software engineering

This study addresses the widespread lack of computational reproducibility in R supplementary code deposited on the Open Science Framework (OSF). A systematic audit of 296 published R code packages revealed that 98.8% incompletely declare dependencies. To address this, we propose the first automated reproducibility auditing framework tailored to the R ecosystem. It combines static source-code analysis—leveraging regular expressions and abstract syntax trees (ASTs)—to accurately infer dependencies, with Docker-based containerized execution and failure diagnostics (e.g., path errors, OS-specific inconsistencies, missing packages) to enable end-to-end environment reconstruction and validation. Experiments successfully executed 25.87% of scripts, identifying undeclared dependencies, hardcoded file paths, and cross-platform compatibility issues as the three primary barriers to reproducibility. The framework enables large-scale, low-cost, and scalable quantitative assessment of computational reproducibility in scholarly research, providing a practical toolchain to enhance transparency and verifiability.

Addressing challenges in FAIR principle implementation—including fragmented data and code lifecycles, lack of executable environments, and high technical barriers—this study proposes a unified open-science platform. The platform uniquely integrates version control, containerized computational environments, and modular project scaffolding to support end-to-end reproducible research, from grant proposal to publication. It interoperates with mainstream scientific toolchains, supports deployment on both local workstations and institutional servers, and provides a lightweight graphical user interface. Empirical validation demonstrates successful re-execution of over a dozen interdisciplinary studies published more than ten years ago, confirming the platform’s robust long-term reproducibility, cross-platform compatibility, and seamless execution across diverse domains. By significantly lowering technical adoption barriers for researchers, the platform enables practical integration of FAIR principles and reproducibility practices into routine scientific workflows.

Scientific software often suffers from poor reproducibility and low sharing rates, primarily because researchers lack formal software engineering training—leading to version chaos, uncontrolled code quality, and cumbersome release processes. To address this, scikit-package introduces a progressive software engineering roadmap tailored for domain scientists. It provides standardized packaging tutorials, automated workflow templates (covering build systems, CI/CD pipelines, documentation generation, and package management), and community-maintained pedagogical resources. Its key innovation lies in adapting professional software engineering practices to the cognitive load of non-specialist programmers, enabling systematic progression from script-based functions to production-ready, open-source package releases. Empirical evaluation demonstrates that scikit-package significantly improves the reproducibility and maintainability of scientific code, enhances community sharing efficiency, and lowers barriers to standardized scientific software publication.

This work addresses the inefficiency and steep learning curve researchers often encounter when trying to map academic papers to their corresponding implementation code. To bridge this gap, the authors propose an automated tool powered by large language models (LLMs) that achieves cross-modal semantic alignment between scholarly texts and source code for the first time. By integrating program analysis techniques, the method automatically identifies code segments that implement specific research ideas described in a paper and generates high-quality traceability mappings. This approach substantially reduces the manual effort required for alignment, enhances the comprehensibility of research software, and improves reproducibility. Preliminary experiments demonstrate the tool’s practicality and effectiveness in real-world scenarios.

Doctoral students in life sciences commonly lack formal software engineering training, hindering the development of robust, reproducible, and collaborative research software. Method: This study proposes ten pedagogical principles for research software development, establishing the first systematic framework centered on “research software pedagogy”—distinct from generic programming instruction. It integrates software engineering best practices (e.g., Git-based version control, CI/CD pipelines, unit testing, RESTful API design), learning science principles, and authentic research workflows, emphasizing the seamless embedding of automation, documentation, testing, and collaborative practices throughout the research lifecycle. Contribution/Results: The framework delivers a generalizable, plug-and-play pedagogical paradigm. Deployed across multiple Chinese universities’ life sciences PhD programs, it has demonstrably improved software deliverable quality, code reusability, and cross-team collaboration efficiency—bridging critical gaps between computational literacy and rigorous, team-based scientific software practice.

This work addresses the frequent neglect of sampling strategy design and generalizability in software engineering research, which often undermines the representativeness of empirical findings. To remedy this, the paper introduces a domain-specific language (DSL) that explicitly models complex sampling workflows over code repositories through composable sampling operators, enabling—for the first time—formal specification and reasoning about the generalizability of sampling strategies. Implemented as a fluent Python API, the DSL is integrated with a statistical metric system to quantitatively assess the external validity of sampled datasets. The authors demonstrate the expressiveness and practical utility of their approach by reconstructing and formalizing the sampling procedures from multiple Mining Software Repositories (MSR) studies, thereby validating the framework’s capacity to capture real-world methodological diversity.

Automatically reproducing executable bug-fix code pairs from unstructured developer Q&A posts is hindered by ambiguous descriptions and missing dependencies. This work proposes Reprodgen, the first end-to-end automated framework that leverages large language models to jointly model code intent (CI), functional requirements (FR), and structured chains of thought (SCoT) to generate semantically consistent and executable bug-fix code pairs. The approach incorporates an LLM-based iterative review mechanism coupled with real execution validation to ensure correctness. Evaluated on Stack Overflow and GitHub Issues across seven widely used data science libraries, the study introduces the first expert-validated, runnable benchmark of bug-fix pairs. Experimental results demonstrate that Reprodgen reliably reproduces code pairs exhibiting clear behavioral differences between buggy and fixed versions.

This work proposes ReproScore, a novel framework that explicitly decouples reproducibility readiness (RRS) from reproducibility outcomes (ROS)—a distinction often conflated in existing tools that mistakenly treat static repository completeness as a proxy for successful execution. RRS comprises 26 fine-grained submetrics assessing code and documentation quality, while ROS is derived through sandboxed execution probes. The two dimensions are integrated into an adaptive composite score (RCS), with customizable metric weights via versioned YAML configurations contributed by the community. Empirical evaluation across 423 GitHub repositories spanning five failure modes demonstrates that environment-related metrics effectively discriminate failure types, yet reveals a near-zero correlation between RRS and actual execution success—exposing a critical “readiness–outcome gap” and thereby validating the necessity and efficacy of the proposed architectural separation.

This work addresses the widespread irreproducibility of academic Jupyter Notebooks caused by environment drift, missing dependencies, and implicit execution assumptions. The authors propose the first web-oriented, automated reproducibility engineering pipeline that systematically reconstructs and evaluates repository-level execution environments for notebooks hosted on GitHub. By leveraging dependency inference, auto-generated Docker containers, and isolated execution, the pipeline enables large-scale assessment of reproducibility. A novel four-category execution outcome framework is introduced to quantify reproduction fidelity. Evaluation on 443 real-world notebooks shows that containerization resolves 66.7% of dependency-related failures; however, only 46.3% achieve high output fidelity, demonstrating that while containerization is necessary, it is insufficient for bit-for-bit reproducibility. These findings underscore the critical need for systematic reproducibility evaluation in computational research.

This work proposes an AI agent–driven workflow to address the high costs of reproducing large-scale empirical studies, which often stem from discrepancies in computational environments, code, and documentation. The approach decouples scientific reasoning from computational execution: researchers supply standardized diagnostic templates, and the system automatically retrieves and orchestrates reproduction materials within a version-controlled environment. A structured knowledge layer captures failure patterns, enabling adaptive reproduction across heterogeneous studies while ensuring transparency and stability of the analytical pipeline. Evaluated on 92 instrumental variable studies, the method achieves an 87% end-to-end reproduction success rate; when data and code are available, it attains 100% success at both the paper and model levels.