Large-language models (LLMs) have been leveraged to enhance the capability of automated program repair techniques in recent research. While existing LLM-based program repair techniques compared favorably to other techniques based on heuristics, constraint-solving, and learning in producing high-quality patches, they mainly target bugs that can be corrected by changing a single faulty method, which greatly limits the effectiveness of such techniques in repairing bugs that demand patches spanning across multiple methods. In this work, we propose the PReMM technique to effectively propose patches changing multiple methods. PReMM builds on three core component techniques: the faulty method clustering technique to partition the faulty methods into clusters based on the dependence relationship among them, enabling a divide-and-conquer strategy for the repairing task; the fault context extraction technique to gather extra information about the fault context which can be utilized to better guide the diagnosis of the fault and the generation of correct patches; the dual-agent-based patch generation technique that employs two LLM-based agents with different roles to analyze the fault more precisely and generate patches of higher-quality. We have implemented the PReMM technique into a tool with the same name and applied the tool to repair real-world bugs from datasets Defects4J V1.2 and V2.0. PReMM produced correct patches for 307 bugs in total. Compared with ThinkRepair, the state-of-the-art LLM-based program repair technique, PReMM correctly repaired 102 more bugs, achieving an improvement of 49.8%.

End-to-end UI testing plays an indispensable role in web testing. However, the maintenance of UI test scripts can become a challenge as web applications undergo changes, leading to the potential breakage of these scripts. The manual repair of broken scripts is a time-consuming and labor-intensive process, making it imperative to study automated repair approaches. Existing approaches have relied on either the Document Object Model (DOM) or visual information alone to repair broken scripts, which show limited effectiveness as they only utilize a subset of the available information. Furthermore, merely combining the two approaches is not sufficient to improve effectiveness, as the use of two disparate methods may result in conflicting repair outcomes. In this study, we present a novel approach to web test repair that considers both information in the DOM and UI. To optimize the utilization of this information, our method classifies it as either identity-related or appearance-related, subsequently prioritizing its application in the repair process. In addition, we propose a more advanced lightweight Convolutional Neural Network based approach for better processing visual information. Our approach has been implemented as a tool named Webrl, which is available for practical use and further research. The effectiveness of Webrl was evaluated on a set of broken UI scripts constructed from 38 real-world web sites and was found to outperform the state-of-the-art approaches by a significant margin.

The coordinated vulnerability disclosure model, widely adopted in open-source software (OSS) organizations, recommends the silent resolution of vulnerabilities without revealing vulnerability information until their public disclosure. However, the inherently public nature of OSS development leads to security fixes becoming publicly available in repositories weeks before the official disclosure of vulnerabilities. This time gap poses a significant security risk to OSS users, as attackers could discover the fix and exploit vulnerabilities before disclosure. Thus, there is a critical need for OSS users to sense fixes as early as possible to address the vulnerability before any exploitation occurs. In response to this challenge, we introduce EarlyVulnFix, a novel approach designed to identify silent fixes for taint-style vulnerabilities—a persistent class of security weaknesses where attacker-controlled input reaches sensitive operations (sink) without proper sanitization. Leveraging data flow and dependency analysis, our tool distinguishes two types of connections between newly introduced code and sinks, tailored for two common fix scenarios. Our evaluation demonstrates that EarlyVulnFix surpasses state-of-the-art baselines by a substantial margin in terms of F1 score. Furthermore, when applied to the 700 latest commits across seven projects, EarlyVulnFix detected three security fixes before their respective security releases, highlighting its effectiveness in identifying unreported vulnerability fixes in the wild.

Text classification is fundamental in natural language processing (NLP), and Graph Neural Networks (GNN) are recently applied in this task.However, the existing graph-based works can neither capture the contextual word relationships within each document nor fulfil the inductive learning of new words.In this work, to overcome such problems, we propose TextING 1 for inductive text classification via GNN.We first build individual graphs for each document and then use GNN to learn the finegrained word representations based on their local structures, which can also effectively produce embeddings for unseen words in the new document.Finally, the word nodes are incorporated as the document embedding.Extensive experiments on four benchmark datasets show that our method outperforms state-of-theart text classification methods.