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Traditional program slicing techniques are crucial for early bug detection and manual/automated debugging of online code snippets. Nevertheless, their inability to handle incomplete code hinders their real-world applicability in such scenarios. To ...

Large pre-trained models like BERT and RoBERTa have gained massive popularity as they have surpassed previous state-of-the-art models in various Natural Language Processing (NLP) tasks. Nevertheless, interpreting their behavior is still an ongoing ...

The application of linguistic knowledge derived from pre-trained language models has demonstrated considerable potential in text classification tasks. Despite this, effectively learning the distance between samples and different labels for ...

Recently, fine-tuning pre-trained code models such as CodeBERT on downstream tasks has achieved great success in many software testing and analysis tasks. While effective and prevalent, fine-tuning the pre-trained parameters incurs a large ...

Automatic program understanding and generation techniques could significantly advance the productivity of programmers and have been widely studied by academia and industry. Recently, the advent of pre-trained paradigm enlightens researchers to develop ...