Abstract
Continuous Integration and Continuous Deployment (CI/CD) pipelines play a vital role in the DevOps process, enabling developers to automate and enhance software delivery. However, the existence of multiple technologies, such as GitHub Actions, GitLab CI/CD, or Jenkins, poses challenges due to their lack of interoperability and the use of different programming languages for pipeline construction. To address these challenges and improve the CI/CD process, our objective is to develop a block-based language specifically designed for representing CI/CD pipelines. With our language, we intend to empower users to more easily create correct pipelines. Through an interactive and user-friendly process, our approach guides users in constructing pipelines, ensuring accuracy and reducing errors. Additionally, our language will facilitate seamless transitions between different pipeline technologies, providing users with flexibility and ease of adoption.

Abstract
In recent years, the research community has raised serious questions about the replicability and reproducibility of scientific work. In particular, since many studies include some kind of computing work, these are also technological challenges, not only in computer science but in most research domains. Replicability and reproducibility are not easy to achieve, not only because researchers have diverse proficiency in computing technologies, but also because of the variety of computational environments that can be used. Indeed, it is challenging to recreate the same environment using the same frameworks, code, programming languages, dependencies, and so on. In this work, we propose a vision for an Integrated Development Environment allowing the creation, configuration, execution, packaging, and sharing of scientific computational experiments. Such a framework should allow researchers to easily set the code and data used and define the programming languages, code, dependencies, databases, or commands to execute to achieve consistent results for each experiment. With this work, we intend to aid researchers by integrating into the same platform all the stages of the design, execution, and analysis of a computational experiment.

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Abstract
Ensuring the reproducibility of computational scientific experiments is crucial for advancing research and fostering scientific integrity. However, achieving reproducibility poses significant challenges, particularly in the absence of appropriate software tools to help. This paper addresses this issue by comparing existing tools designed to assist researchers across various fields in achieving reproducibility in their work. We were able to successfully run eight tools and execute them to reproduce three existing experiments from different domains. Our findings show the critical role of technical choices in shaping the capabilities of these tools for reproducibility efforts. By evaluating these tools for replicating experiments, we contribute insights into the current landscape of reproducibility support in scientific research. Our analysis offers guidance for researchers seeking appropriate tools to enhance the reproducibility of their experiments, highlighting the importance of informed technical decisions in facilitating reproducibility across diverse domains.

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