-tuning strategy.

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