The increasing complexity of software systems has made them more prone to bugs, prompting the development of automated bug localization techniques to ensure software reliability. Despite these techniques having demonstrated notable success at the file level, their application and optimization at the function level often encounter serious performance cliffs. This limitation underscores the urgent need for a dedicated framework for function-level bug localization, which we address through FunLoc, a novel framework that takes coarse-grained source files as input units and identifies fine-grained buggy functions as output. To address the critical challenges of handling domain-specific bug reports and managing vast function-level sample space, we introduce two key innovations that are seamlessly integrated into FunLoc. First, we design a contrastive learning-based domain-adaptive language model to enhance the framework's ability to process and interpret specialized bug reports effectively. Second, we propose an active learning-based dynamic negative sampling strategy to address the scalability issues arising from the extensive function-level sample space. To evaluate the effectiveness of our approach, we extend and release a function-level bug localization dataset derived from large-scale real-world projects. Extensive experiments demonstrate that our approach outperforms state-of-the-art techniques.

The process of software bug localization can be described as identifying the source code files (i.e., bug location) corresponding to the bug described in the bug report. Most existing bug localization approaches fall short in handling the following three aspects, including (L1) only using partial content in the bug report (i.e., title and description), (L2) direct semantic understanding of the entire bug reports and source files, and (L3) relying solely on semantic matching between bug reports and source files. To overcome these limitations, this paper constructs datasets in which the content of each bug report is augmented with prefix comments for addressing Ll and presents a novel model named BRS_BL for bug localization. Specifically, the proposed BRS_BL designs a specially tailored bug report summarization module to extract core information for semantic representation in bug reports and a chunking source file module to split the source code files into blocks based on lines and words for addressing L2. It further uses a fine-grained matching module utilizing semantic matching and incorporating some well-characterized software-specific features for addressing L3. The experimental results show that our model BRS_BL significantly outperforms the existing representative bug localization techniques in terms of several evaluation metrics across four real-world projects.

Various automated bug localization technologies have recently emerged that require adequate bug-fix records available to train a predictive model. However, many projects in practice might not provide these necessities, especially for new projects in the first release, due to the expensive human effort for constructing a large amount of bug-fix records. Aiming to capture the potential relevance distribution between the bug report and code file from a limited number of available bug-fix records, we present the first semi-supervised bug localization model named BL-GAN in this paper. For this purpose, the promising Generative Adversarial Network is introduced in BL-GAN, in which synthetic bug-fix records close to the real ones are constructed by searching the project directory tree to generate file paths instead of traversing the contents of all code files. For processing bug reports, the proposed BL-GAN adopts an attention-based Transformer architecture to capture semantic and sequence information. In order to capture the proprietary structural information in code files, BL-GAN incorporates a novel multilayer Graph Convolutional Network to process the source code in a graphical view. Extensive experiments on large-scale real-world datasets reveal that our model BL-GAN significantly outperforms the state-of-the-art on all evaluation measures.

With the rapid development of text mining, many studies observe that text generally contains a variety of implicit information, and it is important to develop techniques for extracting such information. Named Entity Recognition (NER), the first step of information extraction, mainly identifies names of persons, locations, and organizations in text. Although existing neural-based NER approaches achieve great success in many language domains, most of them normally ignore the nested nature of named entities. Recently, diverse studies focus on the nested NER problem and yield state-of-the-art performance. This survey attempts to provide a comprehensive review on existing approaches for nested NER from the perspectives of the model architecture and the model property, which may help readers have a better understanding of the current research status and ideas. In this survey, we first introduce the background of nested NER, especially the differences between nested NER and traditional (i.e., flat) NER. We then review the existing nested NER approaches from 2002 to 2020 and mainly classify them into five categories according to the model architecture, including early rule-based, layered-based, region-based, hypergraph-based, and transition-based approaches. We also explore in greater depth the impact of key properties unique to nested NER approaches from the model property perspective, namely entity dependency, stage framework, error propagation, and tag scheme. Finally, we summarize the open challenges and point out a few possible future directions in this area. This survey would be useful for three kinds of readers: (i) Newcomers in the field who want to learn about NER, especially for nested NER. (ii) Researchers who want to clarify the relationship and advantages between flat NER and nested NER. (iii) Practitioners who just need to determine which NER technique (i.e., nested or not) works best in their applications.