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1. LEAM LEAM Public

   We propose a novel DL-based mutation technique (LEAM), which adapts the syntax-guided encoder-decoder architecture to build two sub-models based on AST information, for better learning to represent…

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2. CODA CODA Public

   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 and reference inputs to guide…

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3. Deeplive Deeplive Public

   We propose Deeplive, which maximizes QoE through deep reinforcement learning, so it does not rely on fixed rules. To accelerate the training process of Deeplive, we further propose optimization inc…

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4. CodeDenoise CodeDenoise Public

   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 suc…

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5. EMD EMD Public

   Replication Package for "Large Language Models for Equivalent Mutant Detection: How Far Are We?", ISSTA 2024

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6. CoDefense CoDefense Public

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