OpenReq - Improving Requirements Quality

This component was created as a result of the OpenReq project funded by the European Union Horizon 2020 Research and Innovation programme under grant agreement No 732463.

Technical Description

This stateless microservice checks requirements for quality issues. These quality issues include syntactic and semantic issues in the wording and phrasing of the requirements. The algorithms utilize simple NLP techniques to reach their goals. This allows the microservice to be stateless and offer quick response times.

The algorithms implemented are from existing research, as well as original research. The structure of the code allows interested parties to discover not only which work is from existing work, but also how to find the original work.

Technologies Used

• Docker (-> https://www.docker.com/)

How to Install

This microservice is Dockerized. With Docker installed and running on your machine, run the following commands to build and run the service:

1. docker build --no-cache -t prs-improving-requirements-quality .
   1. OR docker build -t prs-improving-requirements-quality . to cache
2. docker run -p 9799:9799 prs-improving-requirements-quality

How to Use This Microservice

This microservice takes a number of textual requirements as input, and returns a number of noted issues with the requirements quality. For detailed information on the endpoint(s), data format, and examples, see the Swagger file included in this repository, or the hosted version: api.openreq.eu.

See "How to Install" above to run the service, followed by using a REST client to send requests.

For simplicity and due to limitations in Swagger representation, the optional JSON field "config" is not listed in the Swagger documentation. See below for an example JSON object passed to the API, with the optional config field being used.

{
  "requirements": [
    {
      "id": "1",
      "text": "The system may respond within 5 seconds."
    }
  ],
  "config": {
    "algorithms": ["Lexical", "RegularExpressions", "POSRegularExpressions", "CompoundNouns", "Nominalization"]
  }
}

Note: The above example uses the optional field "config" as well as the option sub-field "algorithms". When you pass these two optional fields, only the algorithms listed in the array "algorithms" will be run. In the example above, all possible algorithms have been listed, so this configuration is not restricting the API in any way. To reduce the number of algorithms run (and therefore the results returned), remove algorithms from the "algorithms" array above. If you want all algorithms to run, simply remove the "config" field altogether.

Notes for Developers

Updating requirements.txt

Run the following command to update the requirements.txt before pushing to GIT:

1. venv/bin/pip freeze > requirements.txt
2. Remove en-core-web-sm==2.0.0 from requirements.txt

NOTE: If you run pip freeze > requirements.txt from a normal terminal, you will include all global packages, creating a bloated file. Please only use this command from a virtual environment where you are certain of the packages.

Running Tests

To run the tests:

1. Navigate to the root folder
2. Run python test_all.py

Generating and Viewing Test Coverage Reports

Run the following commands to generate test-coverage files. The XML is necessary for certain quality tools, and the HTML files can be used to view the results in a human-readable format.

1. .coveragerc file contains configurations for test coverage output. Edit as necessary.
2. Ensure that pip install -r requirements.txt has already been run, as it contains the packages necessary for the code to run as well as the test coverage commands
3. Run pytest --cov=. --cov-report={xml,html} --cov-config=.coveragerc test_all.py in the root folder to generate the test coverage files
4. Open coverage-reports/html/index.html to view the results

Available Demos

csv - Ambiguity Counts: This is a demo package for a webpage that loads a CSV, and runs each requirement through the API. Producing a count of ambiguities found.

1. Prepare a CSV file with a single column, where each row is a requirement
2. Upload that CSV using the button provided by this demo

csv - Visualisation: This is a demo package for a webpage that loads a CSV, and runs each requirement through the API. It they displays the output visually as highlighted smells.

1. Prepare a CSV file with a single column, where each row is a requirement
2. If you wish, separating sets of requirements with an empty line causes some visual formatting with the output
3. Upload that CSV using the button provided by this demo

iFrame Functionality: NOT CURRENTLY FUNCTIONAL, ALTHOUGH FIXABLE It is possible to query the functioning API in your web browser, with URL parameters, and it will act as a stand-alone page. Instructions on Use:

1. Start the application locally using Docker and the above commands
2. Open the following your browser: http://0.0.0.0:9799/index?requirementTitle=Requirement%20Title&requirementText=This%20is%20not%20a%20good%20requirement.
3. Alter the URL Parameters as you want for different Titles and Text passed to the API

Sources

Literature used in creating some of the algorithms (details can be found in the repository)

• Fleta MB. Technical Communication: Complex Nominals Used to Express new Concepts in Scientific English-Causes and Ambiguity in Meaning. ESPecialist. 1996;17(1):57-72.
• Landhaußer M, Korner SJ, Tichy WF, Keim J, Krisch J. DeNom: a tool to find problematic nominalizations using NLP. In Artificial Intelligence for Requirements Engineering (AIRE), 2015 IEEE Second International Workshop on 2015 Aug 24 (pp. 1-8). IEEE.
• Yang H, Willis A, De Roeck A, Nuseibeh B. Automatic detection of nocuous coordination ambiguities in natural language requirements. InProceedings of the IEEE/ACM international conference on Automated software engineering 2010 Sep 20 (pp. 53-62). ACM.
• Femmer H, Fernández DM, Juergens E, Klose M, Zimmer I, Zimmer J. Rapid requirements checks with requirements smells: Two case studies. InProceedings of the 1st International Workshop on Rapid Continuous Software Engineering 2014 Jun 3 (pp. 10-19). ACM.
• Xu K, Liao SS, Li J, Song Y. Mining comparative opinions from customer reviews for Competitive Intelligence. Decision support systems. 2011 Mar 1;50(4):743-54.
• Gleich B, Creighton O, Kof L. Ambiguity detection: Towards a tool explaining ambiguity sources. InInternational Working Conference on Requirements Engineering: Foundation for Software Quality 2010 Jun 30 (pp. 218-232). Springer, Berlin, Heidelberg.
• Tjong SF, Berry DM. The design of SREE—a prototype potential ambiguity finder for requirements specifications and lessons learned. InInternational Working Conference on Requirements Engineering: Foundation for Software Quality 2013 Apr 8 (pp. 80-95). Springer, Berlin, Heidelberg.

How to contribute

See OpenReq project contribution Contribution Guidelines

License

Free use of this software is granted under the terms of the EPL version 2 (EPL2.0).