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Automatic Quality User Story Artisan Prototype - Backend

This is an archived implementation of the AQUSA tool described in http://bit.ly/aqusa-paper

Visit https://github.com/RELabUU/aqusa-core for a more up to date version of the core of the AQUSA algorithm.

Installation

  • Tested with Python 3.4
  • Install Flask
  • Install libraries using pip install -r requirements.txt
  • Create a database
  • run migrations: ./manage.py db migrate && ./manage.py db upgrade. You might need to delete the migrations in /migrations/versions first.
  • Install NLTK prerequisite 'Punkt Tokenizer' by running nltk.download in the Python interactive shell.
  • Run the translations with ./manage.py translate. This will throw an error, but this is not a problem.
  • Test if the application works by running nosetests
  • Run server by executing ./run.py
  • Run shell by executing ./shell.py

Instructions for installing the stanford dependency

  • Download the stanford POStagger from
  • Move the files stanford-postagger-withModel.jar and english-left3words-distsim.tagger to this folder

Usage

This is the backend of this application, exposing a simple API to be used by front end applications such as a Ruby on Rails web front-end or an iOS mobile client.

  • POST to /unique_string/project/new_story
  • GET stories from /unique_string/project/stories
  • GET report from /unique_string/project/report

As a demo, you can browse to '/unique_string/project/upload_file' and upload a simple CSV. The report page also serves a simple HTML view.

Code Improvements

Note

This is a prototype application that's still quite difficult to figure out. Future versions will be much improved.