developer platforms

This study addresses core challenges in CI/CD practices for open-source Android applications—namely, configuration complexity, high maintenance overhead, and lack of cross-platform standardization. We conduct an empirical analysis of 2,557 GitHub projects utilizing GitHub Actions, Travis CI, CircleCI, and GitLab CI/CD, employing YAML configuration parsing, commit log topic modeling, and cross-platform comparative analysis. Key findings include: only 9% of projects achieve automated deployment; configurations are updated on average every two months, with over one-third of changes addressing build failures; 50% perform only basic testing; and maintenance intensity strongly correlates with project activity, size, and community engagement. We identify 11 recurring maintenance themes and highlight urgent needs for AI-driven automation tools and portable open-source solutions. This work provides empirical foundations and practical guidelines for standardizing CI/CD in mobile development.

This work addresses the challenges in edge and embedded application development—namely, heterogeneous software stacks, multi-language runtimes, and difficult debugging—which lead to rigid deployment workflows and complex fault diagnosis. To overcome these limitations, the paper proposes a novel architecture enabling unified end-edge-cloud development. Its core components include a single programming language, a retargetable runtime system, a local recording and replay mechanism for distributed events, and a cross-platform deployment framework. This design breaks down traditional debugging barriers in edge–cloud collaborative development, facilitating seamless scalability, consistent testing, and flexible deployment across heterogeneous environments. Evaluation of the prototype system demonstrates that the proposed approach significantly simplifies deployment procedures and enhances fault diagnosis efficiency.

This study identifies core barriers faced by novice developers when installing open-source development tools: ambiguous documentation, absence of system feedback, and compatibility uncertainty. Through empirical observation of 24 real-world installation sessions—complemented by behavioral coding, search log analysis, and comparison with unofficial documentation—we identify two effective coping strategies: “query reformulation” and “reliance on community documentation.” Our behavioral data provide the first empirical confirmation of a significant disconnect between official documentation and actual installation experience, as well as the lack of progressive, actionable feedback mechanisms in tool installers. Based on these findings, we propose a three-tiered set of actionable recommendations: (1) for tool vendors—improve documentation structure and error-message design; (2) for end users—introduce lightweight, context-sensitive guidance; and (3) for researchers—establish an installation usability evaluation framework. The work yields a practical, implementation-ready guideline for tool design and documentation optimization.

In the open-source CI ecosystem, tool functionality exhibits severe redundancy—approximately 65% of newly published CI Actions replicate existing capabilities within six months. Method: To address the extension decision problem for CI Actions on GitHub Marketplace, we propose a functional temporal graph modeling framework that integrates version history mining, dynamic graph construction, and time-aware clustering, yielding an evolutionary map covering 3,869 vendors and 6,983 tools. We innovatively define a “functional debut time” annotation mechanism to identify dominant propagation paths from pioneering tools to subsequent forks and quantify redundancy diffusion patterns. Contribution/Results: Our analysis reveals that functional replication is temporally concentrated in early phases and exhibits vendor convergence. The findings provide data-driven guidance for developers on optimal entry timing and strategic differentiation. We publicly release the complete dataset and evolutionary graph to support empirical research on software ecosystem innovation and competitive strategy.

Continuous Integration (CI) practices suffer from severe monitoring deficiencies: developers largely neglect critical metrics such as “build health” and “time-to-fix failed builds,” while mainstream CI services offer only weak native monitoring capabilities, forcing reliance on fragmented and often redundant third-party tools. Method: We conducted a triangulated investigation—including documentation analysis, developer surveys, functional audits of CI platforms, and case studies of open-source projects—to systematically identify cognitive gaps and practical monitoring needs. Contribution/Results: Our study provides the first empirical evidence that although over 80% of developers track test coverage, only a minority monitor build health or timeliness; further, all major CI services lack built-in multidimensional monitoring support. These findings establish an evidence-based foundation for designing next-generation CI monitoring frameworks and prioritizing tooling enhancements.

Bug fixing is a complex and time-consuming task in software development. Bug localization research tends to focus on the accuracy of automated tools that suggest source code files for developers to look at. However, little is known about how developers use these tools in practice. This paper reports on an ongoing qualitative user study. Eleven participants worked through four realistic bug localization tasks in a controlled environment and were given varying levels of support information offered by a specialized tool. Participants were asked to think aloud in a semi-structured interview session. The preliminary findings provide insight into three aspects of practice: how developers interact with tools, the role social and contextual information plays, and problem solving. The study demonstrates that bug localization is complex and suggests that the adoption of effective tools depends on more than their accuracy.

This study addresses the limitation of existing templates in specification-driven development, which fail to evaluate specification clarity and completeness. To overcome this, we propose EPIC, a framework grounded in the ISO/IEC/IEEE 29148 standard that conducts quantitative assessments of open-source repositories. By distilling an optimal specification taxonomy encompassing ten quality dimensions and forty practices, EPIC guides developers in clarifying expectations and bridging specification gaps. Empirical evaluations demonstrate that high-quality specifications reduce the proportion of bug-fixing commits to 11.8% and yield a fourfold increase in the median number of contributors. These findings indicate that adopting rigorous specification practices significantly enhances both collaborative efficiency and software quality in open-source projects.

This study addresses the lack of systematic understanding regarding how GitHub Actions workflows are used in real-world scenarios, how developers respond to workflow failures, and how these practices relate to project characteristics. Combining large-scale quantitative analysis of 258,300 workflow runs with qualitative case studies across 21 diverse repositories, this work identifies three typical patterns developers employ to handle workflow failures and uncovers a “configuration–usage gap”—where YAML configurations exist but workflows remain effectively unused. Furthermore, the study empirically validates five hypotheses linking project features to workflow usage intensity, revealing a significant positive correlation between high usage intensity and low failure rates. These findings provide actionable empirical evidence for improving CI/CD practices.

This study investigates the similarities and differences between DevOps specialists and general software developers with respect to tool usage, technical preferences, career stage, and work arrangements, leveraging the Stack Overflow 2023 Developer Survey dataset. Employing Python (Pandas) for large-scale data cleaning and statistical analysis, the research reveals substantial overlap in the adoption of critical tools such as Docker and Kubernetes. However, DevOps practitioners are predominantly mid-career professionals, whereas general developers tend to be younger. Both groups exhibit widespread adoption of remote and hybrid work models. By providing empirical insights into the evolving roles and collaborative dynamics between these two developer archetypes, this work addresses a notable gap in the literature and underscores their complementary relationship and growing synergy within modern software development ecosystems.

While large language models (LLMs) can generate executable multi-service application environments, they often deviate from the architectural and security requirements essential for production deployment. This work proposes a method to automatically generate Dockerfiles and Docker Compose configurations solely from code repositories, evaluating deployment fidelity through end-to-end HTTP testing and structural comparison. It explicitly distinguishes between functional correctness and fidelity to deployment intent, deriving a minimal set of explicit deployment specifications that cannot be inferred automatically from source code alone. Experiments successfully reproduce the topology and dependencies of three heterogeneous multi-service systems, confirming functional feasibility; however, critical production-grade features—such as network isolation and multi-stage builds—are consistently absent, revealing fundamental limitations in current LLMs’ ability to model deployment intent.