detector integration

This work addresses the limitations of traditional structural coverage metrics in embedded software testing, which are often confined to the unit level and fail to reflect true coverage completeness in integration and system testing. Instrumentation-based approaches risk perturbing runtime behavior, while pure tracing techniques suffer from unreliability under high compiler optimization. To overcome these challenges, the paper proposes an integration-test-driven coverage strategy featuring a novel “integration-first” closed-loop workflow. By synergistically combining embedded tracing with hybrid runtime analysis (hRA) to preserve semantic boundaries, and leveraging source-to-target mapping for evidential traceability alongside Hyper Coverage for cross-variant merging, the approach establishes a unified evidence-integration mechanism. Evaluated on -O3-optimized release binaries, it reliably achieves branch, condition, and MC/DC coverage measurements and precisely identifies source code lines consistently uncovered across all variants, thereby significantly enhancing confidence in the test completeness of embedded systems.

To address the trajectory reconstruction bottleneck posed by massive detector hit data in the High-Luminosity LHC (HL-LHC) era, this work proposes a novel end-to-end particle tracking paradigm based on the Transformer architecture. Targeting the high computational cost of hit-to-track association in conventional methods, we pioneer the adaptation of large language model principles to high-energy physics tracking—introducing a dual-path architecture comprising “next-hit prediction” and “event-level full-track one-shot prediction.” We further demonstrate, for the first time, the feasibility and practicality of an encoder-classifier Transformer design for physics-based tracking. Evaluated on the lightweight TrackML dataset (5-level complexity) and the REDVID simulation framework, our approach achieves an optimal trade-off between accuracy and latency: the single-prediction Transformer attains state-of-the-art association accuracy in complex scenarios while meeting real-time trigger latency constraints.

This study addresses the critical challenge of “silent failures”—erroneous model decisions without system crashes—in production machine learning systems, which undermine conventional monitoring and expose a gap in empirically grounded observability practices. Through seven cross-industry focus group interviews, we applied qualitative thematic coding and scenario mapping to systematically identify the types of observability data practitioners collect and their concrete uses in model validation, anomaly detection, and root-cause diagnosis. Our findings constitute the first empirical characterization of key blind spots in current ML observability tooling: delayed response to feature drift, lack of decision traceability, and difficulty quantifying business impact. Based on these insights, we propose three foundational design principles for next-generation observability tools—explanability-awareness, causal attribution support, and business-impact alignment—and establish an empirically anchored theoretical foundation for future evaluation frameworks and standardization efforts. (149 words)

This work addresses the pervasive issue of redundant and isolated messages in system logs, which hinder downstream tasks such as model reasoning and anomaly detection. To tackle this challenge, the authors propose LogPurifier—the first task-agnostic log cleansing framework—that systematically purifies logs by extracting log templates and modeling their dependencies to accurately identify and remove messages irrelevant to system functional behavior. By doing so, LogPurifier enables effective log sanitization applicable across diverse analytical scenarios. Experimental results demonstrate that LogPurifier substantially improves both accuracy and efficiency in various downstream tasks, thereby validating its effectiveness and generalizability.

To address insufficient observability in software systems—leading to slow fault localization and low operational efficiency—this paper designs and implements a lightweight, full-stack observability framework. The framework integrates Java bytecode instrumentation with event stream collection to enable runtime call-chain tracing, performance diagnostics, and root-cause analysis. It introduces a novel dual-mode deployment architecture supporting both online services and on-premises deployment, and achieves cross-toolchain collaborative visualization via tight REST API integration with ExplorViz. Evaluated on the TeaStore benchmark, the system delivers millisecond-scale distributed tracing and real-time heatmap rendering, reduces end-to-end latency by 32%, and shortens mean time to fault identification to the minute level. These results significantly enhance observability and operational intelligence for microservice systems.

This study addresses the imbalance in the test pyramid—characterized by an overreliance on coarse-grained integration and system tests, which leads to difficulties in fault localization and slow execution—by proposing, for the first time, a method to automatically generate unit tests from existing integration tests. The approach combines static and dynamic analysis to automatically isolate component dependencies and enhance coverage at the unit level. Implemented as a Node.js tool and evaluated on twelve open-source JavaScript projects, the technique produces high-quality unit tests that significantly improve test suite structure, thereby increasing both testing efficiency and maintainability.

This work addresses the absence of open-source time series point forecasting tools compliant with regulatory frameworks such as the EU AI Act in safety-critical contexts. It presents the first solution that deeply embeds standards—including the EU AI Act and IEC 61508—into library design through four mandatory development rules: zero dead code, deterministic processing, fail-safe mechanisms, and minimal dependencies. Regulatory compliance is further ensured via rigorous process controls, including model cards, executable documentation, CI/CD pipelines, and REUSE licensing compliance, which collectively establish a bidirectional traceability matrix linking regulatory clauses to code implementation. The resulting open-source package, spotforecast2-safe (licensed under AGPL 3.0+), has undergone end-to-end validation on European power system forecasting tasks, demonstrating safe, auditable, reproducible, and regulation-compliant time series prediction.

This study addresses the challenge of achieving both high accuracy and interpretability in malicious code detection under static analysis settings where package metadata, maintainer information, and dynamic execution traces are unavailable. To this end, we propose the first interpretable detection framework that derives behavioral sequences from static call graphs, leverages a Transformer architecture to model these sequences, and integrates an explanation module to precisely localize suspicious source code regions. Designed to operate within the practical constraints of real-world DevSecOps pipelines, our approach significantly outperforms existing open-source tools on Python and JavaScript packages from PyPI and npm, meeting production-grade requirements in terms of detection accuracy, runtime overhead, memory consumption, and false positive rate. We also release Open Malicious-Code Bench, an open benchmark accompanying this work.

This work proposes the first unified formal framework that integrates runtime verification and model-based diagnosis, overcoming the limitations of traditional approaches that rely on disjoint toolchains and struggle to jointly support online fault detection and localization. Built upon the LOLA stream specification language, the method encodes system behavior, component health states, and observational data into a common stream-based representation. This formulation accommodates both time-invariant and transient faults and handles nondeterministic observations. By leveraging online stream processing and explicit fault semantics, the approach enables efficient, continuous end-to-end fault localization without requiring additional tools, thereby significantly enhancing the integration and practicality of runtime monitoring for complex systems.

This work addresses the lack of standardized governance in AI-assisted development of scientific software, which currently hinders compliance with stringent quality assurance requirements such as ASME NQA-1. The authors propose a structured framework that, for the first time, integrates large language models with software verification and validation (V&V) methodologies under NQA-1 compliance. Using the open-source nuclear-grade code TMAP8 as a testbed, they establish an AI-assisted V&V use case development process that is verifiable, traceable, and auditable. The framework explicitly defines mechanisms for disclosing AI-generated content, subjecting it to rigorous review, and assigning human accountability, thereby unifying transparency, reproducibility, and regulatory compliance. This approach ensures software correctness while meeting the rigorous demands of high-assurance quality standards.