Automated unit test generation has been widely studied, with Large Language Models (LLMs) recently showing significant potential. LLMs like GPT-4, trained in vast text and code data, excel in various code-related tasks, including unit test generation. However, existing LLM-based approaches often focus solely on the context within the code itself, such as referenced variables, while neglecting broader task-specific contexts, such as the utility of referring to existing tests of relevant methods in unit test generation. Moreover, in the context of unit test generation, these tools prioritize high code coverage, often at the expense of practical usability, correctness, and maintainability. In response, we propose Reference-Based Retrieval Augmentation , a novel mechanism that extends LLM-based Retrieval-Augmented Generation (RAG) to retrieve relevant information by considering task-specific context. In the unit test generation task, for a given focal method, the reference relationships is defined as the reusability or referentiality of tests between the focal method and other methods. To generate high-quality unit tests for the focal method, the test reference relationships are then used to retrieve relevant methods and their existing unit tests. Specifically, we account for the unique structure of unit tests by dividing the test generation process into Given , When , and Then phases. When generating unit tests for a focal method, we retrieve pre-existing tests of other relevant methods, which can provide valuable insights for any of the Given , When , and Then phases. We implement this approach in a tool called RefTest , which sequentially performs preprocessing, test reference retrieval, and unit test generation, using an incremental strategy in which newly generated tests guide the creation of subsequent ones. We evaluated RefTest on 12 open source projects with 1,515 methods, and the results demonstrate that RefTest consistently outperforms existing tools in terms of correctness, completeness, and maintainability of the generated tests.

Mobile task automation is an emerging technology that leverages AI to automatically execute routine tasks by users' commands on mobile devices like Android, thus enhancing efficiency and productivity.While large language models (LLMs) excel at general mobile tasks through training on massive datasets, they struggle with app-specific workflows.To solve this problem, we designed UI Map, a structured representation of target app's UI information.We further propose a UI Map-guided LLM-based approach UICOMPASS to automate mobile tasks.Specifically, UICOMPASS first leverages static analysis and LLMs to automatically build UI Map from either source codes of apps or byte codes (i.e., APK packages).During task execution, UICOMPASS mines the task-relevant information from UI Map to feed into the LLMs, generates a planned path, and adaptively adjusts the path based on the actual app state and action history.Experimental results demonstrate that UICOMPASS achieves a 14.52% higher task executing success rate than SOTA approaches.Even when only APK is available, UICOMPASS maintains superior performance, demonstrating its applicability to closed-source apps.

Large Language Models (LLMs) have shown significant potential for vulnerability localization in software security. However, current LLM-based approaches face a critical dilemma: direct application of general-purpose LLMs lacks crucial domainspecific expertise, while fine-tuning suffers from limited robustness when faced with unfamiliar data. These problems result in subpar performance in vulnerability localization and weak generalization capabilities. To address these limitations, we introduce ENVUL, a novel domain adaptation framework for vulnerability localization. ENVUL improves vulnerability localization by synergizing enhanced task-specific tuning with prompt engineering of general-purpose LLMs. ENVUL incorporates three key innovations for addressing two problems: (1) how to optimize fine-tuning for localization task, and (2) when to wisely choose tuning and prompting. To solve the first problem, we introduce: (a). a context Consolidator that captures rich statement-level code semantic, improving the model's understanding of code context; (b). a semantic Indicator employing attention rectification to highlight patterns indicative of vulnerabilities, focusing the model on critical security signals. To solve the second problem, we introduce a dynamic routing mechanism based on joint-representation similarity analysis that strategically delegates tasks between the fine-tuned model and the general LLM. It ensures ENVUL's robust performance across diverse real-world vulnerability types. Real-world evaluations demonstrate ENVUL's robust expertise in outperforming state-of-the-art vulnerability localization baselines, achieving absolute improvements of$\mathbf{2 2. 7 \% - 3 0. 3 \%}$in top-1 accuracy. Notably, ENVul exhibits exceptional generalization, achieving$\mathbf{4 3. 6 \% - 5 0 \%}$higher accuracy on unfamiliar vulnerability types.

In recent years, the proliferation of software vulnerabilities has significantly increased the complexities and costs associated with manual remediation efforts. Although AI-based methods for automated vulnerability repair are gaining traction, many existing approaches have two limitations: 1) treat code as a sequence of tokens, neglecting critical structural information like control flow and data flow, and 2) do not fully utilize the repair patterns of vulnerabilities. To address these limitations, we introduce FAVOR, an innovative tool that utilizes both the vulnerable function's code and its control flow graph (CFG) as inputs. FAVOR incorporates a dependency embedding module to capture structural and dependency information and leverages CodeT5, a state-of-the-art model pre-trained for code generation tasks. To further enhance the repair process, we introduce a pattern store that uses KNN search to retrieve similar past repair patterns, which helps guide the model toward generating more contextually accurate patches. In our experiments, FAVOR, trained on a dataset of 6548 faulty C/C++ functions, repaired 45 more vulnerabilities compared to VULREPAIR, demonstrating improved accuracy and efficiency in automated vulnerability repair.

Web UI test cases are used for the automatic testing of web applications. When a web application is updated, these UI tests should also be updated for regression testing of the new version of web application. With the rapid evolution, updating UI tests is a tedious and time-consuming task. To solve these problems, automatically repairing web UI tests has gained increasing attention recently. To repair web UI tests, the most important step is to match the UI elements before and after the web page update. Existing work matches UI elements according to visual information, attributes value, or Document Object Model (DOM) structures. However, they either achieve low element matching accuracy or only work on simple UI tests. To solve these problems, we proposed UITestFix, an approach based on a novel iterative matching algorithm for improving the accuracy of matching UI elements. UITestFix is designed based on two main insights: (1) beyond attribute and DOM structures, the relations between different elements can also guide the matching process, and (2) the matching results of previous iterations could guide the matching of the current iteration. Our evaluation of publicly available datasets and two industrial apps shows that UITestFix outperforms four existing approaches by achieving more accurate element matching and producing more correct fixes.