arXiv subjects

Ruikai Huang

Publications and source records attributed to Ruikai Huang.

3 recordsLinked to original sources

arXiv2026

Kotlin-MP: DSL and IR Transformer for Parallelism

While Kotlin provides exceptional abstractions for asynchronous concurrency (like Coroutines for I/O), it lacks low-overhead, directive-based constructs for true hardware parallelism. When developers attempt to parallelize compute-bound mathematical loops using standard task-parallel libraries, they encounter significant runtime overhead such as lambda allocations and state-machine generation due to the abstraction layers, and face difficulties in ensuring readability, reliability and maintainability in implementing them. To address this, this project proposes Kotlin-MP, a native Kotlin Domain-Specific Language (DSL) paired with a custom Intermediate Representation (IR) transformer as a Kotlin compiler plugin. The DSL provides an intuitive, OpenMP-like syntax for developers to denote parallel regions, schedules, and critical sections. Instead of acting as a standard runtime wrapper, the IR transformer intercepts this DSL at compile-time and structurally rewrites the code. It lowers the parallelized sections directly into optimized \textit{java.util.concurrent.ForkJoinPool} tasks, automatically managing loop chunking, performing thread scheduling, and injecting hardware synchronization and mutual exclusion.

cs.DC↗

arXiv2025

SAINT: Service-level Integration Test Generation with Program Analysis and LLM-based Agents

Enterprise applications are typically tested at multiple levels, with service-level testing playing an important role in validating application functionality. Existing service-level testing tools, especially for RESTful APIs, often employ fuzzing and/or depend on OpenAPI specifications which are not readily available in real-world enterprise codebases. Moreover, these tools are limited in their ability to generate functional tests that effectively exercise meaningful scenarios. In this work, we present SAINT, a novel white-box testing approach for service-level testing of enterprise Java applications. SAINT combines static analysis, large language models (LLMs), and LLM-based agents to automatically generate endpoint and scenario-based tests. The approach builds two key models: an endpoint model, capturing syntactic and semantic information about service endpoints, and an operation dependency graph, capturing inter-endpoint ordering constraints. SAINT then employs LLM-based agents to generate tests. Endpoint-focused tests aim to maximize code and database interaction coverage. Scenario-based tests are synthesized by extracting application use cases from code and refining them into executable tests via planning, action, and reflection phases of the agentic loop. We evaluated SAINT on eight Java applications, including a proprietary enterprise application. Our results illustrate the effectiveness of SAINT in coverage, fault detection, and scenario generation. Moreover, a developer survey provides strong endorsement of the scenario-based tests generated by SAINT. Overall, our work shows that combining static analysis with agentic LLM workflows enables more effective, functional, and developer-aligned service-level test generation.

cs.SE↗

arXiv2025

Towards Generalizable Context-aware Anomaly Detection: A Large-scale Benchmark in Cloud Environments

Anomaly detection in cloud environments remains both critical and challenging. Existing context-level benchmarks typically focus on either metrics or logs and often lack reliable annotation, while most detection methods emphasize point anomalies within a single modality, overlooking contextual signals and limiting real-world applicability. Constructing a benchmark for context anomalies that combines metrics and logs is inherently difficult: reproducing anomalous scenarios on real servers is often infeasible or potentially harmful, while generating synthetic data introduces the additional challenge of maintaining cross-modal consistency. We introduce CloudAnoBench, a large-scale benchmark for context anomalies in cloud environments, comprising 28 anomalous scenarios and 16 deceptive normal scenarios, with 1,252 labeled cases and roughly 200,000 log and metric entries. Compared with prior benchmarks, CloudAnoBench exhibits higher ambiguity and greater difficulty, on which both prior machine learning methods and vanilla LLM prompting perform poorly. To demonstrate its utility, we further propose CloudAnoAgent, an LLM-based agent enhanced by symbolic verification that integrates metrics and logs. This agent system achieves substantial improvements in both anomaly detection and scenario identification on CloudAnoBench, and shows strong generalization to existing datasets. Together, CloudAnoBench and CloudAnoAgent lay the groundwork for advancing context-aware anomaly detection in cloud systems. Project Page: https://jayzou3773.github.io/cloudanobench-agent/

cs.AI↗