Recent years have witnessed remarkable advances in graph representation learning using Graph Neural Networks (GNNs). To fully exploit the unlabeled graphs, researchers pre-train GNNs on large-scale graph databases and then fine-tune these pre-trained <u>...

Vulnerability detection is garnering increasing attention in software engineering, since code vulnerabilities possibly pose significant security. Recently, reusing various code pre-trained models (e.g., CodeBERT, CodeT5, and CodeGen) has become common ...

Bug reports, containing crucial information such as the Observed Behavior (OB), the Expected Behavior (EB), and the Steps to Reproduce (S2R), can help developers localize and fix bugs efficiently. However, due to the increasing complexity of some bugs ...

Historical newspapers serve as invaluable resources for understanding past societies and preserving cultural heritage. However, digitizing these newspapers presents challenges due to their complex layouts and vast content. Article segmentation, ...

Current industry trends demand automation in every aspect, where machines could replace humans. Recent advancements in conversational agents have grabbed a lot of attention from industries, markets, and businesses. Building conversational agents that ...

We introduce C-Pack, a package of resources that significantly advances the field of general text embeddings for Chinese. C-Pack includes three critical resources. 1) C-MTP is a massive training dataset for text embedding, which is based on the curation ...

Code search, which refers to the process of identifying the most relevant code snippets for a given natural language query, plays a crucial role in software maintenance. However, current approaches heavily rely on labeled data for training, which results ...

The automation of the classification of issue reports helps to improve the efficiency of the software tracking cycle. This task can be considered a multi-class classification problem. Therefore, this work is a participation in the NLBSE'24 Issue Report ...

Pre-trained Machine Learning (ML) models help to create ML-intensive systems without having to spend conspicuous resources on training a new model from the ground up. However, the lack of transparency for such models could lead to undesired consequences ...

Documentation debt hinders the effective utilisation of open-source software. Although code summarisation tools have been helpful for developers, most would prefer a detailed account of each parameter in a function rather than a high-level summary. ...

The tremendous success of Stack Overflow has accumulated an extensive corpus of software engineering knowledge, thus motivating researchers to propose various solutions for analyzing its content. The performance of such solutions hinges significantly on ...

Language models such as Bidirectional Encoder Representations from Transformers (BERT) have been very effective in various Natural Language Processing (NLP) and text mining tasks including text classification. However, some tasks still pose challenges ...

Recent studies on pre-trained vision/language models have demonstrated the practical benefit of a new, promising solution-building paradigm in AI where models can be pre-trained on broad data describing a generic task space and then adapted successfully ...

Continuous integration and delivery (CI/CD) are nowadays at the core of software development. Their benefits come at the cost of setting up and maintaining the CI/CD pipeline, which requires knowledge and skills often orthogonal to those entailed in ...

Code search, which locates code snippets in large code repositories based on natural language queries entered by developers, has become increasingly popular in the software development process. It has the potential to improve the efficiency of software ...

Upon evolving their software, organizations and individual developers have to spend a substantial effort to pay back technical debt, i.e., the fact that software is released in a shape not as good as it should be, e.g., in terms of functionality, ...

Automatic emotion recognition has a wide range of applications in human-computer interaction. In this paper, we present our work in the Multimodel Emotion Recognition (MER) 2023, which contains three sub-challenges: MER-MULTI, MER-NOISE, and MER-SEMI. We ...

Numerous pre-training techniques for visual document understanding (VDU) have recently shown substantial improvements in performance across a wide range of document tasks. However, these pre-trained VDU models cannot guarantee continued success when the ...

Pre-trained language models have become the prevailing approach for handling natural language processing tasks in recent years. Given the similarities in sequential features between symbolic music and natural language text, it is fairly logical to adopt ...

There is a trend of researchers and practitioners to directly apply pre-trained models to solve their specific tasks. For example, researchers in software engineering (SE) have successfully exploited the pre-trained language models to automatically ...