Relevant Factors of Requirements Quality: Replication Package

This repository contains the replication package for the case study on identifying relevant factors of requirements quality. In this study, we used the requirements quality theory [1] as a frame to identify which quality and context factors [2] are relevant to the specific company case. This repository contains the (anonymized) data sets, scripts to analyze them, and figures to visualize them.

Contributors and Article Information

Name	Affiliation	Email
Julian Frattini	Blekinge Institute of Technology, Sweden	julian.frattini@bth.se

Cite this work as follows: Frattini, J. (2024). Identifying relevant Factors of Requirements Quality: an industrial Case Study. In Requirements Engineering: Foundation for Software Quality: 30th International Working Conference, REFSQ 2024, Winterthur, Switzerland, April 8-11, 2024, Proceedings 30. Springer International Publishing.

@inproceedings{frattini2024identifying,
  title={Identifying relevant Factors of Requirements Quality: an industrial Case Study},
  author={Frattini, Julian},
  booktitle={Requirements Engineering: Foundation for Software Quality: 30th International Working Conference, REFSQ 2024, Winterthur, Switzerland, April 8--11, 2024, Proceedings 30},
  year={2024},
  organization={Springer}
}

Description of the Artifacts

This repository contains the following artifacts:

• data/ : folder for all data-related information
  • columns.json : list of relevant columns and available values per column
  • data-description.md : further explanation of the data (i.e., sheets, values, etc.)
  • interview-data.xlsx : Excel sheet containing both the original and overlap codes assigned to the extracted interview statements
  • issue-data.xlsx : Excel sheet containing the codes assigned to issues
• doc/ : folder containing all additional documentation and supplementary material
  • Interview Coding Guideline : guidelines for coding the interview transcripts
  • Interview Protocol : protocol that guided the execution of the interviews
  • Issue Coding Guidelines : guidelines for coding the issues
  • Visual Aid : visual aid used during the interview to illustrate the RQT [1]
• figures/ : folder for all figures
  • graphml/ : figures in editable .graphml format
    • activity-model.graphml : tree of activities and attributes
    • interaction-example.graphml : mapping a statement describing an interaction effect to the requirements quality theory
    • method-visualization.graphml : visual overview of the data collection and analysis method
    • requirements-quality-theory.graphml : simplified version of the requirements quality theory [1]
  • pdf/ : the same figures but in viewable .pdf format
• src/ : folder containing all source code
  • analytics/ : folder for all source code related to analysis
    • evaluation.ipynb : notebook to filter and aggregate the data to themes
    • interrater-agreement : notebook to calculate the agreement between two independent raters coding the interview data
    • results.md : summary of the interview code results
  • exploration.ipynb : notebook to further explore the data set
  • requirements.txt : list of python libraries required to execute the source code

This repository does not contain verbatim interview or issue data for privacy reasons.

System Requirements

The following requirements must be met in order to utilize the artifacts contained in this repository:

• To utilize the data, you need a spreadsheet software like Microsoft Excel to open the .xlsx file.
• To exectute the code, Python 3.10 must be available and an editor software like Visual Studio Code is recommended.
• To edit the figures, you require an editor capable of opening Graph Markup Language (.graphml) files, for example the yEd Graph Editor.

Installation Instructions

To execute the code contained in this repository, make sure all requirements contained in the requirements.txt are installed by executing pip install -r requirements.txt. To avoid conflicts with the local Python environment, create a separate virtual environment:

1. Create a virtual environment with python -m venv .venv
2. Activate the virtual environment with ./venv/Scripts/activate
3. Install the requirements as described above.
4. Install the ipykernel either by selecting the virtual environment .venv as the runtime environment for the Jupyter notebook in VS Code (which will trigger VS Code to automatically install the ipykernel) or install it manually.

Once the virtual environment is running and all requirements installed and ipykernel are installed, the Jupyter notebook can be executed from the virtual environment.

Steps to Reproduce

Execute the code blocks of the following Jupyter notebooks from top to bottom to reproduce the results from the manuscript:

• interrateragreement.ipynb: calculation of the inter-rater agreement between the author and the independent rater
• evaluation.ipynb: filtering of the raw data and aggregation of the data into larger, more meaningful units

Additionally, you can use the file exploration.ipynb to explore the data further and in more depth.

References

[1] Frattini, J., Montgomery, L., Fischbach, J., Mendez, D., Fucci, D., & Unterkalmsteiner, M. (2023). Requirements quality research: a harmonized theory, evaluation, and roadmap. Requirements Engineering, 1-14. https://doi.org/10.1007/s00766-023-00405-y

[2] Femmer, H., Mund, J., & Fernández, D. M. (2015, May). It's the activities, stupid! a new perspective on RE quality. In 2015 IEEE/ACM 2nd International Workshop on Requirements Engineering and Testing (pp. 13-19). IEEE. https://doi.org/10.1109/RE54965.2022.00041

Licensing

Copyright © 2023 Julian Frattini. This work is licensed under MIT License.