The Android Open Source Project (AOSP) powers the world's largest mobile ecosystem, yet memory-safety vulnerabilities remain prevalent in native components due to the inherent risks of languages like C and C++, which allow manual memory management and unsafe operations. To mitigate these risks, AOSP began adopting Rust, a language with compile-time memory-safety guarantees, in 2021. However, the trajectory, mechanisms, and effectiveness of this transition remain empirically uncharted. We present the first longitudinal and systematic empirical study of Rust in AOSP. By analyzing 16 quarterly snapshots from January 2021 to October 2024, we track adoption trends and map the distribution of Rust code across 67 components. We further align Android Security Bulletin vulnerabilities with these components to assess memory-safety outcomes before and after Rust adoption, while also identifying the remaining non-Rust hotspots that may warrant future migration. In addition, we characterize platform-level integration mechanisms and examine unsafe governance through a large corpus of SAFETY comments, from which we distill actionable best practices and anti-patterns. Taken together, these results clarify Rust's role in AOSP by shedding light on its adoption, integration, and governance in a safety-critical mobile platform. They also provide industry practitioners with an empirical foundation and practical guidance for similar memory-safe migrations in large-scale production systems.
Unit testing is crucial for software reliability in JavaScript, yet constructing comprehensive suites remains costly and error-prone. JavaScript’s dynamic features, flexible typing, asynchronous execution, and reliance on third-party libraries, pose persistent challenges for automated test generation. Although large language models (LLMs) show promise in code reasoning and synthesis, existing methods often miss the multilayer contextual information required for executable, semantically correct tests. This paper introduces JSTestCraft, an agentic, multi-layer framework for adaptive JavaScript unit test generation. JSTestCraft reconstructs missing context via three enrichment agents: library, structural, and semantic. These agents capture inter-function topology, third-party API semantics, and inferred type constraints in a shared contextual memory for reasoning-driven test synthesis. A testing and optimization layer generates, executes, and iteratively refines test cases. Evaluated on 20 real-world Node.js repositories, JSTestCraft outperforms state-of-the-art baselines, improving test pass rate by 60.9%, statement coverage by 14.2%, and branch coverage by 47.3%. Ablation analysis confirms each agent’s contribution to contextual completeness. Beyond metrics, JSTestCraft discovers 13 previously unknown bugs across 5 repositories, 6 of which received substantive acknowledgment from maintainers or community contributors. These results demonstrate that context reconstruction and agentic collaboration enable LLMs to perform more reliable and adaptive testing in dynamic JavaScript environments.
