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Abstract
Reproducibility in computational science is increasingly dependent on the ability to faithfully re-execute experiments involving code, data, and software environments. However, assessing the effectiveness of reproducibility tools is difficult due to the lack of standardized benchmarks. To address this, we collected 38 computational experiments from diverse scientific domains and attempted to reproduce each using 8 different reproducibility tools. From this initial pool, we identified 18 experiments that could be successfully reproduced using at least one tool. These experiments form our curated benchmark dataset, which we release along with reproducibility packages to support ongoing evaluation efforts. This article introduces the curated dataset, incorporating details about software dependencies, execution steps, and configurations necessary for accurate reproduction. The dataset is structured to reflect diverse computational requirements and methodologies, ranging from simple scripts to complex, multi-language workflows, ensuring it presents the wide range of challenges researchers face in reproducing computational studies. It provides a universal benchmark by establishing a standardized dataset for objectively evaluating and comparing the effectiveness of reproducibility tools. Each experiment included in the dataset is carefully documented to ensure ease of use. We added clear instructions following a standard, so each experiment has the same kind of instructions, making it easier for researchers to run each of them with their own reproducibility tool.The utility of the dataset is demonstrated through extensive evaluations using multiple reproducibility tools.

Abstract
User studies are paramount for advancing research in software engineering, particularly when evaluating tools and techniques involving programmers. However, researchers face several barriers when performing them despite the existence of supporting tools. We base our study on a set of tools and researcher-reported barriers identified in prior work on user studies in software engineering. In this work, we study how existing tools and their features cope with previously identified barriers. Moreover, we propose new features for the barriers that lack support. We validated our proposal with 102 researchers, achieving statistically significant positive support for all but one feature. We study the current gap between tools and barriers, using features as the bridge. We show there is a significant lack of support for several barriers, as some have no single tool to support them.

Abstract
Supporting sustainability through modelling and analysis has become an active area of research in Software Engineering. Therefore, it is important and timely to survey the current state of the art in sustainability in Cyber-Physical Systems (CPS), one of the most rapidly evolving classes of complex software systems. This work presents the findings of a Systematic Mapping Study (SMS) that aims to identify key primary studies reporting on CPS modelling approaches that address sustainability over the last 10 years. Our literature search retrieved 2209 papers, of which 104 primary studies were deemed relevant fora detailed characterisation. These studies were analysed based on nine research questions designed to extract information on sustainability attributes, methods, models/meta-models, metrics, processes, and tools used to improve the sustainability of CPS. These questions also aimed to gather data on domain-specific modelling approaches and relevant application domains. The final results report findings for each of our questions, highlight interesting correlations among them, and identify literature gaps worth investigating in the near future.

Abstract
In the realm of industrial software development, DevOps has emerged as the preferred approach for handling the highly iterative software production process. DevOps refers to the tight integration of development and operations activities, with Continuous Integration, Continuous Delivery, and Continuous Deployment (CI/CD) being pivotal methodologies for ensuring the iterative delivery of high-quality software. To achieve CI/CD, pipelines of activities are deployed using commercial tools. Due to the dynamic nature of these tools, CI/CD pipelines are often migrated to new versions or even new tools. Since this is mostly a manual process, it is a cumbersome and error-prone activity. To assist software engineers during this process, we propose a novel approach that leverages model-driven engineering (MDE) to support the migration of CI/CD pipelines. Our approach is inspired by the traditional reengineering horseshoe model, which abstracts existing pipeline artifacts into a comprehensive model as an intermediate representation. From these models, we can then generate semantic-equivalent pipelines for any novel CI/CD tool. Thus, our main contribution comprises a metamodel designed to represent the structure of existing CI/CD pipelines and build the foundation for MDE-based migration of CI/CD pipelines. We validated our metamodel by successfully modeling 400 existing pipelines. This evaluation demonstrated a 100% applicability rate when applied to configuration files from technologies that collectively account for over 92% of CI/CD scripts in use. Furthermore, we conducted a detailed case study demonstrating the practical applicability of our approach in real-world migration scenarios. Finally, we demonstrate that our metamodel promotes equivalence between an original pipeline and a new one generated from it in a different technology by showing through test cases that the execution traces of both pipelines are identical. © 2025 IEEE.

Abstract