Automated test case generation enhances the efficiency and quality of software testing. Learning-based test case generation methods require an understanding of the relationships between test cases and focal methods. Accurate traceability links between test cases and focal methods provide a clear relational model, facilitating more effective training of test case generation models. However, existing techniques frequently struggle to provide precise traceability links. We conduct an empirical study on the Methods2Test dataset, which includes a wide range of test cases and corresponding focal methods, to identify the limitations of current techniques. The causes of inaccurate traceability links are divided into two categories: incorrect extraction and missed extraction. The incorrect extraction category includes method overloading errors, name matching failures, and constructor ignorance. The missed extraction category involves constructor ignorance, similar but non-identical names, and difficulties in locating test classes involving subclasses. Based on the insights from this study, we propose Coach(Combine trACe Heuristics), an automated approach for establishing test-to-code traceability links. Coach establishes file-level and class-level links as a foundation, integrates multiple heuristics, and defines their scopes to build method-level links, improving both applicability and precision. We evaluate Coach against the M2T method (used in Methods2Test), the NC and LCBA baselines. Experimental results show that Coach outperforms other methods in terms of precision, coverage, and efficiency. In addition, we apply Coach to 19,518 real-world Java projects, creating a novel large-scale dataset called Tracets4J (TRACE TeSt for Java). Models fine-tuned on Tracets4J outperform those trained on the Methods2Test dataset, as shown by BLEU-4 and CodeBLEU. This demonstrates the superiority of Tracets4J and the effectiveness of Coach in improving test case generation.

Recently, generating 3D assets with the control of condition images has achieved impressive quality. However, existing 3D generation methods are limited to handling a single control objective and lack the ability to utilize multiple images to independently control different regions of a 3D asset, which hinders their flexibility in applications. We propose Fuse3D, a novel method that enables generating 3D assets under the control of multiple images, allowing for the seamless fusion of multi-level regional controls from global views to intricate local details. First, we introduce a Multi-Condition Fusion Module to integrate the visual features from multiple image regions. Then, we propose a method to automatically align user-selected 2D image regions with their associated 3D regions based on semantic cues. Finally, to resolve control conflicts and enhance local control features from multi-condition images, we introduce a Local Attention Enhancement Strategy that flexibly balances region-specific feature fusion. Overall, we introduce the first method capable of controllable 3D asset generation from multiple condition images. The experimental results indicate that Fuse3D can flexibly fuse multiple 2D image regions into coherent 3D structures, resulting in high-quality 3D assets. Code and data for this paper are at https://jinnmnm.github.io/Fuse3d.github.io/.

Protecting confidential data against memory disclosure attacks is crucial to many critical applications, especially those involve cryptographic operations. However, it is neither easy to identify involved cryptographic confidential data in a program nor to implement a fine-grained and yet efficient protection. Existing defensive techniques face many shortcomings such as coarse-grained protection or exorbitant overhead. As a result, real world crypto applications seldom applied this kind of protection in practice.To make the protection of cryptographic confidential data practical, we design and implement CRYPTOMPK, a source code analysis and transformation system to implement a domain-based memory isolation. CRYPTOMPK first automatically tracks and labels all sensitive memory buffers and operations in source code with a context-sensitive, crypto-aware information flow analysis. Then it partitions the source code into crypto and non-crypto domains with a context-dependent privilege switch instrumentation. By further utilizing Intel Memory Protection Keys (MPK), CRYPTOMPK generates executables with efficient domain switching, protecting them against typical memory disclosure vulnerabilities such as arbitrary memory read. In particular, by using CRYPTOMPK, a large number of intermediate memory buffers that have been previously ignored before are well protected, and thus the security risks are reduced significantly. We leveraged CRYPTOMPK to protect prevalent applications such as Apache and Nginx with widely used crypto libraries (e.g., OpenSSL, LibSodium). CRYPTOMPK only needs several minutes to analyze each of these complex cryptographic programs and incurs at most 9.53% performance overhead for the protected programs.