track adoption metrics

本文提出了一种通过生产使用信号直接计算变更管理阶段进展的方法来衡量企业AI采用情况，并提供了一个统一框架、具体操作化模型及开源实现。

Enterprise KPIs are often presented in isolation, lacking the contextual grounding and narrative structure necessary to support collaborative interpretation. To address this, we propose the “Metric Narrative” framework, which introduces a hierarchical, KPI-centric interactive canvas integrating time-series visualizations with contextualized textual annotations. The framework ensures semantic alignment between charts and text, enables cross-modal annotation, and supports context-aware collaborative recommendation. Innovatively, it adopts a “remixable” visualization paradigm that facilitates dynamic narrative construction and evolution. Guided by qualitative interviews with data practitioners, we developed a recommendation algorithm that jointly models metric semantics and usage patterns. Evaluation with six enterprise data experts demonstrates that our approach significantly improves narrative reproducibility and extensibility compared to conventional dashboards and slide-based reports, establishing it as a novel medium for collaborative KPI sensemaking.

Practitioners face significant challenges in effectively transforming customer feedback data into actionable software improvements. Method: This study proposes an end-to-end, data-driven improvement framework that systematically integrates feedback collection, multidimensional metric design, descriptive and inferential statistical analysis, interactive visualization dashboards (UX prototypes), and cross-departmental change-enabling mechanisms. Contribution/Results: The framework’s key innovation lies in the deep integration of statistical inference with user experience design, enabling a closed-loop feedback system for real-time insight generation and collaborative decision-making. Empirical evaluation demonstrates substantial improvements in feedback processing efficiency and response accuracy; product teams can rapidly identify high-priority enhancement opportunities using evidence-based insights. The results validate both the feasibility and practical efficacy of data-driven software evolution in industrial settings.

To address the challenges of fragmented engineering data and inefficient, inconsistent manual reporting in large-scale software development, this paper proposes and implements a centralized engineering productivity analytics framework supporting near-real-time aggregation and visualization. Methodologically, the framework introduces a dual-mode data storage architecture coupled with a precomputation engine, integrated with scheduled data ingestion (cron), proactive alerting, and role-based access control (RBAC). It unifies heterogeneous data from multiple source systems via a dual-schema design and leverages Metabase to enable cross-dimensional visual analytics—spanning development efficiency, software quality, and operational effectiveness. Empirical evaluation demonstrates that the deployed system reduces average weekly manual reporting effort by 20 person-hours, significantly accelerates bottleneck identification latency, and concurrently improves engineering decision responsiveness and platform scalability.

Current research on online behavioral change suffers from narrow behavioral coverage, overreliance on API-restricted platforms as data sources, and a persistent theory–empiricism gap. To address these limitations, this study conducts a systematic literature review of 148 peer-reviewed articles published between 2000 and 2023, constructing a four-dimensional knowledge graph encompassing behavioral categories, detection methodologies, platform ecosystems, and theoretical foundations. Our analysis uncovers three salient trends: (1) affective orientation dominates behavioral modeling; (2) platform distribution is heavily skewed toward a few API-constrained platforms; and (3) theoretical integration remains markedly underdeveloped. We propose a novel methodology framework—“Multi-behavioral Modeling, Heterogeneous Data Integration, and Theory–Practice Alignment”—and deliver a structured research map that precisely identifies critical gaps. This work advances the computational behavioral paradigm and offers an actionable methodological guide for digital social governance.

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 rapid adoption of AI by designers, which outpaces tool adaptation, by investigating its distribution patterns and barriers across design workflow stages. Employing the Double Diamond framework, we conducted a large-scale cross-national survey with 443 designers from 43 countries, analyzed through quantitative statistical methods. This work contributes the first stage-specific AI adoption map, distinguishing between "generation from scratch" and "in-software optimization" human-AI collaboration paradigms. Our findings reveal that AI usage peaks in early phases and declines progressively in later stages. Notably, 79.7% of designers already utilize AI tools, spanning an average of 2.89 stages, with perceived usefulness identified as the primary determinant of adoption.

This study investigates the barriers to adoption, developer perceptions, and the impact on productivity and code quality associated with generative AI tools in software development. Drawing on survey data from 147 professional developers and subsequent statistical analysis, the work proposes a “virtuous adoption cycle” model and identifies three distinct developer archetypes: enthusiasts, pragmatists, and cautious adopters. The findings reveal that organizational policies serve as maturity indicators rather than primary drivers in the diffusion process. Frequent and broad usage of AI tools is significantly associated with enhanced perceived productivity and code quality; however, lagging adoption of AI-powered testing tools creates a “testing gap.” Security concerns emerge as a moderately significant barrier to wider integration.

This study addresses the lack of empirical evidence on the real-world impact of Continuous Integration (CI) in mobile application development, particularly concerning app store performance. Leveraging a large-scale dataset of open-source Android projects, we systematically compare CI adopters and non-adopters through time-series analysis to assess CI’s effects on development activity, bug-fixing efficiency, release frequency, and user engagement metrics on Google Play—specifically downloads and review counts. Our findings reveal, for the first time, distinct adoption patterns of CI in the mobile ecosystem: CI is predominantly adopted by larger, more active projects in finance and productivity categories, which exhibit higher release frequencies and significantly greater downloads and reviews, while maintaining stable ratings. This work fills a critical gap in empirical research on CI effectiveness within mobile development contexts.

This study addresses a critical limitation of existing DORA metrics, which rely solely on first-order statistics and thus fail to capture the distributional characteristics of software release cadence or distinguish teams with markedly different release regularity. To overcome this, the work introduces second-order statistics into the DORA framework for the first time, proposing a novel Delivery Consistency (DC) metric based on the coefficient of variation of inter-release intervals. It further constructs an eight-prototype Delivery Health Matrix to enable multidimensional diagnosis and targeted intervention for software delivery rhythms across platforms. Validation using real-world data spanning 120 weeks from four platforms—including Jira, GitHub, and Firebase—demonstrates that the approach effectively identifies teams sharing identical DORA ratings yet exhibiting divergent release patterns, uncovering underlying organizational or process constraints common to such teams.