Abstract
Continuous Integration and Deployment (CI/CD) pipelines are essential for modern software delivery, yet configuring them remains a challenge due to the complexity of text-based formats like YAML. These configurations are error-prone and require substantial expertise, posing a barrier to novices. In this paper, we introduce PipeBlocks, a block-based CI/CD tool featuring a graphical interface for visually constructing pipelines through modular, drag-and-drop blocks. PipeBlocks seamlessly integrates with GitHub Actions, allowing users to design, validate, and execute pipelines entirely within the tool while maintaining full compatibility with existing YAML workflows. A key innovation is the ability to trigger and monitor pipeline runs directly in PipeBlocks, eliminating context-switching. We evaluated PipeBlocks through a controlled study with 10 participants configuring identical pipelines (build, test, deploy) using both PipeBlocks and YAML editing in GitHub Actions. Using the System Usability Scale (SUS) and NASA-TLX benchmarks, we found that PipeBlocks achieved a statistically significantly higher usability score and a lower cognitive load. The results suggest that block-based approaches can effectively lower CI/CD's learning curve while maintaining functional completeness, making them particularly valuable for academic settings and early-career developers building CI/CD competencies. © 2025 IEEE.

Abstract
Continuous Integration and Deployment (CI/CD) pipelines are essential for modern software delivery, yet configuring them remains a challenge due to the complexity of textbased formats like YAML. These configurations are error-prone and require substantial expertise, posing a barrier especially to novices. In this paper, we introduce PipeBlocks, a block-based CI/CD tool featuring a graphical interface for visually constructing pipelines through modular, drag-and-drop blocks. PipeBlocks seamlessly integrates with GitHub Actions, allowing users to design, validate, and execute pipelines entirely within the tool while maintaining full compatibility with existing YAML workflows. A key innovation is the ability to trigger and monitor pipeline runs directly in PipeBlocks, eliminating context-switching. © 2025 IEEE.

Abstract
Computational reproducibility remains a critical yet unresolved issue across scientific disciplines, often hindered by complex configuration requirements and technical barriers. We present SciConv, a novel conversational tool designed to assist researchers in creating and executing reproducible computational experiments using natural language. By leveraging large language models (llMs), SciConv automates the detection of dependencies and programming languages, and packages experiments into portable artifacts with minimal manual input. Unlike traditional platforms based on graphical user interfaces (e.g., web-based), SciConv features a chat-based interface that guides researchers interactively through the reproducibility workflow. This paper introduces the architecture, design principles, and interaction model of SciConv, and discusses its potential to lower the technical barriers to reproducibility. © 2025 IEEE.