Bug Tracking System

This repository contains the second part of a three part assignment of a Computer Science MSc module at the University of Essex.

Table of contents

• Description
• Installing and running the app
• Testing the app
• Functionality overview
• References

Description

A Bug Tracking System (BTS) is an issue tracking system that is used to manage defects that may arise during the software development life cycle. Using a BTS, software defects are logged and tracked by both the end users and developers of software applications.

This repository contains a minimum viable product of a Flask based BTS.

• The front end interface was built using Bootstrap and Jinja
• The back end validation is implemented using WTForms for Flask
• The testing functionality was implemented using pytest

Installing and running the app

Codio IDE (Ubuntu 18.04.3 LTS)

1. Clone the repository:

$ git clone https://github.com/tkachikoti/bug_tracking_system.git

2. Change directory:

$ cd bug_tracking_system

3. Configure the environment and install dependencies:

$ sudo apt-get update
$ sudo apt-get install python3-venv
$ python3 -m venv venv
$ source venv/bin/activate
$ pip install -U flask
$ pip install -U Flask-WTF
$ pip install -U pytest
$ pip install -U numpy

4. Run the app:

$ python app.py

5. On the menu bar, click on the downward pointing chevron to open the preview menu. Please ensure that the 'Box URL' and the 'New Browser Tab' options are selected.

6. Click the 'Box URL' button to open a browser tab running the app.

Please refer to Codio's documentation to resolve any issues: https://docs.codio.com/common/develop/ide/editing/preview.html

Windows and macOS

1. Download and install Python: https://www.python.org/downloads/

2. Clone the repository:

$ git clone https://github.com/tkachikoti/bug_tracking_system.git

3. Change directory:

$ cd bug_tracking_system

4. Configure the environment and install dependencies:

$ python3 -m venv venv
$ source venv/bin/activate
$ pip install -U flask
$ pip install -U Flask-WTF
$ pip install -U pytest
$ pip install -U numpy

5. Run the app:

$ python app.py

6. Open http://192.168.1.12:3000/ in a browser.

Testing the app

1. After following the relevant installation process, tests are executed from the root directory by entering:

$ pytest

Functionality overview

1. Create/Update Ticket

Users can create or update a ticket by interacting with the form and clicking the button labelled 'CREATE/UPDATE TICKET'. The form includes front end validation to ensure all fields contain data.

Pseudo-code:

• Check if the form contains a key labelled 'component-name'
• Validate form data
• If form data is valid persist data in storage otherwise return to index page

2. View/Delete Ticket

Users can view or delete a ticket by referencing the unique identification number (uid) that is assigned to each ticket upon creation. A prompted appears on screen requesting users to confirm the deletion of a ticket prior to the execution of the command. This safeguards against the accidental deletion of a ticket.

Pseudo-code:

• Check if the form contains a key labelled 'uid'
• Retrieve all data about tickets from persistent storage
• Find ticket data with a 'uid' that matches the one received via GET/POST request form
• Return ticket data

3. Search Ticket

Users can search for a ticket by filling a form on the search page. The search algorithm uses cosine similarity to find the ticket(s) which closely resemble the string that is received via search form. (Budiman et al, 2017)

Pseudo-code:

• Check if the form contains a key labelled 'search_value'
• Retrieve all data about tickets from persistent storage
• Iterate through the list of tickets checking the cosine similarity between the ticket data and the 'search_value'
• If the cosine similarity is greater than zero the ticket data is appended to the 'search_results' list
• 'search_results' list is sorted in descending order (with respects to the similarity score)
• 'search results' are returned

References

Budiman, M., Gunawan, D. & Sembiring, C. (2017) 'The Implementation of Cosine Similarity to Calculate Text Relevance between Two Documents', Journal of Physics: Conference Series. Medan, Indonesia, 28–30 November. Medan: IOP Publishing Ltd.