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.

Zhao Tian

Tianjin University

China

Junjie Chen

Tianjin University

China

Xiangyu Zhang

Purdue University

United States

Adversarial examples are important to test and enhance the robustness of deep code models. As source code is discrete and has to strictly stick to complex grammar and semantics constraints, the adversarial example generation techniques in other domains are hardly applicable. Moreover, the adversarial example generation techniques specific to deep code models still suffer from unsatisfactory effectiveness due to the enormous ingredient search space. In this work, we propose a novel adversarial example generation technique (i.e., CODA) for testing deep code models. Its key idea is to use code differences between the target input (i.e., a given code snippet as the model input) and reference inputs (i.e., the inputs that have small code differences but different prediction results with the target input) to guide the generation of adversarial examples. It considers both structure differences and identifier differences to preserve the original semantics. Hence, the ingredient search space can be largely reduced as the one constituted by the two kinds of code differences, and thus the testing process can be improved by designing and guiding corresponding equivalent structure transformations and identifier renaming transformations. Our experiments on 15 deep code models demonstrate the effectiveness and efficiency of CODA, the naturalness of its generated examples, and its capability of enhancing model robustness after adversarial fine-tuning. For example, CODA reveals 88.05% and 72.51% more faults in models than the state-of-the-art techniques (i.e., CARROT and ALERT) on average, respectively.

Zhao Tian

Tianjin University

China

Junjie Chen

Tianjin University

China

Zhi Jin

Peking University

China