online-offline consistency

This work addresses the growing complexity of CI/CD pipelines and the lack of structured analysis capabilities in existing tools for understanding their behavior, failures, and version evolution. The authors propose an innovative approach that uniquely integrates digital twin technology with BPMN-based modeling in DevOps contexts. By automatically parsing raw CI configurations and execution logs, the method constructs structured, high-level process models that enable pipeline visualization, failure traceability, and cross-version comparison. Evaluated across multiple open-source projects, the approach demonstrates effectiveness in monitoring, evolutionary analysis, and fault diagnosis, offering a modular and extensible foundational framework for the analysis and optimization of CI/CD pipelines.

Medical AI deployment is hindered by insufficient production readiness of machine learning (ML) training pipelines. Method: This paper presents a progressive architectural evolution path—monolithic (chaotic) → modular monolithic → microservices—using SPIRA, a voice-based pre-diagnostic system for respiratory insufficiency, as a case study. It systematically introduces continuous training (CT) and a software-quality-attribute-driven MLOps governance framework tailored to healthcare, integrating modular design, microservice decomposition, and engineered CI/CD pipelines. Contribution/Results: The approach significantly improves pipeline maintainability, fault tolerance, and scalability, enabling stable, iterative evolution of SPIRA. It establishes an “agile ML + robust software engineering” co-design paradigm, delivering a reusable methodology and practical benchmark for engineering medical AI in highly regulated environments.

Existing approaches struggle to effectively quantify the similarity and quality between synthetic and real data in evaluating tool-augmented agents. To address this gap, this work proposes SynAE, a novel framework that establishes the first multi-axis evaluation system tailored for multi-turn tool-use scenarios. SynAE introduces four fine-grained metric categories—assessing task instructions, tool invocations, final outputs, and downstream evaluation performance—to systematically measure synthetic data across dimensions of validity, fidelity, and diversity. Integrating natural language processing, trajectory modeling, and controllable generation techniques, the framework enables a reproducible evaluation pipeline and successfully identifies several representative failure modes in synthetic data generation. Empirical results demonstrate that such multidimensional assessment is essential for enhancing the reliability of agent evaluations.

This study addresses the challenges of regression testing in remote and hybrid work environments, where communication, coordination, and quality assurance are increasingly complex. Through qualitative interviews with 20 software practitioners, complemented by process analysis, tool integration assessment, and coding of collaborative practices, the research systematically investigates the sociotechnical evolution of regression testing in distributed settings. Findings indicate that while core testing phases remain largely stable, teams increasingly rely on documentation, automation, and integrated toolchains to sustain effectiveness. Standardized reporting formats, shared repositories, and traceability mechanisms significantly mitigate collaboration barriers inherent in remote work. The study offers novel insights and practical guidance for ensuring software quality in geographically dispersed development contexts.

To address test redundancy, high feedback latency, and inconsistent pre- vs. post-commit test selection objectives in large-scale multilingual monorepos, this paper proposes the first pipeline-aware, bi-objective reinforcement learning framework for regression test optimization: failure detection is prioritized during pre-commit testing, while flaky-change identification is emphasized post-commit. The method operates entirely on language-agnostic features, integrating pipeline semantic modeling with online log analysis to support dynamically evolving industrial test suites. Evaluated on 20 weeks of real-world CI data, it achieves significantly reduced average feedback latency, a 32% improvement in pre-commit test selection precision, and a 41% reduction in false positives—without requiring expensive features such as code coverage.

This study addresses the limitations of existing SysML verification approaches, which are often tool-dependent and restricted to performance properties, lacking support for automated validation of behavioral and interface requirements. To overcome these shortcomings, this work proposes a tool-agnostic, automated verification workflow driven by SysML test cases, integrating UML Testing Profile and behavioral diagram constructs to enable unified validation of multidimensional attributes—including behavior, timing, and state responses. The methodology was developed through a mixed-methods research strategy combining literature review and stakeholder interviews, and its efficacy was empirically validated across two independent SysML toolchains. The approach not only transcends the constraints of conventional parametric methods but also enables automatic traceability of verification results back to the original model elements.

This work addresses the lack of a general, auditable dynamic control mechanism in existing training systems, which typically rely on framework-specific code. The authors propose the first cross-framework, open-source control plane that exposes training interfaces through a unified protocol, integrating declarative configuration, request validation, and secure control-point scheduling within the Aim workspace to enable metric monitoring, real-time intervention, and operational traceability. The system supports safe human and automated controller interventions during training while fully logging all operational trajectories. Experiments across five NLP and reinforcement learning tasks demonstrate its effectiveness, and the open-source implementation provides a foundation for reproducible human-in-the-loop training.