PipeBlocks: A Block-based Environment for CI/CD

📦 Repository Package: PipeBlocks A Block-based Environment for CI/CD This repository accompanies our study on PipeBlocks, a block-based programming environment designed for Continuous Integration and Continuous Deployment (CI/CD). Inside reproducibility.zip, you'll find all the components necessary to reproduce the environment, datasets, and analytical results from our study. 📁 Folder Structure Overview 1. application/ Contains the full source code for the PipeBlocks environment. Install dependencies: yarn install Run the environment: npm run dev 2. metamodel2blockly/ Python script to transform a given .ecore metamodel into initial Blockly blocks for the PipeBlocks editor. Install dependencies: pip install -r requirements.txt Run the parser: python metamodel2blockly.py your_metamodel.ecore 3. visualcicdstudy/ Contains the configuration files used during the study. These files define the CI/CD workflows, experiment setups, and environment settings as applied in the experimental tasks. 4. blocklytestsw/ Includes the JSON specifications used to configure Blockly blocks. These files define the available blocks and their properties as used in the PipeBlocks environment. 5. datasetsvisualcicd/ Holds the datasets collected during the study, including: Pre- and post-study questionnaires System Usability Scale (SUS) responses NASA Task Load Index (NASA-TLX) responses Repository metadata 6. repositories/ An anonymized snapshot of repositories created by participants during the study sessions. 7. statscalculating/ Python scripts for statistical analysis of the collected data. Install dependencies: pip install -r requirements.txt Run individual analysis scripts: NASA-TLX Analysis: python nasa.py # General analysis python nasatlxpercomponent.py # By component python nasatlxpergroup.py # By group python nasatlxperuser.py # By user SUS (System Usability Scale) Analysis: python sus.py # General analysis python susgroup.py # By group python susperindividual.py # By user Pre-questionnaire Analysis: python prequestionnaire.py 8. graphs/ Includes visual representations and Vega-Lite JSON specifications used to generate the graphs presented in the study.