Roadmap Changelog
main features and milestones
The main features of each milestones are also listed below.
completed: April 27th 2020
- Setup a GitHub Organization, Repositories, and Wiki
- Create User Stories and Issues and Integrate on Kanban
- Setup Github Actions for Docker Build
- Setup Github Actions for Deployment to Heroku
- Integrate SonarCloud with Repositories
- Create NPM Packages for Shared Code Style
- Create Issue and Pull Request Templates
- Setup Mandatory Code Review and Status Checks (before Merge)
- Filter unwanted or unfitting movies from “The Movies Dataset”
- Segment movies by release year (0-1960, 1960-1990, 1990-2020)
- Seed the database with an excerpt of the resulting list of movies
- Return a list of N random movies
- Filter the above list according to initial criteria (different from ImdbID X, release year X)
- For a given ImdbID, return the poster path as queried from OMDB
- Specify size of poster with size url parameter
- Design and Implement GraphQL schema
- Setup connection to the Metadata Service
- Setup connection to the Poster Service
- Provide a random movie with metadata and poster url
- Provide multiple random movies to generate a question
- Compute the score of a submitted solution
- Welcome Screen
- Basic Routing
- Movie Guessing
- Trivia Screen
- Scoring System
completed: May 22nd 2020
- Setup GitHub Pages website
- Bonus Question Screen
- Consistent Design
- Track streak: how many movies guessed correctly in a row
- Bonus Question scoring
- seed additional movies
While we have finished an initial v1.0.0 of Kwiz, there are many ideas left to be implemented. We have added ideas for future work to a v2.0.0 milestone and dedicate this space to shortly introduce the most prominent ones.
Related Movies
Computing questions based on a set of related movies, rather than showing the movie in question alongside three randomly chosen movies, would provide the foundation for many other features (some of which are also described here).
Additional Question Types
Optionally, in the future, the current state of the game can be extended with additional question types by combining the existing dataset with more external datasets and API resources.
Such question types could allow for guessing...
- … the overall rating of a movie.
- … if a particular actor played in a movie.
- … how much the production of a movie cost.
Multiplayer Mode
- … a “multiplayer mode” with online scoreboards and playing against friends
Game Parameters
- … varying levels of difficulty, either by choice or automatic increase
- ... choice of movies to be played with (e.g., based on genres or time-frames)
"Movie Ninja"
- … a more gamified “Fruit Ninja” mode where wrong facts need to be slashed
