physical unit conversion

This study addresses the lack of conversion support in existing typed unit calculi and the absence of scale-invariance guarantees in practical languages by proposing the ΛS calculus. This method is the first to integrate unit conversion into a rigorous type system, supporting quantification and vector linear mappings while precisely delineating the boundaries of scale invariance. It formally proves global invariance for expressions without constant terms and local invariance for those involving conversions. The entire framework is formally verified in Lean 4, yielding verified decision procedures and checkers alongside native binary compilation for evaluation. Furthermore, this work establishes a consistency-checking method for unit declarations, extracts precise conversion factors, and proves both an erasure theorem and the Buckingham π theorem for dimensional analysis with n variables.

To address semantic modeling and automatic conversion of prefixed units, this paper introduces the first unified formal semantic model grounded in category and group theory: it jointly models unit prefixes, base units, and conversion rules as a ternary relation endowed with both group and category structures, and defines natural and accidental semantic operations; based on this, it establishes a hierarchy of subclasses with progressively strengthened algebraic properties. Key contributions include: (1) the first unit-semantic framework integrating category theory and group theory, enabling unified characterization of prefixed units and conversion rules; (2) a classification system for conversion relations with well-defined algebraic properties; and (3) a symbolic, deterministic, polynomial-time rewriting-based conversion algorithm that eliminates reliance on lookup tables and supports rigorously verifiable unit reasoning.

This work addresses the lack of a unified semantic foundation in current software systems, which creates comprehension gaps among development, usage, and governance due to deficiencies in usability, modularity, and accountability. To bridge this divide, the paper proposes grounding software semantics in domain behavioral phenomena—specifically individuals, actions, and facts—as a shared conceptual vocabulary for stakeholders. This approach systematically integrates phenomenon-based modeling into software development by organizing behaviors into conceptual units, leveraging large language models (LLMs) to map semantics to modular, readable code, and establishing agent accountability through behavior-oriented norms. Empirical evaluation demonstrates that the proposed method significantly enhances the quality of usability design, improves the modularity and readability of LLM-generated code, and strengthens the accountability of autonomous agent behaviors.

Rigid activity implementation binding in digital business processes hinders adaptation to heterogeneous organizational requirements. Method: This paper proposes a three-level dynamic binding mechanism—operating at compile time, launch time, and runtime—that enables concurrent execution of multiple implementations for the same activity and supports context-aware, dynamic customization of input/output data contracts. Integrating Software Product Line (SPL) engineering with Process-Aware Information Systems (PAIS), we develop a variability modeling and runtime feature configuration framework. Contribution/Results: Our approach achieves, for the first time, end-to-end flexible activity binding across the full process lifecycle. It overcomes the limitations of conventional single-version, static binding by enabling on-demand composition of diverse activity implementations and data interfaces within a unified process model. This significantly enhances the adaptability and configurability of process systems in multi-organizational settings.

Existing code-level formal verification tools scale poorly to large-scale software, while mainstream unit-level verification relies heavily on manual effort, often missing critical defects. This paper proposes the “Unit Proof Framework” research agenda—the first systematic definition of a unit verification paradigm supporting automated decoupling and independent verification of code units. Methodologically, it integrates formal verification, program analysis, modular verification, and automated toolchain design, with deep alignment to industrial development practices (e.g., AWS workflows). Its core contributions include: (1) establishing a scalable, engineering-friendly unit verification methodology; (2) characterizing a taxonomy of key technical challenges; (3) overcoming bottlenecks inherent in manual verification; and (4) significantly improving early detection of code-level defects. Collectively, this work lays the theoretical foundation and provides a practical technical pathway for building high-assurance, deployable automated verification infrastructure.

Existing software engineering metrics often fail to effectively support critical development decisions—such as whether refactoring is necessary or whether testing is sufficient—thereby limiting their practical utility. This work addresses this gap by systematically introducing metrological principles (the science of measurement) into the domain of software measurement for the first time. It proposes a metrology-informed approach to metric modeling and evaluation, establishing a rigorous scientific foundation for the design of software metrics. By grounding metric development in established measurement theory, the proposed method substantially enhances the usability, credibility, and decision-support capability of metrics in real-world engineering contexts. This study thus opens a new research direction for software measurement, aligning it more closely with the epistemological standards of empirical science.

本文提出了一种基于仓库的实现方法，通过自动接口更新和一致性检查减少有人和无人飞行器软件开发中跨域不一致问题。

This study addresses the growing challenge posed by the widespread involvement of AI agents in software development, which undermines the long-standing assumption that development artifacts are exclusively produced by human professionals—an assumption underpinning traditional software metrics. The work systematically exposes how AI-generated traces compromise the foundational premises of established software measurement practices, thereby threatening the validity of prior empirical conclusions. To confront this issue, the authors propose an AI-augmented, systematic replication methodology that integrates modern data analytics with empirical software engineering techniques to rigorously re-evaluate key findings. The project advances a dynamic, reproducible, and sustainable measurement paradigm capable of adapting to evolving data ecosystems, offering a robust and timely framework for software metrics in the AI era.

This study addresses the lack of implementation guidelines for ISO data quality standards, which hinders their practical adoption. We systematically categorize ISO metrics and formulate executable specifications, developing dqmeasure, an open-source Python library for automated assessment. The core innovation lies in a novel method that automatically learns parameters from reference data, eliminating reliance on manual rules. Experimental results demonstrate that the proposed metrics decrease monotonically as data errors increase and exhibit strong correlation with downstream machine learning performance. Furthermore, the approach supports linearly scalable monitoring. This work provides an effective tool for the automated evaluation of data quality.

本文探讨了通过整合数据工程和软件工程实践（如DataOps、MLOps等）来重塑面向数据和AI系统的软件开发生命周期，以应对传统SDLC在处理这些系统时遇到的挑战。